PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?model ?modelLabel WHERE {
  ?contribution orkgp:HAS_BENCHMARK <http://orkg.org/orkg/resource/R131712> .
  ?contribution orkgp:HAS_MODEL ?model .
  ?model rdf:type orkgc:Model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel .
  }
}

The model that has been evaluated on the BUCC French-to-English dataset is Massively Multilingual Sentence Embeddings (http://orkg.org/orkg/resource/R124053). The query now also optionally returns the model label for improved readability.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Can you list the models that have been evaluated on the BUCC French-to-English dataset?

[Assistant]
Let's break down the question:

1. We are looking for "models" that have been "evaluated" on the "BUCC French-to-English dataset".
2. So, we need to identify:
   - The entity for the BUCC French-to-English dataset.
   - The property that links models to datasets they have been evaluated on (e.g., "evaluated on", "evaluation dataset", etc.).
   - The entity type for "model".

First, I will search for the BUCC French-to-English dataset in the ORKG knowledge graph.

[Tool]
Top 10 entity alternatives:
1. BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>): has type Dataset
2. Benchmark BUCC French-to-English (<http://orkg.org/orkg/resource/R131712>): has type Benchmark
3. BUCC@ACL/IJCNLP (<http://orkg.org/orkg/resource/R196451>): has type Venue
4. BUCC Russian-to-English (<http://orkg.org/orkg/resource/R127155>): has type Dataset
5. BUCC German-to-English (<http://orkg.org/orkg/resource/R127150>): has type Dataset
6. BUCC Chinese-to-English (<http://orkg.org/orkg/resource/R127154>): has type Dataset
7. Benchmark BUCC Russian-to-English (<http://orkg.org/orkg/resource/R131716>): has type Benchmark
8. Benchmark BUCC German-to-English (<http://orkg.org/orkg/resource/R131710>): has type Benchmark
9. Benchmark BUCC Chinese-to-English (<http://orkg.org/orkg/resource/R131714>): has type Benchmark
10. AUT Document Alignment Framework for BUCC Workshop Shared Task (<http://orkg.org/orkg/resource/R196450>): has type Paper

[Assistant]
I found two relevant entities:
- BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>) of type Dataset
- Benchmark BUCC French-to-English (<http://orkg.org/orkg/resource/R131712>) of type Benchmark

Since the question asks about the dataset, I will focus on the first entity: BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>).

Next, I need to find the property that links models to datasets they have been evaluated on. I will search for properties related to "evaluation" or "evaluated on".

[Tool]
Top 10 property alternatives:
1. Evaluated on (orkgp:P41838)
2. evaluates (orkgp:P37290)
3. evaluation (orkgp:P5091)
4. evaluation (orkgp:P34)
5. evaluation (orkgp:HAS_EVALUATION)
6. evaluate (orkgp:P20041)
7. evaluate on (orkgp:P20042)
8. Has evaluation (orkgp:HasEvaluation)
9. method evaluated (orkgp:P82000)
10. Means of evaluation (orkgp:P36082)

[Assistant]
The most relevant property appears to be "Evaluated on" (orkgp:P41838).

Now, I need to find models that have been evaluated on the BUCC French-to-English dataset. To do this, I will look for subjects (models) that have the property "Evaluated on" with the object being <http://orkg.org/orkg/resource/R127153>.

But first, let's confirm what entity type is used for "model" in ORKG, so we can filter results to only include models. I'll search for "model" as an entity.

[Tool]
Top 10 entity alternatives:
1. Model (<http://orkg.org/orkg/resource/R150425>): has type Contribution Template
2. Model (<http://orkg.org/orkg/resource/R44212>)
3. Model (<http://orkg.org/orkg/resource/R150408>): has type Contribution Template
4. Model (<http://orkg.org/orkg/resource/R50465>): has type Sentence, has type Model
5. Model (<http://orkg.org/orkg/resource/R50479>): has type Sentence, has type Model
6. Model (<http://orkg.org/orkg/resource/R69780>)
7. Model (<http://orkg.org/orkg/resource/R50384>): has type Sentence, has type Model
8. Model (<http://orkg.org/orkg/resource/R44256>)
9. Model (<http://orkg.org/orkg/resource/R50367>): has type Sentence, has type Model
10. Model (<http://orkg.org/orkg/resource/R50366>): has type Sentence, has type Model

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there are no direct triples using the "Evaluated on" property (orkgp:P41838) with the BUCC French-to-English dataset as the object. This could mean:
- The property used might be different (e.g., "evaluate on", "evaluation", etc.).
- The dataset might be referenced via a benchmark entity instead.
- The linkage might be indirect (e.g., via an evaluation or benchmark node).

Next, I will try the property "evaluate on" (orkgp:P20042), which is also a plausible candidate.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no results when using "evaluate on" (orkgp:P20042) as the property.

Since there is also a "Benchmark BUCC French-to-English" entity, it's possible that models are linked to this benchmark rather than directly to the dataset. I will now check if any models are evaluated on the benchmark entity (<http://orkg.org/orkg/resource/R131712>) using the "Evaluated on" property.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no results when using the benchmark entity as the object. This suggests that either a different property is used, or the linkage is more complex (e.g., via an intermediate evaluation or contribution node).

Next, I will check if there are any triples where the BUCC French-to-English dataset (<http://orkg.org/orkg/resource/R127153>) is used as a subject, to see how it is referenced in the graph.

[Tool]
Got 3 rows and 3 columns:
| subject                                                          | property   | object                              |
| ---------------------------------------------------------------- | ---------- | ----------------------------------- |
| BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>) | rdf:type   | Dataset (orkgc:Dataset)             |
| BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>) | rdfs:label | BUCC French-to-English (xsd:string) |
| BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>) | rdf:type   | orkgc:Resource                      |

[Assistant]
The BUCC French-to-English dataset entity does not have any outgoing links indicating which models have been evaluated on it.

Since direct links are not found, I will try the reverse: look for triples where the dataset is the object, regardless of property, to see if any model or evaluation entity points to it.

[Tool]
Got 1 row and 3 columns:
| subject                                                                    | property                        | object                                                           |
| -------------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------- |
| Benchmark BUCC French-to-English (<http://orkg.org/orkg/resource/R131712>) | has dataset (orkgp:HAS_DATASET) | BUCC French-to-English (<http://orkg.org/orkg/resource/R127153>) |

[Assistant]
The only incoming link to the BUCC French-to-English dataset is from the "Benchmark BUCC French-to-English" entity, using the property "has dataset" (orkgp:HAS_DATASET).

This suggests that models may be linked to the benchmark entity rather than directly to the dataset. To proceed, I will check for any subjects that use the "Evaluated on" property (orkgp:P41838) with the benchmark entity as the object, to see if any models are evaluated on this benchmark.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no results for models evaluated on the benchmark entity using the "Evaluated on" property.

Given that, I will now try to find any entities (possibly models) that are linked to the benchmark entity using any property, to see if another property is used for this relationship.

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                            | object                                                                     |
| -------------------------------------------------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------- |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark BUCC French-to-English (<http://orkg.org/orkg/resource/R131712>) |

[Tool]
Got 26 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                                | object                                                                                |
| -------------------------------------------------------------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------------------------- |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | research problem (orkgp:P32)            | Cross-Lingual Document Classification (<http://orkg.org/orkg/resource/R124044>)         |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/LASER (xsd:string)                                  |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-Japanese (<http://orkg.org/orkg/resource/R131696>) |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has model (orkgp:HAS_MODEL)             | Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>)    |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | rdf:type                                | Contribution (orkgc:Contribution)                                                       |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | rdfs:label                              | Contribution 	Massively Multilingual Sentence Embeddings (xsd:string)                   |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/vizseq (xsd:string)                                 |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/imamathcat/LASER_Dependencies (xsd:string)                           |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/kmkwon94/ainize-laser (xsd:string)                                   |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/LawrenceDuan/myLASER (xsd:string)                                    |
| ...                                                                                | ...                                     | ...                                                                                |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                    | object                                                                               |
| -------------------------------------------------------------------------------------------------- | --------------------------- | ------------------------------------------------------------------------------------ |
| Contribution  Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R131695>) | has model (orkgp:HAS_MODEL) | Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>) |

[Tool]
Got 3 rows and 3 columns:
| subject                                                                              | property   | object                                                  |
| ------------------------------------------------------------------------------------ | ---------- | ------------------------------------------------------- |
| Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>) | rdf:type   | Model (orkgc:Model)                                     |
| Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>) | rdfs:label | Massively Multilingual Sentence Embeddings (xsd:string) |
| Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>) | rdf:type   | orkgc:Resource                                          |

[Tool]
Got 1 row and 1 column:
| model                                                                                |
| ------------------------------------------------------------------------------------ |
| Massively Multilingual Sentence Embeddings (<http://orkg.org/orkg/resource/R124053>) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves models evaluated on the BUCC French-to-English dataset by filtering for contributions with the specified benchmark and extracting associated models. However, the query only returns the model IRIs, not their labels. While labels are optional unless explicitly requested, including them in an OPTIONAL clause would improve the answer's readability and completeness, especially for users who may not recognize the IRIs. Please refine the query to optionally include model labels using rdfs:label or a similar property.

[Assistant]
Thank you for your feedback. You are correct: including the model labels in an OPTIONAL clause will make the results more readable and informative. I will refine the query to include the model labels using rdfs:label.

Here is my revised approach:
- Keep the structure of the original query, which finds models evaluated on the BUCC French-to-English benchmark.
- Add an OPTIONAL clause to retrieve the rdfs:label for each model.

Here is the refined SPARQL query:

```sparql
SELECT ?model ?modelLabel WHERE {
  ?contribution orkgp:HAS_BENCHMARK <http://orkg.org/orkg/resource/R131712> .
  ?contribution orkgp:HAS_MODEL ?model .
  ?model rdf:type orkgc:Model .
  OPTIONAL { ?model rdfs:label ?modelLabel . }
}
```

Now, I will provide this as the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the models evaluated on the BUCC French-to-English dataset by filtering contributions with the specified benchmark and extracting the associated models. It also optionally returns the model label for readability, as requested. The result matches the expected answer, and the query follows all guidelines, including returning the model IRIs. No further refinement is necessary.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?contribution ?model ?evaluation ?value WHERE {
  ?contribution orkgp:HAS_BENCHMARK <http://orkg.org/orkg/resource/R131769> .
  ?contribution orkgp:HAS_MODEL ?model .
  <http://orkg.org/orkg/resource/R131769> orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC <http://orkg.org/orkg/resource/R120314> .
  ?evaluation orkgp:HAS_VALUE ?value .
}
ORDER BY DESC ( xsd:float ( ?value ) )
LIMIT 1

The ORKG data for the AESLC benchmark does not currently link evaluations directly to contributions or models via the HAS_EVALUATION property. Evaluations are only linked to the benchmark entity itself. Therefore, it is not possible to robustly scope the evaluation to the specific model/contribution as requested. The best available query is the previous one, which assumes the evaluation is for the only contribution/model present for this benchmark. If the data model is updated to link evaluations directly to contributions or models, a more robust query can be constructed.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What is the best performing model benchmarking the AESLC dataset in terms of ROUGE-1 metric?

[Assistant]
Let's break down the question:

1. We are looking for models that have been benchmarked on the AESLC dataset.
2. We want to compare their performance in terms of the ROUGE-1 metric.
3. We want to find the model with the best (highest) ROUGE-1 score.

Step 1: Search for the AESLC dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. Dataset (<http://orkg.org/orkg/resource/R178304>): has type Contribution Template
2. Dataset (<http://orkg.org/orkg/resource/R216575>): has type Contribution, has type R216526
3. Dataset (<http://orkg.org/orkg/resource/R182198>): has type Contribution
4. Dataset (<http://orkg.org/orkg/resource/R70898>): has type Contribution Template
5. Dataset (<http://orkg.org/orkg/resource/R11002>): has type Contribution
6. Dataset (<http://orkg.org/orkg/resource/R11006>): has type Contribution
7. Dataset (<http://orkg.org/orkg/resource/R12082>): has type Contribution
8. Dataset (<http://orkg.org/orkg/resource/R11015>): has type Contribution
9. Dataset (<http://orkg.org/orkg/resource/R11012>): has type Contribution
10. Dataset (<http://orkg.org/orkg/resource/R12102>): has type Contribution

[Assistant]
The search for "AESLC dataset" returned several generic "Dataset" entities, but none are explicitly labeled as "AESLC". To ensure accuracy, I need to refine the search or look for entities related to "AESLC" or "Abstractive Email Summarization" (the likely full name).

Next, I will search for "AESLC" directly to see if there is a more specific match.

[Tool]
Top 10 entity alternatives:
1. AESLC (<http://orkg.org/orkg/resource/R124775>): has type Dataset
2. Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>): has type Benchmark

[Assistant]
Now I have found two relevant entities:
1. AESLC (<http://orkg.org/orkg/resource/R124775>) - type: Dataset
2. Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) - type: Benchmark

For benchmarking, the "Benchmark AESLC" entity is likely the one linking models and their performance on this dataset.

Next, I need to identify the property that links models to their performance metrics (specifically ROUGE-1) on this benchmark. I'll search for properties related to "ROUGE-1".

[Tool]
Top 10 property alternatives:
1. R8171 (orkgp:P7028)
2. AG 1 (orkgp:P37181)
3. R@1 (orkgp:P9013)
4. Category 1 (orkgp:P35007)
5. Custom1 (orkgp:P37137)
6. R60081 (orkgp:P33115)
7. R59861 (orkgp:P33089)
8. Epoxy resin (cm-1) (orkgp:P41109)
9. R8141 (orkgp:P7019)
10. R219771 (orkgp:P71109)

[Assistant]
The search for "ROUGE-1" returned several properties, but none are explicitly labeled as "ROUGE-1". The most likely candidate is the first one, "R8171 (orkgp:P7028)", as it is the top result and follows a naming pattern for metrics.

To confirm, I'll check how "orkgp:P7028" is used in the knowledge graph.

[Tool]
Got 2 rows and 3 columns:
| subject                                                | property            | object                |
| ------------------------------------------------------ | ------------------- | --------------------- |
| Observation #1 (<http://orkg.org/orkg/resource/R8166>) | R8171 (orkgp:P7028) | Baseline (xsd:string) |
| Observation #2 (<http://orkg.org/orkg/resource/R8170>) | R8171 (orkgp:P7028) | ORKG (xsd:string)     |

[Assistant]
It appears that orkgp:P7028 ("R8171") is not used for numeric metric values, but rather for string values like "Baseline" and "ORKG". This suggests it is not the ROUGE-1 metric property.

To be thorough, I'll search for properties related to "metric" to see if there is a more general property for metric values, or I can look for "score" or "ROUGE" as a broader term.

[Tool]
Top 10 property alternatives:
1. Polymer (orkgp:P15182)
2. Fat (orkgp:P52033)
3. transforms (orkgp:P20106)
4. Talc (orkgp:P43163)
5. CG (orkgp:P15465)
6. transform (orkgp:P20111)
7. Neutral (orkgp:P59008)
8. Type (orkgp:P23139)
9. type (orkgp:type)
10. type (orkgp:P4012)

[Assistant]
The search for "ROUGE" as a property did not return any relevant metric properties. Since the direct property search is not yielding results, I will try a different approach:

I'll look at the properties of the "Benchmark AESLC" entity to see how model performances are recorded, and if any property is related to ROUGE-1 or performance metrics.

[Tool]
Got 7 rows and 3 columns:
| subject                                                   | property                          | object                                                             |
| --------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------ |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | has dataset (orkgp:HAS_DATASET)   | AESLC (<http://orkg.org/orkg/resource/R124775>)                    |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                        |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | rdfs:label                        | Benchmark AESLC (xsd:string)                                       |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | rdf:type                          | orkgc:Resource                                                     |

[Assistant]
The "Benchmark AESLC" entity has several evaluations linked via the property orkgp:HAS_EVALUATION. One of them is "Evaluation ROUGE-1_37.68" (<http://orkg.org/orkg/resource/R131770>), which likely represents an evaluation of a model on the AESLC benchmark with a ROUGE-1 score of 37.68.

To find the best performing model, I need to:
1. Find all evaluations of models on the AESLC benchmark with ROUGE-1 scores.
2. Extract the model and the ROUGE-1 score from each evaluation.
3. Select the model with the highest ROUGE-1 score.

I'll now inspect the "Evaluation ROUGE-1_37.68" entity to see how the model and score are represented.

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                            |
| ------------------------------------------------------------------ | ------------------------- | ------------------------------------------------- |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | value (orkgp:HAS_VALUE)   | 37.68 (xsd:string)                                |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | metric (orkgp:HAS_METRIC) | ROUGE-1 (<http://orkg.org/orkg/resource/R120314>) |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdf:type                  | Evaluation (orkgc:Evaluation)                     |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdfs:label                | Evaluation ROUGE-1_37.68 (xsd:string)             |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdf:type                  | orkgc:Resource                                    |

[Assistant]
The "Evaluation ROUGE-1_37.68" entity has:
- orkgp:HAS_VALUE: 37.68 (the ROUGE-1 score)
- orkgp:HAS_METRIC: ROUGE-1 (<http://orkg.org/orkg/resource/R120314>)

However, there is no direct link to a model in this entity. It's possible that the model is linked from a higher-level entity or from the benchmark itself.

I'll check the other evaluations linked from the "Benchmark AESLC" entity to see if any of them link to a model, or if there is a pattern for associating models with evaluations.

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                            |
| ------------------------------------------------------------------ | ------------------------- | ------------------------------------------------- |
| Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) | value (orkgp:HAS_VALUE)   | 21.25 (xsd:string)                                |
| Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) | metric (orkgp:HAS_METRIC) | ROUGE-2 (<http://orkg.org/orkg/resource/R124684>) |
| Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) | rdf:type                  | Evaluation (orkgc:Evaluation)                     |
| Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) | rdfs:label                | Evaluation ROUGE-2_21.25 (xsd:string)             |
| Evaluation ROUGE-2_21.25 (<http://orkg.org/orkg/resource/R131771>) | rdf:type                  | orkgc:Resource                                    |

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                            |
| ------------------------------------------------------------------ | ------------------------- | ------------------------------------------------- |
| Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) | value (orkgp:HAS_VALUE)   | 36.51 (xsd:string)                                |
| Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) | metric (orkgp:HAS_METRIC) | ROUGE-L (<http://orkg.org/orkg/resource/R116360>) |
| Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) | rdf:type                  | Evaluation (orkgc:Evaluation)                     |
| Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) | rdfs:label                | Evaluation ROUGE-L_36.51 (xsd:string)             |
| Evaluation ROUGE-L_36.51 (<http://orkg.org/orkg/resource/R131772>) | rdf:type                  | orkgc:Resource                                    |

[Assistant]
The evaluations for ROUGE-2 and ROUGE-L are structured similarly, with no direct link to a model. This suggests that the evaluations themselves do not store the model information.

Given this, it's likely that the model is linked from the benchmark or from another entity that aggregates the evaluation results. I will check if there are any entities that link together the model, dataset, and evaluation.

I'll search for entities related to "model" and "AESLC" to see if there are any such aggregating entities.

[Tool]
Top 10 entity alternatives:
1. Model (<http://orkg.org/orkg/resource/R150425>): has type Contribution Template
2. Model (<http://orkg.org/orkg/resource/R44212>)
3. Model (<http://orkg.org/orkg/resource/R150408>): has type Contribution Template
4. Model (<http://orkg.org/orkg/resource/R50465>): has type Sentence, has type Model
5. Model (<http://orkg.org/orkg/resource/R50479>): has type Sentence, has type Model
6. Model (<http://orkg.org/orkg/resource/R69780>)
7. Model (<http://orkg.org/orkg/resource/R50384>): has type Sentence, has type Model
8. Model (<http://orkg.org/orkg/resource/R44256>)
9. Model (<http://orkg.org/orkg/resource/R50367>): has type Sentence, has type Model
10. Model (<http://orkg.org/orkg/resource/R50366>): has type Sentence, has type Model

[Assistant]
The search for "model AESLC" returned several generic "Model" entities, but none are explicitly linked to AESLC in their labels. Since the evaluations are linked from the benchmark, and the benchmark is linked to the dataset, it's possible that the model information is not explicitly modeled in this part of the knowledge graph.

Given the available data, the best I can do is to query for all evaluations linked to the "Benchmark AESLC" entity, filter for those with the ROUGE-1 metric, and return the evaluation(s) with the highest value. If the model information is not present, I will note that in the answer.

I'll now write a SPARQL query to:
- Get all evaluations linked to the AESLC benchmark via orkgp:HAS_EVALUATION.
- Filter for those where the metric is ROUGE-1 (<http://orkg.org/orkg/resource/R120314>).
- Get the value (score) for each.
- Return the evaluation(s) with the highest ROUGE-1 value.

If model information is present, I will include it; otherwise, I will return the evaluation and value.

[Tool]
Got 1 row and 2 columns:
| evaluation                                                         | value              |
| ------------------------------------------------------------------ | ------------------ |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | 37.68 (xsd:string) |

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                            |
| ------------------------------------------------------------------ | ------------------------- | ------------------------------------------------- |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | value (orkgp:HAS_VALUE)   | 37.68 (xsd:string)                                |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | metric (orkgp:HAS_METRIC) | ROUGE-1 (<http://orkg.org/orkg/resource/R120314>) |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdf:type                  | Evaluation (orkgc:Evaluation)                     |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdfs:label                | Evaluation ROUGE-1_37.68 (xsd:string)             |
| Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | rdf:type                  | orkgc:Resource                                    |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves the evaluation with the highest ROUGE-1 value for the AESLC dataset, but it does not return the model identifier or IRI, which is crucial for answering the user's question about the best performing model. The query should be refined to also return the model associated with the evaluation, if available, by traversing the appropriate property (e.g., HAS_MODEL or similar). Additionally, the value should ideally be cast to a numeric type for correct ordering, though this may depend on the data. Please refine the query to include the model IRI in the result.

[Assistant]
Thank you for the feedback. I agree: to fully answer the question, the query should return the model IRI associated with the best ROUGE-1 evaluation on the AESLC benchmark.

Step-by-step plan:
1. Identify the property that links an evaluation to a model (likely something like HAS_MODEL).
2. Refine the query to traverse from the evaluation to the model and include the model IRI in the result.
3. Ensure the ROUGE-1 value is cast to a numeric type for correct ordering.

First, I will search for properties related to "model" to identify the correct property for linking evaluations to models.

[Tool]
Top 10 property alternatives:
1. model (orkgp:P20072)
2. model (orkgp:P1004)
3. models (orkgp:P71209)
4. Models (orkgp:P43024)
5. model type (orkgp:P71176)
6. Proposed model (orkgp:P59015)
7. Mathematical model (orkgp:P41907)
8. has model (orkgp:hasModel)
9. Has model (orkgp:P9018)
10. has model (orkgp:HAS_MODEL)

[Tool]
Got 658 rows and 3 columns, showing the first 10 rows below:
| subject                                                                   | property                    | object                                                      |
| ------------------------------------------------------------------------- | --------------------------- | ----------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>)   | has model (orkgp:HAS_MODEL) | DDQN (tuned) hs (<http://orkg.org/orkg/resource/R124898>)   |
| Contribution  DQN hs (<http://orkg.org/orkg/resource/R132805>)            | has model (orkgp:HAS_MODEL) | DQN hs (<http://orkg.org/orkg/resource/R124911>)            |
| Contribution  Prior+Duel hs (<http://orkg.org/orkg/resource/R132906>)     | has model (orkgp:HAS_MODEL) | Prior+Duel hs (<http://orkg.org/orkg/resource/R124919>)     |
| Contribution  DQN noop (<http://orkg.org/orkg/resource/R132706>)          | has model (orkgp:HAS_MODEL) | DQN noop (<http://orkg.org/orkg/resource/R124908>)          |
| Contribution  Prior hs (<http://orkg.org/orkg/resource/R132500>)          | has model (orkgp:HAS_MODEL) | Prior hs (<http://orkg.org/orkg/resource/R124895>)          |
| Contribution  Prior noop (<http://orkg.org/orkg/resource/R132401>)        | has model (orkgp:HAS_MODEL) | Prior noop (<http://orkg.org/orkg/resource/R124894>)        |
| Contribution  A3C FF hs (<http://orkg.org/orkg/resource/R133504>)         | has model (orkgp:HAS_MODEL) | A3C FF hs (<http://orkg.org/orkg/resource/R124913>)         |
| Contribution  A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R133605>) | has model (orkgp:HAS_MODEL) | A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R124914>) |
| Contribution  A3C LSTM hs (<http://orkg.org/orkg/resource/R133706>)       | has model (orkgp:HAS_MODEL) | A3C LSTM hs (<http://orkg.org/orkg/resource/R124921>)       |
| Contribution  Duel noop (<http://orkg.org/orkg/resource/R131981>)         | has model (orkgp:HAS_MODEL) | Duel noop (<http://orkg.org/orkg/resource/R124891>)         |
| ...                                                                       | ...                         | ...                                                         |

[Assistant]
The property orkgp:HAS_MODEL (has model) is used to link contributions or evaluations to models.

Now, I need to check if the evaluation entities (like Evaluation ROUGE-1_37.68) are directly linked to a model via orkgp:HAS_MODEL, or if the link is indirect (e.g., via a contribution entity).

I'll check if the evaluation entity <http://orkg.org/orkg/resource/R131770> has a HAS_MODEL property.

[Tool] Got no rows and 3 columns

[Assistant]
The evaluation entity does not have a direct HAS_MODEL property. This suggests the model may be linked via another entity, such as a "Contribution" or "Result" entity, which connects the evaluation and the model.

To confirm, I will check if any entity links to the evaluation via a property, possibly representing a contribution or result. I'll search for triples where the evaluation is the object.

[Tool]
Got 1 row and 3 columns:
| subject                                                   | property                          | object                                                             |
| --------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------ |
| Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) |

[Assistant]
The only entity linking to the evaluation is the "Benchmark AESLC" entity, via the HAS_EVALUATION property. There is no direct or indirect link to a model from the evaluation entity in the available data.

Given this, I will broaden the search: I'll look for any entity that has both a HAS_MODEL property and is associated with the AESLC dataset and a ROUGE-1 evaluation. This may involve traversing through contributions or results.

I'll search for entities with HAS_MODEL and see if any are also associated with the AESLC benchmark or dataset.

[Tool]
Got 658 rows and 3 columns, showing the first 10 rows below:
| subject                                                                   | property                    | object                                                      |
| ------------------------------------------------------------------------- | --------------------------- | ----------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>)   | has model (orkgp:HAS_MODEL) | DDQN (tuned) hs (<http://orkg.org/orkg/resource/R124898>)   |
| Contribution  DQN hs (<http://orkg.org/orkg/resource/R132805>)            | has model (orkgp:HAS_MODEL) | DQN hs (<http://orkg.org/orkg/resource/R124911>)            |
| Contribution  Prior+Duel hs (<http://orkg.org/orkg/resource/R132906>)     | has model (orkgp:HAS_MODEL) | Prior+Duel hs (<http://orkg.org/orkg/resource/R124919>)     |
| Contribution  DQN noop (<http://orkg.org/orkg/resource/R132706>)          | has model (orkgp:HAS_MODEL) | DQN noop (<http://orkg.org/orkg/resource/R124908>)          |
| Contribution  Prior hs (<http://orkg.org/orkg/resource/R132500>)          | has model (orkgp:HAS_MODEL) | Prior hs (<http://orkg.org/orkg/resource/R124895>)          |
| Contribution  Prior noop (<http://orkg.org/orkg/resource/R132401>)        | has model (orkgp:HAS_MODEL) | Prior noop (<http://orkg.org/orkg/resource/R124894>)        |
| Contribution  A3C FF hs (<http://orkg.org/orkg/resource/R133504>)         | has model (orkgp:HAS_MODEL) | A3C FF hs (<http://orkg.org/orkg/resource/R124913>)         |
| Contribution  A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R133605>) | has model (orkgp:HAS_MODEL) | A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R124914>) |
| Contribution  A3C LSTM hs (<http://orkg.org/orkg/resource/R133706>)       | has model (orkgp:HAS_MODEL) | A3C LSTM hs (<http://orkg.org/orkg/resource/R124921>)       |
| Contribution  Duel noop (<http://orkg.org/orkg/resource/R131981>)         | has model (orkgp:HAS_MODEL) | Duel noop (<http://orkg.org/orkg/resource/R124891>)         |
| ...                                                                       | ...                         | ...                                                         |

[Assistant]
Entities with HAS_MODEL are labeled as "Contribution ..." and link to specific models. To find the best performing model, I need to check if these "Contribution" entities are also linked to the AESLC dataset and have a ROUGE-1 evaluation.

I'll check the properties of a sample "Contribution" entity to see if it links to both a dataset and an evaluation.

[Tool]
Got 128 rows and 3 columns, showing the first 10 rows below:
| subject                                                                 | property                                | object                                                                       |
| ----------------------------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | research problem (orkgp:P32)            | Atari Games (<http://orkg.org/orkg/resource/R124884>)                        |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/1jsingh/rl_navigation (xsd:string)                        |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132606>)      |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has model (orkgp:HAS_MODEL)             | DDQN (tuned) hs (<http://orkg.org/orkg/resource/R124898>)                    |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | rdf:type                                | Contribution (orkgc:Contribution)                                            |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | rdfs:label                              | Contribution 	DDQN (tuned) hs (xsd:string)                                   |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/Adrelf/DRL-navigation (xsd:string)                        |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks (xsd:string) |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/atavakol/action-branching-agents (xsd:string)             |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/austinsilveria/Banana-Collection-DQN (xsd:string)         |
| ...                                                                     | ...                                     | ...                                                                          |

[Assistant]
The "Contribution" entity links to a benchmark (via HAS_BENCHMARK), a model (via HAS_MODEL), and other properties. If this pattern holds for AESLC, I should look for "Contribution" entities that:
- Have a HAS_BENCHMARK property pointing to the AESLC benchmark (<http://orkg.org/orkg/resource/R131769>).
- Have a HAS_MODEL property.
- Have an evaluation for ROUGE-1.

I'll search for contributions with HAS_BENCHMARK = AESLC.

[Tool]
Got 1 row and 3 columns:
| subject                                                         | property                            | object                                                    |
| --------------------------------------------------------------- | ----------------------------------- | --------------------------------------------------------- |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark AESLC (<http://orkg.org/orkg/resource/R131769>) |

[Tool]
Got 18 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property                                | object                                                           |
| --------------------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | research problem (orkgp:P32)            | Text Summarization (<http://orkg.org/orkg/resource/R124682>)     |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/amiyamandal-dev/pegasus (xsd:string)          |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark GigaWord (<http://orkg.org/orkg/resource/R131765>)     |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has model (orkgp:HAS_MODEL)             | PEGASUS (<http://orkg.org/orkg/resource/R124688>)                |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | rdf:type                                | Contribution (orkgc:Contribution)                                |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | rdfs:label                              | Contribution 	PEGASUS (xsd:string)                               |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/google-research/pegasus (xsd:string)          |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/huggingface/transformers (xsd:string)         |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/ibrahim-elsawy/test (xsd:string)              |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/jiacheng-xu/text-sum-uncertainty (xsd:string) |
| ...                                                             | ...                                     | ...                                                              |

[Tool] Got no rows and 3 columns

[Assistant]
The "Contribution PEGASUS" entity is linked to the AESLC benchmark and a model, but does not directly link to an evaluation. However, since the benchmark entity links to the evaluation, and the contribution links to the benchmark, it is likely that the evaluation is implicitly associated with the model via the contribution.

Given this, I can write a query that:
- Finds contributions with HAS_BENCHMARK = AESLC.
- Gets the associated model via HAS_MODEL.
- Gets the ROUGE-1 evaluation for the AESLC benchmark.
- Returns the model, evaluation, and ROUGE-1 value, sorted by value.

I'll write a SPARQL query that joins these elements.

[Tool]
Got 1 row and 4 columns:
| contribution                                                    | model                                             | evaluation                                                         | value              |
| --------------------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------------------------ | ------------------ |
| Contribution  PEGASUS (<http://orkg.org/orkg/resource/R131760>) | PEGASUS (<http://orkg.org/orkg/resource/R124688>) | Evaluation ROUGE-1_37.68 (<http://orkg.org/orkg/resource/R131770>) | 37.68 (xsd:string) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and returns the best performing model on the AESLC dataset in terms of the ROUGE-1 metric, as requested. However, there is a potential issue: the query assumes that all evaluations for the benchmark are directly attached to the benchmark resource, rather than to the specific contribution/model. This could lead to incorrect associations if multiple models/contributions are benchmarked on AESLC, each with their own evaluation.

A more robust query would ensure that the evaluation is linked to the specific contribution/model, not just the benchmark. For example, by requiring that the evaluation is associated with the contribution (e.g., ?contribution or ?model <HAS_EVALUATION> ?evaluation) rather than the benchmark. Please refine the query to ensure the evaluation is correctly scoped to the model/contribution.

[Assistant]
Thank you for the detailed feedback. You are correct: to ensure accuracy, the query should only consider evaluations directly linked to the specific contribution (or model), not just to the benchmark. This avoids incorrect associations when multiple models are benchmarked.

Step-by-step plan:
1. Find contributions linked to the AESLC benchmark.
2. For each contribution, get its associated model.
3. For each contribution (or model), get its directly linked evaluation(s) via HAS_EVALUATION.
4. Filter for evaluations with the ROUGE-1 metric.
5. Return the model, contribution, evaluation, and value, sorted by the highest ROUGE-1 value.

First, I will check if the property HAS_EVALUATION is used to link contributions or models to evaluations.

[Tool]
Got 3,717 rows and 3 columns, showing the first 10 rows below:
| subject                                                               | property                          | object                                                                                |
| --------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------- |
| 1-SOTAB_description (<http://orkg.org/orkg/resource/R282239>)         | evaluation (orkgp:HAS_EVALUATION) | Using DoDuo (<http://orkg.org/orkg/resource/R286796>)                                                         |
| MammoTab (<http://orkg.org/orkg/resource/R282243>)                    | evaluation (orkgp:HAS_EVALUATION) | Evaluation using the MTab system...versions of the SemTab challenge (<http://orkg.org/orkg/resource/R287505>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R136069>)              | evaluation (orkgp:HAS_EVALUATION) | Gold standard (<http://orkg.org/orkg/resource/R136078>)                                                       |
| Wikinary (<http://orkg.org/orkg/resource/R282241>)                    | evaluation (orkgp:HAS_EVALUATION) | Human evaluation (<http://orkg.org/orkg/resource/R273019>)                                                    |
| Genome Evolutionary Analysis (<http://orkg.org/orkg/resource/R75376>) | evaluation (orkgp:HAS_EVALUATION) | country of origin (<http://orkg.org/orkg/resource/R33266>)                                                    |
| FedX (<http://orkg.org/orkg/resource/R114017>)                        | evaluation (orkgp:HAS_EVALUATION) | FedBench8 (<http://orkg.org/orkg/resource/R114025>)                                                           |
| Contribution 1 (<http://orkg.org/orkg/resource/R136100>)              | evaluation (orkgp:HAS_EVALUATION) | true (xsd:string)                                                                                |
| SPARQL query optimization (<http://orkg.org/orkg/resource/R75313>)    | evaluation (orkgp:HAS_EVALUATION) | Online running time (xsd:string)                                                                              |
| Contribution 1 (<http://orkg.org/orkg/resource/R182425>)              | evaluation (orkgp:HAS_EVALUATION) | Link Prediction (xsd:string)                                                                                |
| (Main) Contribution (<http://orkg.org/orkg/resource/R76790>)          | evaluation (orkgp:HAS_EVALUATION) | Query performance (<http://orkg.org/orkg/resource/R113263>)                                                   |
| ...                                                                   | ...                               | ...                                                                                |

[Tool] Got no rows and 3 columns

[Assistant]
The "Contribution PEGASUS" entity does not have a direct HAS_EVALUATION property. This suggests that, at least for this example, evaluations are not directly linked to the contribution.

To be thorough, I will check if the model entity itself has a HAS_EVALUATION property.

[Tool] Got no rows and 3 columns

[Assistant]
Neither the contribution nor the model has a direct HAS_EVALUATION property. This suggests that, in this data, evaluations are only linked to the benchmark, not to the contribution or model.

Given this, the current structure does not allow for a more precise scoping of evaluations to contributions/models for the AESLC benchmark. However, for completeness, I will write a query that would be robust if such links existed, by looking for evaluations attached to contributions (the most likely place for such links in a more detailed dataset).

If no results are found, I will explain that the current data model does not support this more precise association for AESLC, and provide the best available answer.

Here is the robust query attempt:

[Tool] Got no rows and 4 columns

[Tool]
Got 1 row and 3 columns:
| model                                                     | evaluation                                                         | value              |
| --------------------------------------------------------- | ------------------------------------------------------------------ | ------------------ |
| Benchmark X-Sum (<http://orkg.org/orkg/resource/R131778>) | Evaluation ROUGE-1_47.21 (<http://orkg.org/orkg/resource/R131779>) | 47.21 (xsd:string) |

[Tool] Stopped generation process


PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?problem ?problemLabel WHERE {
  ?paper a orkgc:Paper ; orkgp:P30 <http://orkg.org/orkg/resource/R133> ; orkgp:P31 ?contribution .
  ?contribution orkgp:P32 ?problem .
  OPTIONAL {
    ?problem rdfs:label ?problemLabel
  }
}

Got 161 rows and 2 columns, showing the first 5 and last 5 rows below:
| problem                                                                         | problemLabel                                         |
| ------------------------------------------------------------------------------- | ---------------------------------------------------- |
| text extraction from images (<http://orkg.org/orkg/resource/R10011>)            | text extraction from images  (xsd:string)            |
| scene text detection (<http://orkg.org/orkg/resource/R10012>)                   | scene text detection (xsd:string)                    |
| Knowledge Graph Creation (<http://orkg.org/orkg/resource/R10021>)               | Knowledge Graph Creation (xsd:string)                |
| Scholarly communications representation (<http://orkg.org/orkg/resource/R1033>) | Scholarly communications representation (xsd:string) |
| Knowledge Graph Embeddings (<http://orkg.org/orkg/resource/R11025>)             | Knowledge Graph Embeddings (xsd:string)              |
| ...                                                                             | ...                                                  |
| Crowd simulation and evacuation (<http://orkg.org/orkg/resource/R8224>)         | Crowd simulation and evacuation (xsd:string)         |
| finding optimal evacuation strategy (<http://orkg.org/orkg/resource/R8225>)     | finding optimal evacuation strategy (xsd:string)     |
| Question Answering (<http://orkg.org/orkg/resource/R9143>)                      | Question Answering  (xsd:string)                     |
| Semantic Question Answering (<http://orkg.org/orkg/resource/R9145>)             | Semantic Question Answering (xsd:string)             |
| Scholarly Figure Text Extraction (<http://orkg.org/orkg/resource/R9570>)        | Scholarly Figure Text Extraction (xsd:string)        |

This query lists benchmarked problems in the area of Artificial Intelligence by retrieving the research problems associated with contributions in AI papers. The results include both the IRIs and labels of these problems, providing a more precise and human-readable list of benchmarked problems relevant to the field.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Can you list benchmarked problems in the area of Artificial Intelligence?

[Assistant]
Let's break down the question:

1. We are looking for "benchmarked problems"—these are likely entities representing problems that have been used as benchmarks in research.
2. The area of interest is "Artificial Intelligence".

Step 1: Search for the entity "Artificial Intelligence" in the ORKG to get its identifier.

Step 2: Search for properties or classes related to "benchmarked problems" in the context of AI.

Step 3: Build a query to list problems (entities) that are benchmarked and associated with Artificial Intelligence.

Let's start by searching for "Artificial Intelligence" in the ORKG.

[Tool]
Top 10 entity alternatives:
1. Artificial Intelligence (<http://orkg.org/orkg/resource/R133>): has type ResearchField
2. artificial intelligence (<http://orkg.org/orkg/resource/wikidata:Q11660>): has type External
3. artificial intelligence (<http://orkg.org/orkg/resource/R214177>): has type Method
4. Artificial intelligence (<http://orkg.org/orkg/resource/R260031>): has type Contribution
5. Artificial Intelligence (<http://orkg.org/orkg/resource/R70405>): has type Venue
6. Artificial Intelligence (<http://orkg.org/orkg/resource/R204281>): has type Venue
7. Artificial Intelligence (<http://orkg.org/orkg/resource/R193204>): has type Venue
8. Artificial Intelligence (<http://orkg.org/orkg/resource/R163129>): has type Venue
9. Artificial Intelligence (<http://orkg.org/orkg/resource/R209843>)
10. Artificial Intelligence Review (<http://orkg.org/orkg/resource/R255117>): has type Venue

[Assistant]
The most relevant entity for the research field "Artificial Intelligence" is:
Artificial Intelligence (<http://orkg.org/orkg/resource/R133>), type: ResearchField.

Next, I need to identify how "benchmarked problems" are represented in the ORKG. These could be entities of a certain class (e.g., Problem, Task, Benchmark) or linked via a property (e.g., "has benchmark", "benchmarked problem", "evaluated on", etc.).

I'll search for properties related to "benchmark" to see how benchmarked problems might be linked.

[Tool]
Top 10 property alternatives:
1. Benchmark Performance (orkgp:P59252)
2. Benchmark Time (orkgp:P59253)
3. uses benchmark (orkgp:P38001)
4. Has benchmark (orkgp:P34103)
5. has benchmark (orkgp:hasBenchmark)
6. has benchmark (orkgp:HAS_BENCHMARK)
7. Performance metric (orkgp:P5003)
8. Performance measurment (orkgp:P58068)
9. Performance metrics (orkgp:P49059)
10. performance (orkgp:P5097)

[Assistant]
There are several relevant properties, but the most promising for linking problems to benchmarks are:

- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- has benchmark (orkgp:HAS_BENCHMARK)

However, these may link methods or systems to benchmarks, not necessarily problems. To clarify, I'll search for entities related to "problem" and see if there is a class or property for "benchmarked problem" or "problem" in general.

[Tool]
Top 10 entity alternatives:
1. Problem (<http://orkg.org/orkg/resource/R48000>): has type Contribution Template
2. Problem (<http://orkg.org/orkg/resource/R209732>)
3. problem (orkgp:P11): A situation, person, or thing that needs attention and needs to be dealt with or solved.
4. Problem (orkgc:Problem)
5. Problem (<http://orkg.org/orkg/resource/R201410>)
6. Problem (<http://orkg.org/orkg/resource/R148124>)
7. Problem report (<http://orkg.org/orkg/resource/R107986>): has type R76809
8. Problem report (<http://orkg.org/orkg/resource/R107987>): has type R76809
9. Problem report (<http://orkg.org/orkg/resource/R107988>): has type R76809
10. Problem report (<http://orkg.org/orkg/resource/R107910>): has type R76809

[Assistant]
There is a class "Problem" (orkgc:Problem) which likely represents problems in the ORKG.

To find benchmarked problems in Artificial Intelligence, I should look for entities of type orkgc:Problem that are associated with the research field "Artificial Intelligence" (R133).

Now, I need to determine how problems are linked to research fields. This is often done via a property like "has research field", "field", or similar. I'll search for properties related to "research field".

[Tool]
Top 10 property alternatives:
1. research field (orkgp:P30)
2. on research field (orkgp:P34100)
3. research_field_investigated (orkgp:P47004)
4. Research Area (orkgp:P37333)
5. Template of research field (orkgp:TemplateOfResearchField)
6. field of work (<http://orkg.org/orkg/predicate/wikidata:P101>)
7. paper:fieldsOfStudy (orkgp:P97071)
8. research type (orkgp:P62118)
9. Area of study (orkgp:P37364)
10. has research domain (orkgp:P41310)

[Assistant]
The most relevant properties for linking a problem to a research field are:

- research field (orkgp:P30)
- on research field (orkgp:P34100)
- research_field_investigated (orkgp:P47004)

I will use orkgp:P30 ("research field") as it is the most direct.

Now, to summarize, I will construct a SPARQL query to:

- Select entities of type orkgc:Problem
- That have the property orkgp:P30 (research field) with value <http://orkg.org/orkg/resource/R133> (Artificial Intelligence)
- Optionally, retrieve their labels

Let's build and execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query returned no results, which suggests that either:

- Problems are not directly typed as orkgc:Problem and linked to Artificial Intelligence via orkgp:P30, or
- The linkage between problems and research fields is modeled differently in the ORKG.

To investigate further, I'll check how entities of type orkgc:Problem are actually used in the knowledge graph. I'll list some triples where the subject is of type orkgc:Problem to see what properties are used with them.

[Tool]
Got 5,065 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property | object                  |
| ------------------------------------------------------------------------------------------------------------- | -------- | ----------------------- |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>)                                             | rdf:type | Problem (orkgc:Problem) |
| disease (<http://orkg.org/orkg/resource/R78081>)                                                              | rdf:type | Problem (orkgc:Problem) |
| Impact of open access (<http://orkg.org/orkg/resource/R187722>)                                               | rdf:type | Problem (orkgc:Problem) |
| Impact of open access on knowledge transfer (<http://orkg.org/orkg/resource/R187743>)                         | rdf:type | Problem (orkgc:Problem) |
| Machine Learning Model (<http://orkg.org/orkg/resource/R151047>)                                              | rdf:type | Problem (orkgc:Problem) |
| Impact of open access on inequality in the science system (<http://orkg.org/orkg/resource/R187775>)           | rdf:type | Problem (orkgc:Problem) |
| Impact of open access on the quality of scientific publications (<http://orkg.org/orkg/resource/R187721>)     | rdf:type | Problem (orkgc:Problem) |
| Answer Type prediction (<http://orkg.org/orkg/resource/R217636>)                                              | rdf:type | Problem (orkgc:Problem) |
| How frequently are refactoring o...lly versus with automated tools? (<http://orkg.org/orkg/resource/R108354>) | rdf:type | Problem (orkgc:Problem) |
| Knowledge Base Question Answering (<http://orkg.org/orkg/resource/R119815>)                                   | rdf:type | Problem (orkgc:Problem) |
| ...                                                                                | ...      | ...                     |

[Assistant]
There are many entities of type orkgc:Problem, such as "Named Entity Recognition" and "Knowledge Base Question Answering".

Next, I need to see how these problems are linked to Artificial Intelligence. Since my previous attempt with orkgp:P30 (research field) yielded no results, I'll check what properties are used with one of these problem entities, for example, "Named Entity Recognition" (<http://orkg.org/orkg/resource/R69806>).

[Tool]
Got 23 rows and 3 columns, showing the first 10 rows below:
| subject                                                           | property                       | object                                                                           |
| ----------------------------------------------------------------- | ------------------------------ | -------------------------------------------------------------------------------- |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | same as (orkgp:SAME_AS)        | https://en.wikipedia.org/wiki/Named-entity_recognition (xsd:string)              |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Low Resource Named Entity Recognition (<http://orkg.org/orkg/resource/R121394>)  |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | rdf:type                       | Problem (orkgc:Problem)                                                          |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | rdfs:label                     | Named Entity Recognition (xsd:string)                                            |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | same as (orkgp:SAME_AS)        | Named Entity Recognition and Classification (xsd:string)                         |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Chinese Named Entity Recognition (<http://orkg.org/orkg/resource/R122976>)       |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Nested Named Entity Recognition (<http://orkg.org/orkg/resource/R122993>)        |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Medical Named Entity Recognition (<http://orkg.org/orkg/resource/R122996>)       |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Cross-Domain Named Entity Recognition (<http://orkg.org/orkg/resource/R123004>)  |
| Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>) | Sub Problem (orkgp:subProblem) | Named Entity Recognition In Vietnamese (<http://orkg.org/orkg/resource/R123006>) |
| ...                                                               | ...                            | ...                                                                              |

[Assistant]
The properties used with "Named Entity Recognition" do not directly indicate a research field or area. However, since this is a well-known AI problem, perhaps the link to Artificial Intelligence is indirect or via another property.

Given that, I'll check if any of these problems are linked to "Artificial Intelligence" (<http://orkg.org/orkg/resource/R133>) via any property. I'll search for triples where the object is <http://orkg.org/orkg/resource/R133>.

[Tool]
Got 461 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                                                   | object                                                         |
| ------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | -------------------------------------------------------------- |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>)  | research field (orkgp:P30)                                 | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| artificial intelligence (<http://orkg.org/orkg/resource/wikidata:Q11660>)                                     | same as (orkgp:SAME_AS)                                    | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Knowledge-Based eXplainable systems (<http://orkg.org/orkg/resource/R69680>)                                  | has subject (orkgp:hasSubject)                             | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Computer Sciences (<http://orkg.org/orkg/resource/R132>)                                                      | has subfield (orkgp:P36)                                   | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Overall Dataset (<http://orkg.org/orkg/resource/R163749>)                                                     | Data domains (orkgp:P41878)                                | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Ontology learning from data sources (<http://orkg.org/orkg/resource/R138077>)                                 | Template of research field (orkgp:TemplateOfResearchField) | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R49005>)                                                       | Knowledge graph creation (orkgp:P16016)                    | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Overall Dataset (<http://orkg.org/orkg/resource/R163749>)                                                     | Target domains (orkgp:P44184)                              | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| SIGMORPHON 2020 Shared Task 0: T...Diverse Morphological Inflection (<http://orkg.org/orkg/resource/R140882>) | research field (orkgp:P30)                                 | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| Multi-Task Learning for Calorie ...hed with Nutritional Information (<http://orkg.org/orkg/resource/R182107>) | research field (orkgp:P30)                                 | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>) |
| ...                                                                                | ...                                                        | ...                                                            |

[Assistant]
There are entities linked to "Artificial Intelligence" via the property "research field" (orkgp:P30). Some of these entities, such as "SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment" and "SIGMORPHON 2020 Shared Task 0: T...Diverse Morphological Inflection", appear to be benchmark tasks or problems.

To confirm, I'll check the type of one such entity, for example, "SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment" (<http://orkg.org/orkg/resource/R76441>).

[Tool]
Got 56 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                                |
| ------------------------------------------------------------------------------------------------------------ | ----------------------------- | ------------------------------------------------------------------------------------------------------ |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | author (orkgp:P27)            | Anna Korhonen (xsd:string)                                                                             |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | contribution (orkgp:P31)      | Task (<http://orkg.org/orkg/resource/R76443>)                                                          |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | research field (orkgp:P30)    | Artificial Intelligence (<http://orkg.org/orkg/resource/R133>)                                         |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | publication year (orkgp:P29)  | 2020 (xsd:string)                                                                                |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | doi (orkgp:P26)               |  (xsd:string)                                                                                |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | venue (orkgp:HAS_VENUE)       | Proceedings of the Fourteenth Workshop on Semantic Evaluation (<http://orkg.org/orkg/resource/R76442>) |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | publication month (orkgp:P28) | 12 (xsd:string)                                                                                |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | url (orkgp:url)               |  (xsd:string)                                                                                |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | rdf:type                      | Paper (orkgc:Paper)                                                                                |
| SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (<http://orkg.org/orkg/resource/R76441>) | rdfs:label                    | SemEval-2020 Task 2: Predicting ...gual (Graded) Lexical Entailment (xsd:string)                       |
| ...                                                                                | ...                           | ...                                                                                |

[Assistant]
This entity is of type orkgc:Paper and has a "contribution" property linking it to a "Task" entity (<http://orkg.org/orkg/resource/R76443>). This suggests that benchmarked problems may be modeled as "Task" entities, which are then linked to papers or contributions.

Therefore, to list benchmarked problems in Artificial Intelligence, I should:

- Find entities of type "Task" (possibly orkgc:Task) or "Problem" (orkgc:Problem)
- That are associated with papers or contributions that have the research field "Artificial Intelligence" (orkgp:P30 <http://orkg.org/orkg/resource/R133>)

I'll check if "Task" is a class in the ORKG.

[Tool]
Top 10 entity alternatives:
1. Task (<http://orkg.org/orkg/resource/R76402>): aim to identify the change in meaning of words over time using corpus data, has type Contribution
2. Task (<http://orkg.org/orkg/resource/R76058>): to evaluate whether a system can distinguish a natural language ... sense to humans from one that does not, and provide the reasons, has type Contribution
3. Task (<http://orkg.org/orkg/resource/R76443>): to catalyze the development of models for predicting LE, has type Contribution
4. Task (<http://orkg.org/orkg/resource/R75787>): Counterfactual recognition in natural language, Modelling Causal Reasoning in Language, has type Contribution
5. Task (<http://orkg.org/orkg/resource/R111691>): has type Contribution
6. Task (<http://orkg.org/orkg/resource/R76159>): to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish, has type Contribution
7. Task (<http://orkg.org/orkg/resource/R76340>): a SemEval shared task in which participants must extract definit... reflects the complex reality of definitions in natural language, has type Contribution
8. Task (<http://orkg.org/orkg/resource/R76415>): treat each pair of corpora C1 and C2 as different languages L1 a... compute cosine similarity to classify and rank the target words, has type Contribution
9. Task (<http://orkg.org/orkg/resource/R76418>): present a model based on context-free word representations, a mo...nal representations for all words from the finetuned BERT model., has type Contribution
10. Task (<http://orkg.org/orkg/resource/R140594>): Disambiguate the senses of words in a sentence given an annotate...ic description of the specific meaning\nthe word in the sentence, has type Contribution

[Assistant]
It appears that "Task" entities are of type "Contribution" rather than a specific class like orkgc:Task. These tasks are likely benchmarked problems described in papers or contributions.

Given this, the best approach is to:

- Find papers (orkgc:Paper) with research field "Artificial Intelligence" (orkgp:P30 <http://orkg.org/orkg/resource/R133>)
- Retrieve their "contribution" (orkgp:P31), which are "Task" entities (with "Task" in their label)
- List these tasks as benchmarked problems

I'll construct a SPARQL query to do this.

[Tool]
Got 128 rows and 2 columns, showing the first 5 and last 5 rows below:
| task                                                            | taskLabel                          |
| --------------------------------------------------------------- | ---------------------------------- |
| Task (<http://orkg.org/orkg/resource/R111691>)                  | Task (xsd:string)                  |
| Subtask A - Questions (<http://orkg.org/orkg/resource/R111702>) | Subtask A - Questions (xsd:string) |
| Subtask B - Answers (<http://orkg.org/orkg/resource/R111703>)   | Subtask B - Answers (xsd:string)   |
| Task A - Questions (<http://orkg.org/orkg/resource/R111781>)    | Task A - Questions (xsd:string)    |
| Task A - Questions (<http://orkg.org/orkg/resource/R111784>)    | Task A - Questions (xsd:string)    |
| ...                                                             | ...                                |
| Subtask 2 - LA (<http://orkg.org/orkg/resource/R77002>)         | Subtask 2 - LA (xsd:string)        |
| Subtask 2 - SV (<http://orkg.org/orkg/resource/R77003>)         | Subtask 2 - SV (xsd:string)        |
| Subtask 2 - DE (<http://orkg.org/orkg/resource/R77004>)         | Subtask 2 - DE (xsd:string)        |
| Subtask 2 - LA (<http://orkg.org/orkg/resource/R77005>)         | Subtask 2 - LA (xsd:string)        |
| Subtask 2 - SW (<http://orkg.org/orkg/resource/R77006>)         | Subtask 2 - SW (xsd:string)        |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and returns relevant benchmarked problems (tasks) in the area of Artificial Intelligence. However, it only returns the IRIs of the tasks and not their labels, which would be helpful for human readability. Since the question asks for a list, including both the IRIs and their labels would be more informative. To improve the query, add ?taskLabel to the SELECT clause and ensure it is not filtered out by the DISTINCT. Also, the FILTER on 'task' in the label may exclude relevant benchmarked problems that do not have 'task' in their label. Consider removing or relaxing this filter to capture a broader range of benchmarked problems.

[Assistant]
Thank you for the feedback. Here’s my plan to refine the query:

1. I will include both the IRIs (?task) and their labels (?taskLabel) in the SELECT clause, so the results are more human-readable.
2. I will remove the FILTER that restricts results to only those with "task" in their label, to capture a broader range of benchmarked problems.
3. I will keep the DISTINCT to avoid duplicates.

Here is the improved SPARQL query:

SELECT DISTINCT ?task ?taskLabel WHERE {
  ?paper a orkgc:Paper ;
         orkgp:P30 <http://orkg.org/orkg/resource/R133> ;
         orkgp:P31 ?task .
  OPTIONAL { ?task rdfs:label ?taskLabel }
}

This will return all benchmarked problems (tasks/contributions) associated with papers in the research field of Artificial Intelligence, along with their labels if available.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is generally correct in structure and returns IRIs as required, with labels as optional. However, the results include generic items such as 'Method' and 'Contribution 1', which are not benchmarked problems but rather generic contributions or methods. The query could be refined to filter out such generic or irrelevant items, possibly by excluding items whose labels match 'Method', 'Contribution', or similar generic terms, or by including only those that are explicitly benchmarked problems (if such a property or class exists in the ORKG schema). Additionally, if there is a more specific property or class for 'benchmarked problem' or 'task', it should be used to improve precision.

[Assistant]
Thank you for the detailed feedback. Here’s my plan to further refine the query:

1. Investigate if there is a more specific class or property for "benchmarked problem" or "task" in the ORKG schema, such as orkgc:Problem or a similar class.
2. If such a class exists, restrict the query to only include entities of that class.
3. If not, filter out generic items by excluding those whose labels contain terms like "Method", "Contribution", or other non-problematic terms.
4. Ensure that the query still returns both IRIs and labels.

First, I will check if the "contribution" entities (?task) are typed as orkgc:Problem or another relevant class. I will do this by searching for the types of a sample of these entities.

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                        | property                                     | object                                                                                |
| ---------------------------------------------- | -------------------------------------------- | ---------------------------------------------------------------------------------------------- |
| Task (<http://orkg.org/orkg/resource/R111691>) | research problem (orkgp:P32)                 | Fact Checking in Community Question Answering Forums (<http://orkg.org/orkg/resource/R111698>) |
| Task (<http://orkg.org/orkg/resource/R111691>) | Dataset name (orkgp:P34063)                  | factcheck-cqa (<http://orkg.org/orkg/resource/R111695>)                                        |
| Task (<http://orkg.org/orkg/resource/R111691>) | Description (orkgp:P7037)                    | Aims to check factual informations in question answering forums (xsd:string)                   |
| Task (<http://orkg.org/orkg/resource/R111691>) | Has evaluation metrics (orkgp:P35217)        | Accuracy (<http://orkg.org/orkg/resource/R111697>)                                             |
| Task (<http://orkg.org/orkg/resource/R111691>) | Total submissions (orkgp:P36024)             | 28 (xsd:string)                                                                                |
| Task (<http://orkg.org/orkg/resource/R111691>) | Online competition (orkgp:P35181)            | https://competitions.codalab.org/competitions/20022 (xsd:string)                               |
| Task (<http://orkg.org/orkg/resource/R111691>) | Data repositories (orkgp:P15671)             | alt.qcri.org/semeval2016/task3/d...nannotated-data-subtaskA.xml.zip (xsd:string)               |
| Task (<http://orkg.org/orkg/resource/R111691>) | Data annotation granularities (orkgp:P35221) | Natural Answers (<http://orkg.org/orkg/resource/R111687>)                                      |
| Task (<http://orkg.org/orkg/resource/R111691>) | rdf:type                                     | Contribution (orkgc:Contribution)                                                              |
| Task (<http://orkg.org/orkg/resource/R111691>) | rdfs:label                                   | Task (xsd:string)                                                                              |
| ...                                            | ...                                          | ...                                                                                |

[Assistant]
The entity "Task" (<http://orkg.org/orkg/resource/R111691>) is typed as orkgc:Contribution, not as orkgc:Problem or a more specific class. However, it is linked to a "research problem" via orkgp:P32, which points to a more specific problem entity ("Fact Checking in Community Question Answering Forums").

This suggests that benchmarked problems may be modeled as the object of the "research problem" (orkgp:P32) property of a contribution/task.

To improve precision, I will:

- Find papers in Artificial Intelligence (orkgp:P30 <http://orkg.org/orkg/resource/R133>)
- Get their contributions (orkgp:P31)
- For each contribution, get the associated research problem (orkgp:P32)
- Return the IRIs and labels of these research problems, which are likely to be the actual benchmarked problems

This should filter out generic contributions and focus on specific benchmarked problems.

Let me construct and run this refined query.

[Tool]
Got 161 rows and 2 columns, showing the first 5 and last 5 rows below:
| problem                                                                         | problemLabel                                         |
| ------------------------------------------------------------------------------- | ---------------------------------------------------- |
| text extraction from images (<http://orkg.org/orkg/resource/R10011>)            | text extraction from images  (xsd:string)            |
| scene text detection (<http://orkg.org/orkg/resource/R10012>)                   | scene text detection (xsd:string)                    |
| Knowledge Graph Creation (<http://orkg.org/orkg/resource/R10021>)               | Knowledge Graph Creation (xsd:string)                |
| Scholarly communications representation (<http://orkg.org/orkg/resource/R1033>) | Scholarly communications representation (xsd:string) |
| Knowledge Graph Embeddings (<http://orkg.org/orkg/resource/R11025>)             | Knowledge Graph Embeddings (xsd:string)              |
| ...                                                                             | ...                                                  |
| Crowd simulation and evacuation (<http://orkg.org/orkg/resource/R8224>)         | Crowd simulation and evacuation (xsd:string)         |
| finding optimal evacuation strategy (<http://orkg.org/orkg/resource/R8225>)     | finding optimal evacuation strategy (xsd:string)     |
| Question Answering (<http://orkg.org/orkg/resource/R9143>)                      | Question Answering  (xsd:string)                     |
| Semantic Question Answering (<http://orkg.org/orkg/resource/R9145>)             | Semantic Question Answering (xsd:string)             |
| Scholarly Figure Text Extraction (<http://orkg.org/orkg/resource/R9570>)        | Scholarly Figure Text Extraction (xsd:string)        |

[Tool] Stopped generation process