PREFIX orkgp: <http://orkg.org/orkg/predicate/>
SELECT ?definition WHERE {
  <http://orkg.org/orkg/resource/R144136> orkgp:P24009 ?definition
}

Raman spectroscopy is a non-destructive chemical analysis technique which provides detailed information about chemical structure, phase and polymorphism, crystallinity and molecular interactions. It is based upon the interaction of light with the chemical bonds within a material, typically using a high intensity laser light source.

[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 Raman spectroscopy?

[Assistant]
First, I need to identify if "Raman spectroscopy" exists as an entity in the ORKG knowledge graph. I will search for this term.

Step 1: Search for the entity "Raman spectroscopy" in ORKG.

[Tool]
Top 10 entity alternatives:
1. Raman spectroscopy (<http://orkg.org/orkg/resource/R144136>)
2. Raman microprobe spectroscopy (<http://orkg.org/orkg/resource/R144145>)
3. Journal of Raman Spectroscopy (<http://orkg.org/orkg/resource/R144035>): has type Venue
4. Journal of Raman Spectroscopy (<http://orkg.org/orkg/resource/R144025>): has type Venue
5. Journal of Raman Spectroscopy (<http://orkg.org/orkg/resource/R144016>): has type Venue
6. Journal of Raman Spectroscopy (<http://orkg.org/orkg/resource/R138525>): has type Venue
7. Raman spectroscopy of the joaquinite minerals (<http://orkg.org/orkg/resource/R144034>): has type Paper
8. Raman spectroscopy of the borosilicate mineral ferroaxinite (<http://orkg.org/orkg/resource/R144024>): has type Paper
9. Spectroscopy characteristics (<http://orkg.org/orkg/resource/R238224>): Properties that describe the spectroscopic measuring process and/or end results., has type Quality type
10. Spectroscopy characteristics (<http://orkg.org/orkg/resource/R236394>): Properties that describe the spectroscopic measuring process and/or end results., has type Quality type

[Assistant]
The entity "Raman spectroscopy" exists in the ORKG knowledge graph with the IRI: <http://orkg.org/orkg/resource/R144136>.

Next, I will look for properties or descriptions associated with this entity to answer the question "What is Raman spectroscopy?". I will list the triples where this entity is the subject to find definitions or descriptions.

Step 2: List triples for the entity "Raman spectroscopy".

[Tool]
Got 3 rows and 3 columns:
| subject                                                      | property                      | object                                                                           |
| ------------------------------------------------------------ | ----------------------------- | -------------------------------------------------------------------------------- |
| Raman spectroscopy (<http://orkg.org/orkg/resource/R144136>) | Has definition (orkgp:P24009) | Raman Spectroscopy is a non-dest...gh intensity laser light source. (xsd:string) |
| Raman spectroscopy (<http://orkg.org/orkg/resource/R144136>) | rdf:type                      | orkgc:Resource                                                                   |
| Raman spectroscopy (<http://orkg.org/orkg/resource/R144136>) | rdfs:label                    | Raman spectroscopy  (xsd:string)                                                 |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query retrieves the definition of Raman spectroscopy using the appropriate property and returns the actual identifier in the WHERE clause. The answer provided matches the information retrieved from the knowledge graph. No further refinement is necessary.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?model ?modelLabel ?eval ?f1score ?ptb ?ptbLabel WHERE {
  ?ptb rdfs:label ?ptbLabel .
  FILTER ( REGEX( LCASE( ?ptbLabel ) , "penn treebank|ptb|treebank" ) ) ?ptb ?link ?eval .
  FILTER ( ?link IN ( orkgp:HAS_EVALUATION , orkgp:P5091 , orkgp:P34 , orkgp:P41838 , orkgp:P37290 , orkgp:HAS_BENCHMARK , orkgp:hasBenchmark , orkgp:P34103 , orkgp:P38001 , orkgp:P25063 , orkgp:P18087 ) ) ?eval ?f1p ?f1score .
  FILTER ( ?f1p IN ( orkgp:P18037 , orkgp:P36078 , orkgp:P71208 , orkgp:P23063 , orkgp:P71212 ) ) ?eval orkgp:HAS_MODEL ?model .
  ?model rdfs:label ?modelLabel .
}
ORDER BY DESC ( xsd:float ( ?f1score ) )
LIMIT 1

Even after expanding the F1-score property coverage, linkage properties, and label matching, there are still no results in ORKG for models with an F1 score on the Penn Treebank benchmark or its variants.

This suggests that either:
- Such evaluations are not present in the current ORKG data, or
- The data is represented in a structure not discoverable via the available properties and label conventions.

Here is the refined SPARQL query, which is comprehensive and covers all the discussed improvements:

```sparql
SELECT ?model ?modelLabel ?eval ?f1score ?ptb ?ptbLabel WHERE {
  ?ptb rdfs:label ?ptbLabel .
  FILTER(REGEX(LCASE(?ptbLabel), "penn treebank|ptb|treebank"))
  ?ptb ?link ?eval .
  FILTER(?link IN (
    orkgp:HAS_EVALUATION, orkgp:P5091, orkgp:P34, orkgp:P41838, orkgp:P37290,
    orkgp:HAS_BENCHMARK, orkgp:hasBenchmark, orkgp:P34103, orkgp:P38001,
    orkgp:P25063, orkgp:P18087
  ))
  ?eval ?f1p ?f1score .
  FILTER(?f1p IN (
    orkgp:P18037, orkgp:P36078, orkgp:P71208, orkgp:P23063, orkgp:P71212
  ))
  ?eval orkgp:HAS_MODEL ?model .
  ?model rdfs:label ?modelLabel .
}
ORDER BY DESC(xsd:float(?f1score))
LIMIT 1
```

**Conclusion:**
Despite a comprehensive search, there are no models in ORKG with a recorded F1 score on the Penn Treebank benchmark. If you have more specific information about how such results might be represented, I can further refine the search. Otherwise, this query represents the best possible approach with the current data.

[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] Which model has achieved the highest F1 score score on the Penn Treebank benchmark dataset?

[Assistant]
Let's break down the question:

1. Entities:
   - "model": Refers to machine learning models.
   - "F1 score": A metric for model evaluation.
   - "Penn Treebank benchmark dataset": A specific dataset used for benchmarking.

2. Properties:
   - "achieved": Relationship between model and score/dataset.
   - "F1 score": The value we want to maximize.
   - "on the Penn Treebank benchmark dataset": The context for the score.

First, I will search for the "Penn Treebank" entity in ORKG to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Penn Treebank (<http://orkg.org/orkg/resource/R116334>): has type Dataset
2. Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>): has type Benchmark
3. Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R130837>): has type Benchmark
4. the Penn Treebank (<http://orkg.org/orkg/resource/R141599>): WSJ sections 15-18 of the Penn Treebank as training material and section 20 as test material
5. English Penn Treebank (<http://orkg.org/orkg/resource/R172657>): has type Material
6. Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R120926>): has type Dataset
7. Penn Treebank (Character Level) (<http://orkg.org/orkg/resource/R121031>): has type Dataset
8. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130901>): has type Benchmark
9. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130922>): has type Benchmark
10. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130805>): has type Benchmark

[Assistant]
There are several entities related to "Penn Treebank" in ORKG, including datasets and benchmarks. Since the question asks about the "Penn Treebank benchmark dataset," the most relevant entities are:

- Penn Treebank (<http://orkg.org/orkg/resource/R116334>): Dataset
- Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>): Benchmark
- Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R130837>): Benchmark

To be comprehensive, I will focus on the general "Penn Treebank" dataset (R116334) and the main "Benchmark Penn Treebank" (R129333).

Next, I need to identify how F1 scores are represented for models evaluated on these benchmarks. I will search for properties related to "F1 score" in ORKG.

[Tool]
Top 10 property alternatives:
1. F1-Score (orkgp:P71208)
2. F1-score (orkgp:P23063)
3. approximate f1 score (orkgp:P71212)
4. F1-Score (Discuss) (orkgp:P67035)
5. F1-Score (Agree) (orkgp:P67033)
6. Average F1-Score (orkgp:P41894)
7. F1-score (Other) (orkgp:P68125)
8. F1-Score (Disagree) (orkgp:P67034)
9. Original Features (F1-score) (orkgp:P68074)
10. F1 (orkgp:P18037)

[Assistant]
There are several properties for F1 score in ORKG, with the most general being:

- F1-Score (orkgp:P71208)
- F1-score (orkgp:P23063)
- F1 (orkgp:P18037)

To ensure completeness, I will consider both "F1-Score" (orkgp:P71208) and "F1-score" (orkgp:P23063), as they are likely used for this metric.

Now, I need to find which models have F1 scores reported on the Penn Treebank benchmark. I will check how models, datasets, and scores are connected in ORKG by listing triples for the Penn Treebank benchmark entity (R129333).

[Tool]
Got 5 rows and 3 columns:
| subject                                                           | property                          | object                                                             |
| ----------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------ |
| Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) |
| Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>) | has dataset (orkgp:HAS_DATASET)   | Penn Treebank (<http://orkg.org/orkg/resource/R116334>)            |
| Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                        |
| Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>) | rdfs:label                        | Benchmark Penn Treebank (xsd:string)                               |
| Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>) | rdf:type                          | orkgc:Resource                                                     |

[Assistant]
The "Benchmark Penn Treebank" entity (R129333) has an "evaluation" property (orkgp:HAS_EVALUATION) pointing to an evaluation result (R129334: "Evaluation Accuracy_97.7"). This suggests that evaluation results are represented as separate entities.

To find F1 scores, I should look for evaluation entities related to the Penn Treebank benchmark and check if they have F1 score properties. I will list triples for the evaluation entity (R129334) to see if it contains F1 score information or links to a model.

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                             |
| ------------------------------------------------------------------ | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) | value (orkgp:HAS_VALUE)   | 97.7 (xsd:string)                                  |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) | rdfs:label                | Evaluation Accuracy_97.7 (xsd:string)              |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) | rdf:type                  | orkgc:Resource                                     |

[Assistant]
The evaluation entity (R129334) only contains an accuracy metric, not an F1 score. To find F1 scores, I need to search for evaluation entities related to the Penn Treebank benchmark that use F1 score properties.

I will search for entities connected to the Penn Treebank benchmark (R129333) via the "evaluation" property and then filter those that have F1 score properties.

[Tool]
Got 1 row and 1 column:
| eval                                                               |
| ------------------------------------------------------------------ |
| Evaluation Accuracy_97.7 (<http://orkg.org/orkg/resource/R129334>) |

[Assistant]
There is only one evaluation entity directly linked to the Penn Treebank benchmark (R129333), and it only contains accuracy, not F1 score.

To be thorough, I should also check the general "Penn Treebank" dataset (R116334) for evaluations, as some results might be linked to the dataset rather than the benchmark entity. I will list triples for the Penn Treebank dataset (R116334) to see if there are any evaluation or model links.

[Tool]
Got 3 rows and 3 columns:
| subject                                                 | property   | object                     |
| ------------------------------------------------------- | ---------- | -------------------------- |
| Penn Treebank (<http://orkg.org/orkg/resource/R116334>) | rdf:type   | Dataset (orkgc:Dataset)    |
| Penn Treebank (<http://orkg.org/orkg/resource/R116334>) | rdfs:label | Penn Treebank (xsd:string) |
| Penn Treebank (<http://orkg.org/orkg/resource/R116334>) | rdf:type   | orkgc:Resource             |

[Assistant]
The general "Penn Treebank" dataset entity (R116334) does not have any direct evaluation or model links.

Since direct links from the benchmark and dataset entities do not yield F1 score evaluations, I need to take a different approach. I will search for evaluation entities in ORKG that mention "Penn Treebank" and have an F1 score property. This could help find evaluations not directly linked to the main dataset or benchmark entities.

I will search for entities with "Penn Treebank" in their label or description.

[Tool]
Top 10 entity alternatives:
1. Penn Treebank (<http://orkg.org/orkg/resource/R116334>): has type Dataset
2. Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>): has type Benchmark
3. Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R130837>): has type Benchmark
4. the Penn Treebank (<http://orkg.org/orkg/resource/R141599>): WSJ sections 15-18 of the Penn Treebank as training material and section 20 as test material
5. English Penn Treebank (<http://orkg.org/orkg/resource/R172657>): has type Material
6. Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R120926>): has type Dataset
7. Penn Treebank (Character Level) (<http://orkg.org/orkg/resource/R121031>): has type Dataset
8. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130901>): has type Benchmark
9. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130922>): has type Benchmark
10. Benchmark Penn Treebank (Word Level) (<http://orkg.org/orkg/resource/R130805>): has type Benchmark

[Assistant]
There are several variants of the Penn Treebank dataset and benchmark, including word-level and character-level versions. Since the question does not specify which variant, I should consider all entities containing "Penn Treebank" in their label.

To find F1 scores, I will search for evaluation entities that are associated with any "Penn Treebank" entity and have an F1 score property. I will construct a SPARQL query that:

- Finds evaluations linked to any entity with "Penn Treebank" in its label.
- Retrieves the F1 score and the model associated with the evaluation.
- Orders the results by F1 score in descending order to find the highest score.

First, I will check how models are linked to evaluations in ORKG by searching for properties related to "model" for an evaluation entity.

[Tool]
Top 10 property alternatives:
1. value (orkgp:HAS_VALUE)
2. metric (orkgp:HAS_METRIC)

No other items found

[Assistant]
It appears that the evaluation entity does not have a direct property linking it to a "model" entity. However, in ORKG, models are often represented as resources, and evaluations may be linked to models via other entities or as part of a comparison.

Given this, I will write a SPARQL query that:

- Finds all evaluations with an F1 score property (using both orkgp:P71208 and orkgp:P23063).
- Ensures the evaluation is associated with any entity whose label contains "Penn Treebank".
- Attempts to retrieve the model (if present) and the F1 score value.
- Orders results by F1 score descending and limits to the top result.

Here is the SPARQL query:

[Tool] Got no rows and 4 columns

[Assistant]
The query returned no results, which suggests that there are no evaluation entities in ORKG with an F1 score property directly linked to any "Penn Treebank" entity, or that the F1 score may be represented differently (e.g., as part of a comparison or in a different structure).

Given the lack of direct F1 score data for the Penn Treebank benchmark in ORKG, I am unable to provide a satisfactory SPARQL query that answers the question as intended.

I will now provide a detailed explanation and my best attempt at a SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The explanation is reasonable and the SPARQL query is well-structured for the intended question. However, there are a few areas for refinement:

1. The query currently searches for F1-score properties using two different property IRIs (orkgp:P71208 and orkgp:P23063). It would be helpful to verify if these are indeed the only relevant properties for F1-score in the ORKG, or if there are additional variants (e.g., lowercase, alternate spellings, or other F1-related properties) that should be included.

2. The linkage between the Penn Treebank dataset and evaluations is done via any property (?ptb ?p ?eval). This could be too broad and may miss more specific relationships (e.g., 'has benchmark', 'evaluated on', etc.). Consider expanding the query to include other plausible properties that might link the dataset to evaluations.

3. The query assumes the Penn Treebank is always labeled as such. There could be alternate labels or abbreviations (e.g., 'PTB'). Consider broadening the label filter.

4. The query does not check if the model is actually present (it is optional). If the model is missing, the result may be incomplete. Consider filtering out results where ?model is not bound, or at least noting this in the explanation.

Overall, the query is close, but a more comprehensive exploration of possible property and label variants could improve the chances of finding an answer if the data exists.

[Assistant]
Thank you for the detailed feedback. Here’s how I’ll refine the approach:

**Step-by-step plan:**

1. **Expand F1-score property coverage:**
   I will search for all properties related to "F1 score" in ORKG and include all relevant variants in the query.

2. **Broaden linkage properties:**
   I will search for properties that plausibly link datasets/benchmarks to evaluations (e.g., "has evaluation", "evaluated on", "has benchmark", etc.) and include them in the query.

3. **Expand dataset label matching:**
   I will include alternate labels and abbreviations such as "PTB" in the label filter.

4. **Model presence:**
   I will ensure the query only returns results where a model is present, or at least clearly indicate if the model is missing.

Let’s proceed step by step.

---

**Step 1: Expand F1-score property coverage**
I previously found several F1-score related properties. I will now explicitly list all their IRIs for completeness.

[Tool]
Top 10 property alternatives:
1. F1 (orkgp:P18037)
2. Best F1 (orkgp:P36078)
3. F1-Score (orkgp:P71208)
4. F1-score (orkgp:P23063)
5. F1-score (True News) (orkgp:P68127)
6. approximate f1 score (orkgp:P71212)
7. F1-Score (Discuss) (orkgp:P67035)
8. F1-Score (Agree) (orkgp:P67033)
9. F1-Score (Real News) (orkgp:P66044)
10. Original Features (F1-score) (orkgp:P68074)

[Assistant]
There are several F1-score related properties. For a general search, the most relevant are:

- orkgp:P18037 (F1)
- orkgp:P36078 (Best F1)
- orkgp:P71208 (F1-Score)
- orkgp:P23063 (F1-score)
- orkgp:P71212 (approximate f1 score)

I will include these in the query to maximize coverage.

---

**Step 2: Broaden linkage properties**
I will search for properties related to "evaluation", "benchmark", and "dataset" to see how evaluations might be linked to datasets/benchmarks.

[Tool]
Top 10 property alternatives:
1. evaluation (orkgp:P5091)
2. evaluation (orkgp:P34)
3. evaluation (orkgp:HAS_EVALUATION)
4. Evaluated on (orkgp:P41838)
5. Means of evaluation (orkgp:P36082)
6. Evaluation method (orkgp:P59089)
7. evaluates (orkgp:P37290)
8. type of evaluation (orkgp:P82001)
9. Evaluation results (orkgp:P25064)
10. evaluate (orkgp:P20041)

[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)

[Tool]
Top 10 property alternatives:
1. Dataset (orkgp:P96045)
2. dataset (orkgp:P2005)
3. Dataset used (orkgp:P25063)
4. Datasets (orkgp:P44054)
5. Uses dataset (orkgp:P18087)
6. dataset description (orkgp:P16037)
7. Datasets used (orkgp:P58072)
8. dataset format (orkgp:P62072)
9. Dataset name (orkgp:P34063)
10. Experimental Dataset (orkgp:P15096)

[Assistant]
Relevant linkage properties for connecting datasets/benchmarks to evaluations include:

- orkgp:HAS_EVALUATION
- orkgp:P5091 (evaluation)
- orkgp:P34 (evaluation)
- orkgp:P41838 (Evaluated on)
- orkgp:P37290 (evaluates)
- orkgp:HAS_BENCHMARK
- orkgp:hasBenchmark
- orkgp:P34103 (Has benchmark)
- orkgp:P38001 (uses benchmark)
- orkgp:P25063 (Dataset used)
- orkgp:P18087 (Uses dataset)

I will include these as possible linkage properties in the query.

---

**Step 3: Expand dataset label matching**
I will match labels containing "penn treebank", "ptb", or "treebank" (case-insensitive).

---

**Step 4: Model presence**
I will filter out results where ?model is not bound.

---

**Now, I will construct the refined SPARQL query:**

[Tool] Got no rows and 6 columns

[Assistant]
Even after expanding the F1-score property coverage, linkage properties, and label matching, there are still no results in ORKG for models with an F1 score on the Penn Treebank benchmark or its variants.

This suggests that either:
- Such evaluations are not present in the current ORKG data, or
- The data is represented in a structure not discoverable via the available properties and label conventions.

Here is the refined SPARQL query, which is comprehensive and covers all the discussed improvements:

```sparql
SELECT ?model ?modelLabel ?eval ?f1score ?ptb ?ptbLabel WHERE {
  ?ptb rdfs:label ?ptbLabel .
  FILTER(REGEX(LCASE(?ptbLabel), "penn treebank|ptb|treebank"))
  ?ptb ?link ?eval .
  FILTER(?link IN (
    orkgp:HAS_EVALUATION, orkgp:P5091, orkgp:P34, orkgp:P41838, orkgp:P37290,
    orkgp:HAS_BENCHMARK, orkgp:hasBenchmark, orkgp:P34103, orkgp:P38001,
    orkgp:P25063, orkgp:P18087
  ))
  ?eval ?f1p ?f1score .
  FILTER(?f1p IN (
    orkgp:P18037, orkgp:P36078, orkgp:P71208, orkgp:P23063, orkgp:P71212
  ))
  ?eval orkgp:HAS_MODEL ?model .
  ?model rdfs:label ?modelLabel .
}
ORDER BY DESC(xsd:float(?f1score))
LIMIT 1
```

**Conclusion:**
Despite a comprehensive search, there are no models in ORKG with a recorded F1 score on the Penn Treebank benchmark. If you have more specific information about how such results might be represented, I can further refine the search. Otherwise, this query represents the best possible approach with the current data.

[User]
Feedback (status=done):
The answer is correct and complete. The SPARQL query is comprehensive, covers a wide range of possible property and label variants for both the Penn Treebank benchmark and F1 score, and returns the required identifiers. The explanation clearly states that no relevant results were found in the ORKG knowledge graph, and the query itself is well-structured and follows all the specified rules. No further refinement is necessary.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?paper ?title WHERE {
  ?paper orkgp:hasContribution ?contribution .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R122338> .
  OPTIONAL {
    ?paper rdfs:label ?title
  }
}

This query retrieves the IDs and (optionally) the titles of research papers that include a benchmark for the DCASE dataset, by following the benchmark's link to the DCASE dataset.

[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 are the titles and IDs of research papers that include a benchmark for the DCASE dataset?

[Assistant]
Let's break down the question:

1. We are looking for research papers (entities of type "research paper").
2. These papers should include a "benchmark" (likely as a property or related entity).
3. The benchmark should be for the "DCASE dataset".

Step 1: Search for the "DCASE dataset" entity in ORKG to get its IRI.

Step 2: Search for properties related to "benchmark" in ORKG.

Step 3: Find research papers that have a benchmark for the DCASE dataset, and retrieve their titles and IDs.

Let's start with Step 1: search for the "DCASE dataset" entity.

[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 "DCASE dataset" returned several generic "Dataset" entities, but none are explicitly labeled as "DCASE". To ensure accuracy, I'll refine the search to "DCASE" only, which might yield a more specific entity.

Next, I'll search for the entity "DCASE" to try to find the correct dataset.

[Tool]
Top 10 entity alternatives:
1. DCASE (<http://orkg.org/orkg/resource/R122338>): has type Dataset
2. Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>): has type Benchmark

[Assistant]
Great, we have found two relevant entities:
1. DCASE (<http://orkg.org/orkg/resource/R122338>) — type: Dataset
2. Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) — type: Benchmark

Since the question asks for papers that include a benchmark for the DCASE dataset, the most direct approach is to look for research papers that reference either the DCASE dataset or the DCASE benchmark.

Next, let's search for properties related to "benchmark" to see how benchmarks are linked to research papers in ORKG.

[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]
We have found several properties related to "benchmark," including:
- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- has benchmark (orkgp:HAS_BENCHMARK)

To be thorough, let's check which of these properties are actually used to link research papers to the DCASE dataset or DCASE benchmark. I'll start by listing triples where the object is DCASE (<http://orkg.org/orkg/resource/R122338>) or Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>), and the property is one of the benchmark-related ones.

Let's try with "uses benchmark" (orkgp:P38001) and DCASE benchmark as the object.

[Tool] Got no rows and 3 columns

[Assistant] It appears that there are no triples using the "uses benchmark" property with the DCASE benchmark as the object. Let's try the next property: "Has benchmark" (orkgp:P34103) with the DCASE benchmark as the object.

[Tool] Got no rows and 3 columns

[Assistant] There are no triples using "Has benchmark" with the DCASE benchmark either. Let's try the next property: "has benchmark" (orkgp:hasBenchmark) with the DCASE benchmark as the object.

[Tool] Got no rows and 3 columns

[Assistant] There are still no results. Let's try the last similar property: "has benchmark" (orkgp:HAS_BENCHMARK) with the DCASE benchmark as the object.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                            | object                                                    |
| ----------------------------------------------------------- | ----------------------------------- | --------------------------------------------------------- |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) |

[Assistant]
We have found one triple:
- Subject: Contribution XDC (<http://orkg.org/orkg/resource/R129978>)
- Property: has benchmark (orkgp:HAS_BENCHMARK)
- Object: Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>)

This means that "Contribution XDC" is linked to the DCASE benchmark. In ORKG, "contributions" are often linked to research papers. Let's check if this contribution is associated with a research paper, and if so, retrieve the paper's title and ID.

I'll list triples where the object is this contribution to find its associated research paper.

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                      |
| -------------------------------------------------------------------------------------------------------- | ------------------------ | ----------------------------------------------------------- |
| Self-Supervised Learning by Cross-Modal Audio-Video Clustering (<http://orkg.org/orkg/resource/R129977>) | contribution (orkgp:P31) | Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) |

[Assistant]
The contribution "Contribution XDC" is linked to the research paper "Self-Supervised Learning by Cross-Modal Audio-Video Clustering" (<http://orkg.org/orkg/resource/R129977>).

Now, let's construct a SPARQL query to generalize this process: find all research papers that have a contribution which has a benchmark (using orkgp:HAS_BENCHMARK) for the DCASE benchmark (<http://orkg.org/orkg/resource/R130049>), and return their IDs and titles.

I will also include the title as an optional field in case some papers do not have it.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is mostly correct, as it retrieves the IDs and optionally the titles of research papers that include a benchmark for the DCASE dataset. However, there is a potential issue with the use of the property orkgp:P31 for linking papers to contributions. In ORKG, the property orkgp:P31 is typically used for 'instance of', not for linking papers to their contributions. The correct property for linking a paper to its contributions is usually orkgp:hasContribution. The query should be refined to use the correct property for linking papers to contributions to ensure completeness and correctness of the results.

[Assistant]
Thank you for the feedback. You are correct: in ORKG, the property orkgp:hasContribution is typically used to link research papers to their contributions, not orkgp:P31 (which is generally 'instance of').

Let's proceed step by step:

1. Use orkgp:hasContribution to link research papers to their contributions.
2. From the contribution, use orkgp:HAS_BENCHMARK to link to the DCASE benchmark.
3. Retrieve the paper's ID and (optionally) its title.

Here is the refined SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is structurally correct and follows the rules: it returns the paper IRIs (IDs) and optionally their titles, and does not use non-standard label services. However, the query returned no results. This could be due to the use of the property 'orkgp:HAS_BENCHMARK' and the resource '<http://orkg.org/orkg/resource/R130049>' for DCASE, which may not match the actual data in the ORKG.

Suggestions for refinement:
1. Double-check that '<http://orkg.org/orkg/resource/R130049>' is indeed the correct IRI for the DCASE dataset in the ORKG.
2. Verify that 'orkgp:HAS_BENCHMARK' is the correct property for linking contributions to benchmarks in the ORKG schema. Sometimes, the property might be named differently (e.g., 'hasBenchmark', 'PXXX', etc.).
3. Consider relaxing the query by searching for papers where the contribution mentions DCASE in any relevant property (e.g., using rdfs:label or other dataset-related properties) if the direct benchmark property is not used consistently.

The query logic is good, but it needs refinement to ensure it matches the actual data in the knowledge graph.

[Assistant]
Thank you for the detailed feedback and suggestions. I will follow your recommendations step by step:

Step 1: Double-check that <http://orkg.org/orkg/resource/R130049> is the correct IRI for the DCASE dataset or benchmark.
Step 2: Verify the correct property for linking contributions to benchmarks.
Step 3: If direct links are not found, relax the query to find contributions mentioning DCASE in any relevant property or label.

Let's start by listing all triples where the object is <http://orkg.org/orkg/resource/R130049> to see how it is used and which properties are involved.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                            | object                                                    |
| ----------------------------------------------------------- | ----------------------------------- | --------------------------------------------------------- |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) |

[Assistant]
The only triple involving <http://orkg.org/orkg/resource/R130049> uses the property orkgp:HAS_BENCHMARK, linking it to "Contribution XDC". This confirms that this IRI is used for the DCASE benchmark, and orkgp:HAS_BENCHMARK is used in at least this instance.

However, to ensure completeness, let's check if there are other properties linking contributions to benchmarks by searching for properties related to "benchmark" that are actually used with contributions. I'll list all properties used with the subject "Contribution XDC" (<http://orkg.org/orkg/resource/R129978>).

[Tool]
Got 99 rows and 3 columns, showing the first 10 rows below:
| subject                                                     | property                                | object                                                                       |
| ----------------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------------------- |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | research problem (orkgp:P32)            | Self-Supervised Action Recognition (<http://orkg.org/orkg/resource/R118765>) |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/HumamAlwassel/XDC (xsd:string)                            |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark HMDB51 (<http://orkg.org/orkg/resource/R129983>)                   |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has model (orkgp:HAS_MODEL)             | XDC (<http://orkg.org/orkg/resource/R118769>)                                |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | rdf:type                                | Contribution (orkgc:Contribution)                                            |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | rdfs:label                              | Contribution 	XDC (xsd:string)                                               |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark UCF101 (<http://orkg.org/orkg/resource/R129992>)                   |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>)                    |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark ESC-50 (<http://orkg.org/orkg/resource/R130053>)                   |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark HMDB51 (finetuned) (<http://orkg.org/orkg/resource/R129979>)       |
| ...                                                         | ...                                     | ...                                                                          |

[Assistant]
The property orkgp:HAS_BENCHMARK is indeed used to link contributions to various benchmarks, including Benchmark DCASE. This confirms that our previous approach was correct for this instance.

However, since the previous query returned no results, let's check if there are other contributions linked to DCASE benchmarks, or if DCASE is mentioned in other ways (e.g., in rdfs:label or other dataset-related properties).

Let's search for contributions (entities of type orkgc:Contribution) that mention "DCASE" in their rdfs:label.

[Tool]
Top 10 entity alternatives:
1. DCASE (<http://orkg.org/orkg/resource/R122338>): has type Dataset
2. Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>): has type Benchmark

[Tool]
Got 21,259 rows and 3 columns, showing the first 10 rows below:
| subject                                                                  | property                                                | object                            |
| ------------------------------------------------------------------------ | ------------------------------------------------------- | --------------------------------- |
| Component for template R138077 (<http://orkg.org/orkg/resource/R236251>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Contribution (<http://orkg.org/orkg/resource/R172145>)                   | Template of class (orkgp:TemplateOfClass)               | Contribution (orkgc:Contribution) |
| Contribution (<http://orkg.org/orkg/resource/R249265>)                   | rdf:type                                                | Contribution (orkgc:Contribution) |
| Component for template R111155 (<http://orkg.org/orkg/resource/R111158>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R111155 (<http://orkg.org/orkg/resource/R111170>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R111155 (<http://orkg.org/orkg/resource/R111181>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R111155 (<http://orkg.org/orkg/resource/R111194>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R111155 (<http://orkg.org/orkg/resource/R111206>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R138077 (<http://orkg.org/orkg/resource/R149994>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| Component for template R138077 (<http://orkg.org/orkg/resource/R150645>) | Template component value (orkgp:TemplateComponentValue) | Contribution (orkgc:Contribution) |
| ...                                                                      | ...                                                     | ...                               |

[Assistant]
There are many contributions, but searching through all of them directly is inefficient. Instead, let's search for any entity (including contributions) whose rdfs:label contains "DCASE". This will help us identify all relevant contributions or papers mentioning DCASE, regardless of the property used.

I'll construct a SPARQL query to find all entities with "DCASE" in their rdfs:label, and then check if any of them are contributions linked to research papers.

[Tool]
Got 2 rows and 2 columns:
| entity                                                    | label                        |
| --------------------------------------------------------- | ---------------------------- |
| DCASE (<http://orkg.org/orkg/resource/R122338>)           | DCASE (xsd:string)           |
| Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) | Benchmark DCASE (xsd:string) |

[Assistant]
The only entities with "DCASE" in their rdfs:label are:
- DCASE (<http://orkg.org/orkg/resource/R122338>) — labeled as "DCASE"
- Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) — labeled as "Benchmark DCASE"

No contributions or papers directly mention "DCASE" in their rdfs:label.

As a next step, let's check if any contributions are linked to the DCASE dataset entity (<http://orkg.org/orkg/resource/R122338>) via any property. This will help us find indirect links, such as "uses dataset" or similar properties.

[Tool]
Got 1 row and 3 columns:
| subject                                                   | property                        | object                                          |
| --------------------------------------------------------- | ------------------------------- | ----------------------------------------------- |
| Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>) | has dataset (orkgp:HAS_DATASET) | DCASE (<http://orkg.org/orkg/resource/R122338>) |

[Assistant]
The only triple involving the DCASE dataset (<http://orkg.org/orkg/resource/R122338>) is:
- Subject: Benchmark DCASE (<http://orkg.org/orkg/resource/R130049>)
- Property: has dataset (orkgp:HAS_DATASET)
- Object: DCASE (<http://orkg.org/orkg/resource/R122338>)

This means that the DCASE dataset is only referenced as part of the DCASE benchmark, and the only contribution linked to the DCASE benchmark is "Contribution XDC".

To ensure completeness, let's check if there are any other contributions linked to the DCASE dataset or benchmark via any property by searching for all triples where the object is either <http://orkg.org/orkg/resource/R122338> or <http://orkg.org/orkg/resource/R130049>.

Since we've already checked both and found only one relevant contribution, let's now generalize the query to return all research papers whose contributions are linked to any benchmark that uses the DCASE dataset.

So, the logic is:
- Paper orkgp:hasContribution Contribution
- Contribution orkgp:HAS_BENCHMARK Benchmark
- Benchmark orkgp:HAS_DATASET DCASE

Let's write this SPARQL query.

[Tool] Stopped generation process


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?model ?modelLabel WHERE {
  VALUES ?dataset {
    <http://orkg.org/orkg/resource/R128074>
  }
  ?benchmark orkgp:HAS_DATASET ?dataset .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark ; orkgp:HAS_MODEL ?model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel
  }
}

The models that have been tested on the NLP-TDMS (Exp, arXiv only) benchmark dataset are:
- AxCell (http://orkg.org/orkg/resource/R128071)
- TDMS-IE (http://orkg.org/orkg/resource/R128075)

[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] Could you provide a list of models that have been tested on the NLP-TDMS (Exp, arXiv only) benchmark dataset?

[Assistant]
Let's break down the question:

1. We need to find the entity for the "NLP-TDMS (Exp, arXiv only)" benchmark dataset.
2. Then, we need to find models that have been tested on this dataset.
3. The relationship is likely something like "tested on", "evaluated on", or "benchmark".

First, I'll search for the entity "NLP-TDMS (Exp, arXiv only)" in the ORKG knowledge graph.

[Tool]
Top 10 entity alternatives:
1. NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>): has type Dataset
2. Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>): has type Benchmark
3. Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>): has type Benchmark
4. NLP-TDMS (<http://orkg.org/orkg/resource/R217408>): has type Benchmark Dataset, has type Contribution, has type Dataset
5. NLP-TDMS (<http://orkg.org/orkg/resource/R147521>): The resulting dataset NLP-TDMS (Full) contains 332 papers with 8...ound half of them (77) are associated with at least five papers.
6. arXiv (<http://orkg.org/orkg/resource/wikidata:Q118398>): has type External
7. arxiv (<http://orkg.org/orkg/resource/R215448>): has type Venue
8. arXiv (<http://orkg.org/orkg/resource/R210205>): has type Venue
9. ArXiv (<http://orkg.org/orkg/resource/R206411>): has type Venue
10. ArXiv (<http://orkg.org/orkg/resource/R206398>): has type Venue

[Assistant]
I found several closely related entities, but the most relevant for your question appears to be:

- NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) — type: Dataset

There are also two entities labeled "Benchmark NLP-TDMS (Exp, arXiv only)" with different IRIs, but since your question specifically asks for the dataset, I will use the dataset entity.

Next, I need to find which models have been tested on this dataset. To do this, I will search for properties related to "tested on", "evaluated on", or "benchmark" to find the correct relationship between models and datasets in ORKG.

[Tool]
Top 10 property alternatives:
1. Test (orkgp:P18019)
2. #Test (orkgp:P15371)
3. tested subjects (orkgp:P59184)
4. Test set (orkgp:P67096)
5. Tests (orkgp:P35115)
6. Bioassays conducted (orkgp:P37002)
7. materials for testing (orkgp:P59182)
8. Testing Set (orkgp:P65042)
9. has experiment (orkgp:P37152)
10. Type of test (orkgp:P15685)

[Tool]
Top 10 property alternatives:
1. Evaluated on (orkgp:P41838)
2. evaluate on (orkgp:P20042)
3. evaluates (orkgp:P37290)
4. evaluate-on (orkgp:P20081)
5. evaluation (orkgp:P5091)
6. evaluation (orkgp:P34)
7. evaluation (orkgp:HAS_EVALUATION)
8. evaluate (orkgp:P20041)
9. method evaluated (orkgp:P82000)
10. Has evaluation (orkgp:HasEvaluation)

[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)

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
None of the most likely properties ("evaluated on", "tested on", "benchmark", etc.) returned any results linking models to the "NLP-TDMS (Exp, arXiv only)" dataset.

It's possible that:
- The relationship is modeled differently in ORKG (e.g., via a contribution, experiment, or paper entity).
- The models are linked through a more indirect path, such as being part of a contribution that references the dataset.

Next, I will check what entities are directly linked to the dataset "NLP-TDMS (Exp, arXiv only)" to see if there is an indirect path (e.g., via contributions or experiments).

[Tool]
Got 3 rows and 3 columns:
| subject                                                              | property   | object                                  |
| -------------------------------------------------------------------- | ---------- | --------------------------------------- |
| NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) | rdf:type   | Dataset (orkgc:Dataset)                 |
| NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) | rdfs:label | NLP-TDMS (Exp, arXiv only) (xsd:string) |
| NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) | rdf:type   | orkgc:Resource                          |

[Tool]
Got 2 rows and 3 columns:
| subject                                                                        | property                        | object                                                               |
| ------------------------------------------------------------------------------ | ------------------------------- | -------------------------------------------------------------------- |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | has dataset (orkgp:HAS_DATASET) | NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | has dataset (orkgp:HAS_DATASET) | NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>) |

[Tool]
Got 10 rows and 3 columns:
| subject                                                                        | property                          | object                                                                    |
| ------------------------------------------------------------------------------ | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>)        |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | has dataset (orkgp:HAS_DATASET)   | NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>)      |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | rdfs:label                        | Benchmark NLP-TDMS (Exp, arXiv only) (xsd:string)                         |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>)    |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>)        |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>)    |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) | rdf:type                          | orkgc:Resource                                                            |

[Tool]
Got 10 rows and 3 columns:
| subject                                                                        | property                          | object                                                                   |
| ------------------------------------------------------------------------------ | --------------------------------- | ------------------------------------------------------------------------ |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>)        |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | has dataset (orkgp:HAS_DATASET)   | NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R128074>)     |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                              |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | rdfs:label                        | Benchmark NLP-TDMS (Exp, arXiv only) (xsd:string)                        |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>)    |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>)        |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>)    |
| Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) | rdf:type                          | orkgc:Resource                                                           |

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                             |
| ------------------------------------------------------------------ | ------------------------- | -------------------------------------------------- |
| Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>) | value (orkgp:HAS_VALUE)   | 19.7 (xsd:string)                                  |
| Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>) | metric (orkgp:HAS_METRIC) | Macro F1 (<http://orkg.org/orkg/resource/R114479>) |
| Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>) | rdfs:label                | Evaluation Macro F1_19.7 (xsd:string)              |
| Evaluation Macro F1_19.7 (<http://orkg.org/orkg/resource/R135196>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) | value (orkgp:HAS_VALUE)   | 20.2 (xsd:string)                                         |
| Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) | metric (orkgp:HAS_METRIC) | Macro Precision (<http://orkg.org/orkg/resource/R121300>) |
| Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) | rdfs:label                | Evaluation Macro Precision_20.2 (xsd:string)              |
| Evaluation Macro Precision_20.2 (<http://orkg.org/orkg/resource/R135197>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                  | object                                                 |
| ---------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------ |
| Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>) | value (orkgp:HAS_VALUE)   | 20.6 (xsd:string)                                      |
| Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>) | metric (orkgp:HAS_METRIC) | Macro Recall (<http://orkg.org/orkg/resource/R121301>) |
| Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>) | rdf:type                  | Evaluation (orkgc:Evaluation)                          |
| Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>) | rdfs:label                | Evaluation Macro Recall_20.6 (xsd:string)              |
| Evaluation Macro Recall_20.6 (<http://orkg.org/orkg/resource/R135198>) | rdf:type                  | orkgc:Resource                                         |

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                  | object                                             |
| ------------------------------------------------------------------ | ------------------------- | -------------------------------------------------- |
| Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>) | value (orkgp:HAS_VALUE)   | 25.8 (xsd:string)                                  |
| Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>) | metric (orkgp:HAS_METRIC) | Micro F1 (<http://orkg.org/orkg/resource/R114480>) |
| Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>) | rdfs:label                | Evaluation Micro F1_25.8 (xsd:string)              |
| Evaluation Micro F1_25.8 (<http://orkg.org/orkg/resource/R135199>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) | value (orkgp:HAS_VALUE)   | 27.4 (xsd:string)                                         |
| Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) | metric (orkgp:HAS_METRIC) | Micro Precision (<http://orkg.org/orkg/resource/R128072>) |
| Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) | rdfs:label                | Evaluation Micro Precision_27.4 (xsd:string)              |
| Evaluation Micro Precision_27.4 (<http://orkg.org/orkg/resource/R135200>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                  | object                                                 |
| ---------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------ |
| Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>) | value (orkgp:HAS_VALUE)   | 24.4 (xsd:string)                                      |
| Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>) | metric (orkgp:HAS_METRIC) | Micro Recall (<http://orkg.org/orkg/resource/R128073>) |
| Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>) | rdf:type                  | Evaluation (orkgc:Evaluation)                          |
| Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>) | rdfs:label                | Evaluation Micro Recall_24.4 (xsd:string)              |
| Evaluation Micro Recall_24.4 (<http://orkg.org/orkg/resource/R135201>) | rdf:type                  | orkgc:Resource                                         |

[Tool]
Got 5 rows and 3 columns:
| subject                                                           | property                  | object                                             |
| ----------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>) | value (orkgp:HAS_VALUE)   | 8.8 (xsd:string)                                   |
| Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>) | metric (orkgp:HAS_METRIC) | Macro F1 (<http://orkg.org/orkg/resource/R114479>) |
| Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>) | rdfs:label                | Evaluation Macro F1_8.8 (xsd:string)               |
| Evaluation Macro F1_8.8 (<http://orkg.org/orkg/resource/R135205>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                  | object                                                    |
| ------------------------------------------------------------------------ | ------------------------- | --------------------------------------------------------- |
| Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) | value (orkgp:HAS_VALUE)   | 9.5 (xsd:string)                                          |
| Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) | metric (orkgp:HAS_METRIC) | Macro Precision (<http://orkg.org/orkg/resource/R121300>) |
| Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) | rdfs:label                | Evaluation Macro Precision_9.5 (xsd:string)               |
| Evaluation Macro Precision_9.5 (<http://orkg.org/orkg/resource/R135206>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                               | property                  | object                                                 |
| --------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------ |
| Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>) | value (orkgp:HAS_VALUE)   | 8.6 (xsd:string)                                       |
| Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>) | metric (orkgp:HAS_METRIC) | Macro Recall (<http://orkg.org/orkg/resource/R121301>) |
| Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>) | rdf:type                  | Evaluation (orkgc:Evaluation)                          |
| Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>) | rdfs:label                | Evaluation Macro Recall_8.6 (xsd:string)               |
| Evaluation Macro Recall_8.6 (<http://orkg.org/orkg/resource/R135207>) | rdf:type                  | orkgc:Resource                                         |

[Tool]
Got 5 rows and 3 columns:
| subject                                                           | property                  | object                                             |
| ----------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>) | value (orkgp:HAS_VALUE)   | 7.5 (xsd:string)                                   |
| Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>) | metric (orkgp:HAS_METRIC) | Micro F1 (<http://orkg.org/orkg/resource/R114480>) |
| Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>) | rdfs:label                | Evaluation Micro F1_7.5 (xsd:string)               |
| Evaluation Micro F1_7.5 (<http://orkg.org/orkg/resource/R135208>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                  | object                                                    |
| ------------------------------------------------------------------------ | ------------------------- | --------------------------------------------------------- |
| Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) | value (orkgp:HAS_VALUE)   | 6.8 (xsd:string)                                          |
| Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) | metric (orkgp:HAS_METRIC) | Micro Precision (<http://orkg.org/orkg/resource/R128072>) |
| Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) | rdfs:label                | Evaluation Micro Precision_6.8 (xsd:string)               |
| Evaluation Micro Precision_6.8 (<http://orkg.org/orkg/resource/R135209>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                               | property                  | object                                                 |
| --------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------ |
| Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>) | value (orkgp:HAS_VALUE)   | 8.4 (xsd:string)                                       |
| Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>) | metric (orkgp:HAS_METRIC) | Micro Recall (<http://orkg.org/orkg/resource/R128073>) |
| Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>) | rdf:type                  | Evaluation (orkgc:Evaluation)                          |
| Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>) | rdfs:label                | Evaluation Micro Recall_8.4 (xsd:string)               |
| Evaluation Micro Recall_8.4 (<http://orkg.org/orkg/resource/R135210>) | rdf:type                  | orkgc:Resource                                         |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject                                                        | property                            | object                                                                         |
| -------------------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------ |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                         | property                            | object                                                                         |
| --------------------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------ |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) |

[Tool]
Got 8 rows and 3 columns:
| subject                                                        | property                                | object                                                                            |
| -------------------------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------------------- |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | research problem (orkgp:P32)            | Scientific Results Extraction (<http://orkg.org/orkg/resource/R128069>)           |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/paperswithcode/axcell (xsd:string)                             |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark PWC Leaderboards (restricted) (<http://orkg.org/orkg/resource/R135188>) |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | has model (orkgp:HAS_MODEL)             | AxCell (<http://orkg.org/orkg/resource/R128071>)                                  |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | rdf:type                                | Contribution (orkgc:Contribution)                                                 |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | rdfs:label                              | Contribution 	AxCell (xsd:string)                                                 |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135195>)    |
| Contribution  AxCell (<http://orkg.org/orkg/resource/R135187>) | rdf:type                                | orkgc:Resource                                                                    |

[Tool]
Got 7 rows and 3 columns:
| subject                                                         | property                                | object                                                                         |
| --------------------------------------------------------------- | --------------------------------------- | ------------------------------------------------------------------------------ |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | research problem (orkgp:P32)            | Scientific Results Extraction (<http://orkg.org/orkg/resource/R128069>)        |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/IBM/science-result-extractor (xsd:string)                   |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark NLP-TDMS (Exp, arXiv only) (<http://orkg.org/orkg/resource/R135204>) |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | has model (orkgp:HAS_MODEL)             | TDMS-IE (<http://orkg.org/orkg/resource/R128075>)                              |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | rdf:type                                | Contribution (orkgc:Contribution)                                              |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | rdfs:label                              | Contribution 	TDMS-IE (xsd:string)                                             |
| Contribution  TDMS-IE (<http://orkg.org/orkg/resource/R135203>) | rdf:type                                | orkgc:Resource                                                                 |

[Tool]
Got 3 rows and 3 columns:
| subject                                          | property   | object              |
| ------------------------------------------------ | ---------- | ------------------- |
| AxCell (<http://orkg.org/orkg/resource/R128071>) | rdf:type   | Model (orkgc:Model) |
| AxCell (<http://orkg.org/orkg/resource/R128071>) | rdfs:label | AxCell (xsd:string) |
| AxCell (<http://orkg.org/orkg/resource/R128071>) | rdf:type   | orkgc:Resource      |

[Tool]
Got 3 rows and 3 columns:
| subject                                           | property   | object               |
| ------------------------------------------------- | ---------- | -------------------- |
| TDMS-IE (<http://orkg.org/orkg/resource/R128075>) | rdf:type   | Model (orkgc:Model)  |
| TDMS-IE (<http://orkg.org/orkg/resource/R128075>) | rdfs:label | TDMS-IE (xsd:string) |
| TDMS-IE (<http://orkg.org/orkg/resource/R128075>) | rdf:type   | orkgc:Resource       |

[Assistant]
After a step-by-step exploration, I found that the models tested on the "NLP-TDMS (Exp, arXiv only)" benchmark dataset are linked via contributions, which in turn reference the benchmark and dataset. The two models identified are:

- AxCell (<http://orkg.org/orkg/resource/R128071>)
- TDMS-IE (<http://orkg.org/orkg/resource/R128075>)

I will now provide a SPARQL query that lists all models that have been tested on the "NLP-TDMS (Exp, arXiv only)" benchmark dataset, returning their IRIs and (optionally) labels.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs of the models tested on the specified benchmark dataset, and includes optional labels. The answer matches the query result and follows all the required rules. No further refinement is needed.