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 WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124999> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC <http://orkg.org/orkg/resource/R119875> ; orkgp:HAS_VALUE ?score .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark ; orkgp:HAS_MODEL ?model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel
  }
}
ORDER BY DESC ( xsd:float ( ?score ) )
LIMIT 1

The top performing model in terms of Score on the Atari 2600 Tutankham dataset is 'A2C + SIL'.

[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 name of the top performing model in terms of Score score when benchmarked on the Atari 2600 Tutankham dataset?

[Assistant]
Let's break down the question:

1. We are looking for the top performing model (i.e., the model with the highest Score score).
2. The benchmark is on the Atari 2600 Tutankham dataset.
3. We need to identify the relevant entities: the Atari 2600 Tutankham dataset, the Score metric, and models benchmarked on this dataset.
4. We need to find the property that links models to their benchmark results, specifically the Score score.

First, I will search for the "Atari 2600 Tutankham" dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>): has type Dataset
2. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>): has type Benchmark
3. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>): has type Benchmark
4. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133887>): has type Benchmark
5. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133771>): has type Benchmark
6. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133670>): has type Benchmark
7. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133569>): has type Benchmark
8. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>): has type Benchmark
9. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133376>): has type Benchmark
10. Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133356>): has type Benchmark

[Assistant]
I found several entities related to "Atari 2600 Tutankham," including one dataset entity and multiple benchmark entities. The most relevant for benchmarking is likely one of the "Benchmark Atari 2600 Tutankham" entities.

Next, I need to determine which of these benchmark entities is used for recording model performances, and how models, scores, and datasets are linked. I will list triples for the dataset entity to see how it is related to benchmarks and models.

[Tool]
Got 3 rows and 3 columns:
| subject                                                        | property   | object                            |
| -------------------------------------------------------------- | ---------- | --------------------------------- |
| Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>) | rdf:type   | Dataset (orkgc:Dataset)           |
| Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>) | rdfs:label | Atari 2600 Tutankham (xsd:string) |
| Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>) | rdf:type   | orkgc:Resource                    |

[Assistant]
The dataset entity "Atari 2600 Tutankham" is typed as a Dataset and Resource, but there are no direct links to benchmarks or models from this entity.

Since there are multiple "Benchmark Atari 2600 Tutankham" entities, I will check one of them (starting with the first: R132971) to see how it is structured and if it links to models and scores.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                          | object                                                           |
| ------------------------------------------------------------------------ | --------------------------------- | ---------------------------------------------------------------- |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>) | has dataset (orkgp:HAS_DATASET)   | Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>)   |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>) | rdfs:label                        | Benchmark Atari 2600 Tutankham (xsd:string)                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132971>) | rdf:type                          | orkgc:Resource                                                   |

[Assistant]
This benchmark entity (R132971) is linked to the Atari 2600 Tutankham dataset and has an evaluation (Evaluation Score_108.6, R132972).

To understand how the evaluation is structured (e.g., which model it refers to, what score, and which metric), I will list the triples for the evaluation entity R132972.

[Tool]
Got 5 rows and 3 columns:
| subject                                                          | property                  | object                                          |
| ---------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) | value (orkgp:HAS_VALUE)   | 108.6 (xsd:string)                              |
| Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) | rdfs:label                | Evaluation Score_108.6 (xsd:string)             |
| Evaluation Score_108.6 (<http://orkg.org/orkg/resource/R132972>) | rdf:type                  | orkgc:Resource                                  |

[Assistant]
The evaluation entity contains a value (108.6), a metric (Score), and is typed as an Evaluation. However, it does not directly link to a model.

To find how models are associated with evaluations or benchmarks, I will check the properties of the benchmark entity (R132971) to see if there is a property linking to a model.

[Tool]
Top 10 property alternatives:
1. has dataset (orkgp:HAS_DATASET)
2. evaluation (orkgp:HAS_EVALUATION)

No other items found

[Assistant]
The benchmark entity does not have a direct property linking to a model. Since the evaluation entity also does not link to a model, it is possible that the model is linked via another property or that the structure is different.

To investigate further, I will check if other benchmarks for "Atari 2600 Tutankham" (from the earlier list) have a different structure. I will list the triples for the next benchmark entity (R133999).

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                          | object                                                           |
| ------------------------------------------------------------------------ | --------------------------------- | ---------------------------------------------------------------- |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>) | has dataset (orkgp:HAS_DATASET)   | Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>)   |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>) | rdfs:label                        | Benchmark Atari 2600 Tutankham (xsd:string)                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133999>) | rdf:type                          | orkgc:Resource                                                   |

[Assistant] This benchmark entity (R133999) has a similar structure: it links to the dataset and an evaluation (R134000). I will check the evaluation entity R134000 to see if it contains a model reference.

[Tool]
Got 5 rows and 3 columns:
| subject                                                          | property                  | object                                          |
| ---------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) | value (orkgp:HAS_VALUE)   | 130.3 (xsd:string)                              |
| Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) | rdfs:label                | Evaluation Score_130.3 (xsd:string)             |
| Evaluation Score_130.3 (<http://orkg.org/orkg/resource/R134000>) | rdf:type                  | orkgc:Resource                                  |

[Assistant]
Again, the evaluation entity contains the value and metric, but no direct link to a model. Since the model is not linked from the benchmark or evaluation, perhaps the model is the subject of another triple that links it to the benchmark or evaluation.

I will search for entities of type "Model" that are related to the Atari 2600 Tutankham dataset or benchmarks. First, I will search for "Model" entities in ORKG.

[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] There are several entities of type "Model." To find which models are benchmarked on the Atari 2600 Tutankham dataset, I will search for properties relating models to benchmarks or datasets. I will start by searching for properties of one of the model entities (R50465) with the keyword "benchmark."

[Tool]
Top 10 property alternatives:
1. has content (orkgp:hasContent)

No other items found

[Assistant]
There is no direct property linking this model entity to a benchmark. Since the direct approach is not yielding results, I will try a different strategy:

I will search for all evaluations with the "Score" metric on the Atari 2600 Tutankham dataset, and then see if any of these evaluations are linked to a model via any property. If not, I will look for patterns in the data.

First, I will search for all evaluations with the "Score" metric.

[Tool]
Got 1,259 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property                  | object                                          |
| -------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Score_767 (<http://orkg.org/orkg/resource/R131173>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_959 (<http://orkg.org/orkg/resource/R131175>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_403 (<http://orkg.org/orkg/resource/R131177>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_538 (<http://orkg.org/orkg/resource/R131179>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_926 (<http://orkg.org/orkg/resource/R131181>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_929 (<http://orkg.org/orkg/resource/R131183>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_841 (<http://orkg.org/orkg/resource/R131185>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_299 (<http://orkg.org/orkg/resource/R131187>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_769 (<http://orkg.org/orkg/resource/R131189>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_518 (<http://orkg.org/orkg/resource/R131191>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| ...                                                            | ...                       | ...                                             |

[Assistant]
There are many evaluations with the "Score" metric. To narrow this down to only those related to the Atari 2600 Tutankham dataset, I need to find which of these evaluations are linked to benchmarks that use the Atari 2600 Tutankham dataset.

From earlier, I saw that benchmarks link to the dataset via orkgp:HAS_DATASET and to evaluations via orkgp:HAS_EVALUATION. Therefore, I can write a SPARQL query to find all evaluations with the Score metric, linked to benchmarks that use the Atari 2600 Tutankham dataset, and retrieve their values and any associated models.

I will also try to retrieve any model linked to the benchmark or evaluation, if such a link exists.

[Tool]
Got 10 rows and 4 columns:
| benchmark                                                                | evaluation                                                        | score               | model |
| ------------------------------------------------------------------------ | ----------------------------------------------------------------- | ------------------- | ----- |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | Evaluation Score_340.5 (<http://orkg.org/orkg/resource/R133468>)  | 340.5 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R131943>) | Evaluation Score_280.0 (<http://orkg.org/orkg/resource/R131944>)  | 280.0 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132349>) | Evaluation Score_245.9 (<http://orkg.org/orkg/resource/R132350>)  | 245.9 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133887>) | Evaluation Score_241.21 (<http://orkg.org/orkg/resource/R133888>) | 241.21 (xsd:string) |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132248>) | Evaluation Score_218.4 (<http://orkg.org/orkg/resource/R132249>)  | 218.4 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133271>) | Evaluation Score_214.8 (<http://orkg.org/orkg/resource/R133272>)  | 214.8 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132048>) | Evaluation Score_211.4 (<http://orkg.org/orkg/resource/R132049>)  | 211.4 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R132466>) | Evaluation Score_204.6 (<http://orkg.org/orkg/resource/R132467>)  | 204.6 (xsd:string)  |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133376>) | Evaluation Score_197 (<http://orkg.org/orkg/resource/R133377>)    | 197 (xsd:string)    |       |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133325>) | Evaluation Score_184 (<http://orkg.org/orkg/resource/R133326>)    | 184 (xsd:string)    |       |

[Assistant]
The query returned the top Score values for benchmarks on the Atari 2600 Tutankham dataset, but none of the results include a model (the ?model column is empty). This suggests that the model information is not directly linked via the properties I checked.

To find the model, I will check the properties of the top benchmark (R133467) to see if there is any property that might indicate the model used.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                          | object                                                           |
| ------------------------------------------------------------------------ | --------------------------------- | ---------------------------------------------------------------- |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_340.5 (<http://orkg.org/orkg/resource/R133468>) |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | has dataset (orkgp:HAS_DATASET)   | Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R124999>)   |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | rdfs:label                        | Benchmark Atari 2600 Tutankham (xsd:string)                      |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | rdf:type                          | orkgc:Resource                                                   |

[Assistant]
The benchmark entity does not have any property that directly links to a model. Since the evaluation entity also did not have a model property, perhaps the model is linked via another entity or through a different pattern.

I will now check if the evaluation entity (R133468) is referenced as an object in any triple, which might indicate a model or contribution linking to it.

[Tool]
Got 1 row and 3 columns:
| subject                                                                  | property                          | object                                                           |
| ------------------------------------------------------------------------ | --------------------------------- | ---------------------------------------------------------------- |
| Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_340.5 (<http://orkg.org/orkg/resource/R133468>) |

[Assistant]
The only entity referencing the evaluation is the benchmark itself, which we have already checked.

Since the model is not directly linked, it is possible that the benchmark or evaluation is part of a larger structure, such as a "contribution" or "paper," which might link to the model. I will search for entities that reference the benchmark (R133467) as an object, to see if there is a higher-level entity that might contain the model information.

[Tool]
Got 1 row and 3 columns:
| subject                                                           | property                            | object                                                                   |
| ----------------------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------ |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Tutankham (<http://orkg.org/orkg/resource/R133467>) |

[Assistant]
The benchmark is referenced by a "Contribution" entity: "Contribution  A2C + SIL" (R133404) via the property orkgp:HAS_BENCHMARK. This suggests that the model name may be "A2C + SIL," and it is associated with the top Score on the Atari 2600 Tutankham dataset.

To confirm, I will check the label of this contribution entity.

[Tool]
Got 57 rows and 3 columns, showing the first 10 rows below:
| subject                                                           | property                                | object                                                                        |
| ----------------------------------------------------------------- | --------------------------------------- | ----------------------------------------------------------------------------- |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | research problem (orkgp:P32)            | Atari Games (<http://orkg.org/orkg/resource/R124884>)                         |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/alhabk/SGEE--pytorch (xsd:string)                          |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R133405>)       |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has model (orkgp:HAS_MODEL)             | A2C + SIL (<http://orkg.org/orkg/resource/R124912>)                           |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | rdf:type                                | Contribution (orkgc:Contribution)                                             |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | rdfs:label                              | Contribution 	A2C + SIL (xsd:string)                                          |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/junhyukoh/self-imitation-learning (xsd:string)             |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/rwightman/pytorch-opensim-rl (xsd:string)                  |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Name This Game (<http://orkg.org/orkg/resource/R133407>) |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Gopher (<http://orkg.org/orkg/resource/R133409>)         |
| ...                                                               | ...                                     | ...                                                                           |

[Tool]
Got 3 rows and 3 columns:
| subject                                             | property   | object                 |
| --------------------------------------------------- | ---------- | ---------------------- |
| A2C + SIL (<http://orkg.org/orkg/resource/R124912>) | rdf:type   | Model (orkgc:Model)    |
| A2C + SIL (<http://orkg.org/orkg/resource/R124912>) | rdfs:label | A2C + SIL (xsd:string) |
| A2C + SIL (<http://orkg.org/orkg/resource/R124912>) | rdf:type   | orkgc:Resource         |

[Assistant]
The contribution "A2C + SIL" is linked to the model "A2C + SIL" (R124912), which is typed as a Model. This confirms that the top performing model in terms of Score on the Atari 2600 Tutankham dataset is "A2C + SIL."

I will now write a SPARQL query that returns the IRI of the top performing model (with the highest Score) on the Atari 2600 Tutankham dataset, along with its label if available.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies the top performing model by ordering the results in descending order of the Score metric and limiting the output to the top result. It returns the model's IRI and its label in an optional clause, which is appropriate. The answer matches the query result and the requirements of the question. No further refinement is needed.


Failed to generate SPARQL query

[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] Indicate the model that performed best in terms of BLEU score metric on the IWSLT2014 German-English benchmark dataset?

[Assistant]
Let's break down the question:

1. We need to find models evaluated on the IWSLT2014 German-English benchmark dataset.
2. Among those, we need to find their BLEU scores.
3. We need to identify which model performed best (i.e., had the highest BLEU score).

First, let's search for the "IWSLT2014 German-English" dataset entity in ORKG.
I will search for the entity "IWSLT2014 German-English" in the ORKG to identify its IRI and ensure we refer to the correct dataset in subsequent queries.


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 ?evaluation ?metric ?value ?model ?modelLabel WHERE {
  <http://orkg.org/orkg/resource/R129643> orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric ; orkgp:HAS_VALUE ?value .
  FILTER ( ?metric = <http://orkg.org/orkg/resource/R115359> ) OPTIONAL {
    ?evaluation orkgp:HAS_MODEL ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:hasModel ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P20072 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P1004 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P71209 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P43024 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P71176 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P59015 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P41907 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
  OPTIONAL {
    ?evaluation orkgp:P9018 ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel
    }
  }
}
ORDER BY DESC ( xsd:float ( ?value ) )
LIMIT 1

No model is specified in the ORKG data for the best Pearson Correlation result (0.848) on the MedSTS benchmark. The knowledge graph does not contain a model link for this evaluation.

[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] Indicate the model that performed best in terms of Pearson Correlation metric on the MedSTS benchmark dataset?

[Assistant]
Let's break down the question:

1. We are looking for a model.
2. The model should have been evaluated on the MedSTS benchmark dataset.
3. The metric of interest is Pearson Correlation.
4. We want the model that performed best (i.e., highest Pearson Correlation) on MedSTS.

Step 1: Search for the "MedSTS" benchmark dataset entity in ORKG.

Step 2: Search for the "Pearson Correlation" metric entity.

Step 3: Identify models evaluated on MedSTS with Pearson Correlation scores.

Step 4: Write a SPARQL query to find the model with the highest Pearson Correlation on MedSTS.

Let's start by searching for the "MedSTS" benchmark dataset entity.

[Tool]
Top 10 entity alternatives:
1. MedSTS (<http://orkg.org/orkg/resource/R121068>): has type Dataset
2. Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>): has type Benchmark
3. Benchmark MedSTS (<http://orkg.org/orkg/resource/R131734>): has type Benchmark
4. Benchmark MedSTS (<http://orkg.org/orkg/resource/R131739>): has type Benchmark
5. Benchmark MedSTS (<http://orkg.org/orkg/resource/R131744>): has type Benchmark
6. Benchmark MedSTS (<http://orkg.org/orkg/resource/R131749>): has type Benchmark

[Assistant]
I found several entities related to "MedSTS," including one dataset and multiple benchmarks. Since the question specifically refers to the "MedSTS benchmark dataset," I will focus on the first benchmark entity: Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>).

Next, I will search for the "Pearson Correlation" metric entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. pearson correlation (<http://orkg.org/orkg/resource/R282367>): has type Statistical Technique
2. Pearson Correlation (<http://orkg.org/orkg/resource/R115359>): has type Metric
3. pearson correlation (<http://orkg.org/orkg/resource/R199145>): has type hypothesis testing
4. Uncentered pearson correlation (<http://orkg.org/orkg/resource/R76206>)
5. Pearson Correlation Coefficient (<http://orkg.org/orkg/resource/R127376>): has type Metric
6. Pearson Correlation Coefficient (<http://orkg.org/orkg/resource/R236905>)
7. Pearson Uncentered Correlation (orkgp:P36015)
8. Evaluation Pearson Correlation_0.750 (<http://orkg.org/orkg/resource/R131735>): has type Evaluation
9. Evaluation Pearson Correlation_0.759 (<http://orkg.org/orkg/resource/R131745>): has type Evaluation
10. Evaluation Pearson Correlation_0.767 (<http://orkg.org/orkg/resource/R131740>): has type Evaluation

[Assistant]
There are several entities for "Pearson Correlation" and "Pearson Correlation Coefficient" as metrics. The most relevant seem to be:

- Pearson Correlation (<http://orkg.org/orkg/resource/R115359>)
- Pearson Correlation Coefficient (<http://orkg.org/orkg/resource/R127376>)

I will use both as possible metric entities in the query.

Next, I need to find models that have been evaluated on the MedSTS benchmark with a Pearson Correlation metric. To do this, I need to understand how evaluation results are represented in ORKG, specifically how models, benchmarks, and metrics are linked.

I will list some triples for the MedSTS benchmark entity (<http://orkg.org/orkg/resource/R129643>) to see how evaluations are structured.

[Tool]
Got 5 rows and 3 columns:
| subject                                                    | property                          | object                                                                         |
| ---------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------------ |
| Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) |
| Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>) | has dataset (orkgp:HAS_DATASET)   | MedSTS (<http://orkg.org/orkg/resource/R121068>)                               |
| Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                    |
| Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>) | rdfs:label                        | Benchmark MedSTS (xsd:string)                                                  |
| Benchmark MedSTS (<http://orkg.org/orkg/resource/R129643>) | rdf:type                          | orkgc:Resource                                                                 |

[Assistant]
The MedSTS benchmark entity (<http://orkg.org/orkg/resource/R129643>) has an evaluation linked via the property `orkgp:HAS_EVALUATION` to "Evaluation Pearson Correlation_0.848" (<http://orkg.org/orkg/resource/R129644>).

To proceed, I need to see how this evaluation entity is structured—specifically, how it links to the model and the metric value. I will list triples for the evaluation entity (<http://orkg.org/orkg/resource/R129644>).

[Tool]
Got 5 rows and 3 columns:
| subject                                                                        | property                  | object                                                        |
| ------------------------------------------------------------------------------ | ------------------------- | ------------------------------------------------------------- |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | value (orkgp:HAS_VALUE)   | 0.848 (xsd:string)                                            |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | metric (orkgp:HAS_METRIC) | Pearson Correlation (<http://orkg.org/orkg/resource/R115359>) |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdf:type                  | Evaluation (orkgc:Evaluation)                                 |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdfs:label                | Evaluation Pearson Correlation_0.848 (xsd:string)             |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdf:type                  | orkgc:Resource                                                |

[Assistant]
The evaluation entity (<http://orkg.org/orkg/resource/R129644>) has:

- A value (orkgp:HAS_VALUE): 0.848
- A metric (orkgp:HAS_METRIC): Pearson Correlation (<http://orkg.org/orkg/resource/R115359>)

However, there is no direct link to a model in the triples listed. To find out how the model is linked, I will check if there are any other properties for this evaluation entity that might point to a model.

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

No other items found

[Assistant]
There are no properties directly linking the evaluation entity to a model using the keyword "model." It's possible that the model is linked at a different level, perhaps through the benchmark or another related entity.

To investigate further, I will check if the MedSTS benchmark entity (<http://orkg.org/orkg/resource/R129643>) has any properties related to "model."

[Tool]
Top 10 property alternatives:
1. has dataset (orkgp:HAS_DATASET)
2. evaluation (orkgp:HAS_EVALUATION)

No other items found

[Assistant]
There are no properties directly linking the MedSTS benchmark entity to a model either. Since the evaluation entity does not directly reference a model, it's possible that the model information is encoded in the label or is linked through another related entity.

Given that the evaluation entity is named "Evaluation Pearson Correlation_0.848," I will search for other evaluation entities with similar naming patterns to see if any of them have more explicit links to models or if the model name is embedded in the label.

I will search for entities with "Evaluation Pearson Correlation" in their label.

[Tool]
Top 10 entity alternatives:
1. Evaluation Pearson Correlation_0.750 (<http://orkg.org/orkg/resource/R131735>): has type Evaluation
2. Evaluation Pearson Correlation_0.759 (<http://orkg.org/orkg/resource/R131745>): has type Evaluation
3. Evaluation Pearson Correlation_0.767 (<http://orkg.org/orkg/resource/R131740>): has type Evaluation
4. Evaluation Pearson Correlation_0.795 (<http://orkg.org/orkg/resource/R131738>): has type Evaluation
5. Evaluation Pearson Correlation_0.817 (<http://orkg.org/orkg/resource/R131733>): has type Evaluation
6. Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>): has type Evaluation
7. Evaluation Pearson Correlation_0.9159999999999999 (<http://orkg.org/orkg/resource/R129646>): has type Evaluation
8. Evaluation Pearson Correlation_0.925 (<http://orkg.org/orkg/resource/R130182>): has type Evaluation
9. Evaluation Pearson Correlation_0.714 (<http://orkg.org/orkg/resource/R131750>): has type Evaluation
10. Evaluation Pearson Correlation_0.345 (<http://orkg.org/orkg/resource/R131748>): has type Evaluation

[Assistant]
There are several evaluation entities with Pearson Correlation values in their labels, such as:

- Evaluation Pearson Correlation_0.925 (<http://orkg.org/orkg/resource/R130182>)
- Evaluation Pearson Correlation_0.9159999999999999 (<http://orkg.org/orkg/resource/R129646>)
- Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>)
- etc.

To find out which of these are associated with the MedSTS benchmark, I will check which of these evaluation entities are linked to the MedSTS benchmark entity (<http://orkg.org/orkg/resource/R129643>) via the property `orkgp:HAS_EVALUATION`.

Alternatively, I can write a SPARQL query to find all evaluations linked to the MedSTS benchmark, their Pearson Correlation values, and see if any model information is available.

Let's write a SPARQL query to get all evaluations linked to the MedSTS benchmark, their metric, value, and any possible model information.

[Tool]
Got 1 row and 5 columns:
| evaluation                                                                     | metric                                                        | value              | model | modelLabel |
| ------------------------------------------------------------------------------ | ------------------------------------------------------------- | ------------------ | ----- | ---------- |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | Pearson Correlation (<http://orkg.org/orkg/resource/R115359>) | 0.848 (xsd:string) |       |            |

[Assistant]
The only evaluation linked to the MedSTS benchmark (<http://orkg.org/orkg/resource/R129643>) with the Pearson Correlation metric is "Evaluation Pearson Correlation_0.848" (<http://orkg.org/orkg/resource/R129644>), with a value of 0.848. However, there is no explicit model linked via a property such as `orkgp:HAS_MODEL`.

Given this, it appears that the ORKG data for the MedSTS benchmark only records a single Pearson Correlation evaluation, and does not specify the model responsible for this result.

Therefore, based on the available data, the best-performing model in terms of Pearson Correlation on the MedSTS benchmark cannot be determined from the ORKG knowledge graph, as the model information is missing.

I will now provide this as the answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves the highest Pearson Correlation value for the MedSTS benchmark, but it does not return the model responsible for this result, which is the main focus of the user's question. The query should be refined to also retrieve the model associated with the evaluation. This likely involves following a property from the evaluation to the model (e.g., orkgp:HAS_MODEL or similar). The model's IRI should be included in the SELECT clause, and its label can be optionally retrieved. Please refine the query to include the model information.

[Assistant]
Thank you for the feedback. You are correct: the main focus is to identify the model responsible for the best Pearson Correlation result on the MedSTS benchmark. Although my previous exploration did not find a model linked via orkgp:HAS_MODEL for the specific evaluation, it is possible that other evaluations or other properties might be used to link models.

Step-by-step plan:
1. Search for all evaluations linked to the MedSTS benchmark.
2. For each evaluation, try to retrieve the associated model using possible properties (e.g., orkgp:HAS_MODEL, orkgp:MODEL, or similar).
3. Retrieve the Pearson Correlation value, the model IRI, and optionally the model label.
4. Sort by value and return the top result.

First, I will search for properties related to "model" that might be used with evaluation entities in ORKG.

[Tool]
No property items found

No other items found

[Assistant]
No properties explicitly related to "model" are directly associated with the Evaluation class in ORKG. However, it's possible that the property linking models to evaluations uses a different naming convention or is not directly attached to the Evaluation class.

To be thorough, I will list all properties used by one of the evaluation entities (e.g., <http://orkg.org/orkg/resource/R129644>) to see if any property could plausibly link to a model.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                        | property                  | object                                                        |
| ------------------------------------------------------------------------------ | ------------------------- | ------------------------------------------------------------- |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | value (orkgp:HAS_VALUE)   | 0.848 (xsd:string)                                            |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | metric (orkgp:HAS_METRIC) | Pearson Correlation (<http://orkg.org/orkg/resource/R115359>) |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdf:type                  | Evaluation (orkgc:Evaluation)                                 |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdfs:label                | Evaluation Pearson Correlation_0.848 (xsd:string)             |
| Evaluation Pearson Correlation_0.848 (<http://orkg.org/orkg/resource/R129644>) | rdf:type                  | orkgc:Resource                                                |

[Assistant]
The evaluation entity (<http://orkg.org/orkg/resource/R129644>) does not have any property linking it to a model. Since this is the only evaluation linked to the MedSTS benchmark, and since the property search for "model" on the Evaluation class returned nothing, it appears that the model information is not present in the ORKG for this evaluation.

To be thorough, I will check if any other evaluations in ORKG use a property to link to a model by searching for properties containing "model" in their name.

[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)

[Assistant]
There are several properties related to "model," including:

- model (orkgp:P20072)
- model (orkgp:P1004)
- models (orkgp:P71209)
- has model (orkgp:HAS_MODEL)
- has model (orkgp:hasModel)
- Has model (orkgp:P9018)

To maximize coverage, I will refine the SPARQL query to attempt to retrieve a model using any of these properties as an OPTIONAL clause. I will also ensure the query returns the model IRI and its label if available.

Here is the refined SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is comprehensive in its attempt to retrieve the model associated with the best Pearson Correlation on the MedSTS benchmark, checking many plausible properties for a model link. However, the result does not return any model (the model and modelLabel columns are empty), which suggests that either the data does not contain a model for this evaluation, or the relevant property is missing from the query.

To further refine, consider:
- Double-checking if there are additional or alternative properties that might link a model to the evaluation in the ORKG schema.
- If the data genuinely lacks a model link, the answer should explicitly state that no model is specified for the best Pearson Correlation result, rather than implying the query is incomplete.

Otherwise, the query structure and adherence to SPARQL best practices are correct.

[Tool] Stopped generation process


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R209701> .
  ?benchmark orkgp:HAS_EVALUATION ?quantity .
  {
    ?quantity orkgp:P45074 ?metric .
  } UNION {
    ?quantity orkgp:P2006 ?metric .
  } UNION {
    ?quantity orkgp:HAS_METRIC ?metric .
  } UNION {
    ?quantity orkgp:P24010 ?metric .
  } UNION {
    ?quantity orkgp:P44022 ?metric .
  } UNION {
    ?quantity orkgp:P57078 ?metric .
  } UNION {
    ?quantity orkgp:P7069 ?metric .
  } UNION {
    ?quantity orkgp:P43238 ?metric .
  } UNION {
    ?quantity orkgp:P36033 ?metric .
  } UNION {
    ?quantity orkgp:P15511 ?metric .
  } UNION {
    ?quantity orkgp:P4006 ?metric .
  }
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

This query retrieves all evaluation metrics (with their IRIs and optional labels) used in benchmarks that include the DRI Corpus dataset, regardless of which property is used to link the metric. This ensures completeness and covers all possible schema variations in the ORKG knowledge graph.

[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 evaluation metrics are commonly used when benchmarking models on the DRI Corpus dataset?

[Assistant]
Let's break down the question:

1. We need to identify the "DRI Corpus dataset" in the ORKG knowledge graph.
2. We need to find which evaluation metrics are used when benchmarking models on this dataset.

Step 1: Search for the "DRI Corpus" entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. DRI Corpus (<http://orkg.org/orkg/resource/R209701>): has type Benchmark Dataset, has type Contribution, has type Dataset
2. DRI Corpus (<http://orkg.org/orkg/resource/R114640>): has type Dataset
3. Corpus (<http://orkg.org/orkg/resource/R166530>): has type Contribution, has type Dataset
4. NNE Corpus (<http://orkg.org/orkg/resource/R211526>): has type Contribution, has type Dataset
5. SoMeSci Corpus (<http://orkg.org/orkg/resource/R166456>): has type Contribution, has type Dataset
6. AAN Corpus (<http://orkg.org/orkg/resource/R147659>): has type Benchmark Dataset, has type Contribution, has type Dataset
7. HiNER corpus (<http://orkg.org/orkg/resource/R213188>): has type Contribution, has type Dataset
8. GPRO corpus (<http://orkg.org/orkg/resource/R171966>): has type Contribution, has type Dataset
9. NEMO corpus (<http://orkg.org/orkg/resource/R211924>): has type Contribution, has type Dataset
10. SpanishADR corpus (<http://orkg.org/orkg/resource/R214111>): has type Contribution, has type Dataset

[Assistant]
There are two entities named "DRI Corpus" in ORKG:
- DRI Corpus (<http://orkg.org/orkg/resource/R209701>): has type Benchmark Dataset, Contribution, Dataset
- DRI Corpus (<http://orkg.org/orkg/resource/R114640>): has type Dataset

The first one (R209701) is more specific, being a Benchmark Dataset. I will proceed with this entity.

Next, I need to find which evaluation metrics are used when benchmarking models on this dataset. This typically involves looking for properties or relations connecting the DRI Corpus to evaluation metrics, possibly via "benchmark", "evaluation", or "metric" properties.

Step 2: Search for properties related to "evaluation metric" for the DRI Corpus (R209701).

[Tool]
Top 10 property alternatives:
1. assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>)
2. research problem (orkgp:P32)
3. Annotation level (orkgp:P65000)
4. Has-statistics (orkgp:P71116)
5. model (orkgp:P1004)
6. Concept types (orkgp:P34062)
7. url (<http://orkg.org/orkg/predicate/SCHEMAORG:url>)
8. Data coverage (orkgp:P41872)
9. inter-annotator agreement (orkgp:P44159)
10. curated (orkgp:P67000)

No other items found

[Assistant]
The search did not return a direct "evaluation metric" property, but it did return some potentially relevant properties:
- assesses
- Has-statistics
- model
- inter-annotator agreement

"Has-statistics" and "assesses" could be relevant for evaluation metrics. Let's check what triples exist for the DRI Corpus with these properties, starting with "Has-statistics".

[Tool]
Got 1 row and 3 columns:
| subject                                              | property                      | object                                               |
| ---------------------------------------------------- | ----------------------------- | ---------------------------------------------------- |
| DRI Corpus (<http://orkg.org/orkg/resource/R209701>) | Has-statistics (orkgp:P71116) | stats-node (<http://orkg.org/orkg/resource/R220298>) |

[Assistant]
The DRI Corpus (R209701) has a "Has-statistics" property pointing to a node (R220298). This node likely contains information about evaluation metrics or results.

Next, let's explore what properties are associated with this stats-node (R220298) to see if it mentions any evaluation metrics.

[Tool]
Got 5 rows and 3 columns:
| subject                                              | property                          | object                  |
| ---------------------------------------------------- | --------------------------------- | ----------------------- |
| stats-node (<http://orkg.org/orkg/resource/R220298>) | number of papers (orkgp:P34059)   | 40 (xsd:string)         |
| stats-node (<http://orkg.org/orkg/resource/R220298>) | Number of concepts (orkgp:P34060) | 5 (xsd:string)          |
| stats-node (<http://orkg.org/orkg/resource/R220298>) | rdf:type                          | R220250 (orkgc:C40016)  |
| stats-node (<http://orkg.org/orkg/resource/R220298>) | rdfs:label                        | stats-node (xsd:string) |
| stats-node (<http://orkg.org/orkg/resource/R220298>) | rdf:type                          | orkgc:Resource          |

[Assistant]
The stats-node (R220298) does not directly mention any evaluation metrics; it only contains some statistics about the dataset (number of papers, number of concepts).

Since "Has-statistics" did not yield evaluation metrics, let's try the "assesses" property next, as it might connect the dataset to tasks or metrics.

I'll list triples where DRI Corpus (R209701) is the subject and the property is "assesses".

[Tool]
Got 1 row and 3 columns:
| subject                                              | property                                                       | object                                             |
| ---------------------------------------------------- | -------------------------------------------------------------- | -------------------------------------------------- |
| DRI Corpus (<http://orkg.org/orkg/resource/R209701>) | assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>) | Classifying sentences into categories (xsd:string) |

[Assistant]
The "assesses" property indicates that the DRI Corpus is used for "Classifying sentences into categories," but it does not directly mention evaluation metrics.

Since neither "Has-statistics" nor "assesses" led to evaluation metrics, let's try a broader approach: search for properties related to "metric" for the DRI Corpus entity.

[Tool]
Top 10 property alternatives:
1. model (orkgp:P1004)
2. url (<http://orkg.org/orkg/predicate/SCHEMAORG:url>)
3. name (<http://orkg.org/orkg/predicate/SCHEMAORG:name>)
4. inLanguage (<http://orkg.org/orkg/predicate/SCHEMAORG:inLanguage>)
5. description (<http://orkg.org/orkg/predicate/SCHEMAORG:description>)
6. Concept types (orkgp:P34062)
7. research problem (orkgp:P32)
8. alternateName (<http://orkg.org/orkg/predicate/SCHEMAORG:alternateName>)
9. assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>)
10. curated (orkgp:P67000)

No other items found

[Assistant]
There is no direct property related to "metric" for the DRI Corpus entity.

Since evaluation metrics are often associated with models or papers that use the dataset, let's check if there are any models (via the "model" property) linked to DRI Corpus, and then see if those models have associated evaluation metrics.

First, let's list the objects of the "model" property for DRI Corpus (R209701).

[Tool]
Got 1 row and 3 columns:
| subject                                              | property            | object                                               |
| ---------------------------------------------------- | ------------------- | ---------------------------------------------------- |
| DRI Corpus (<http://orkg.org/orkg/resource/R209701>) | model (orkgp:P1004) | Linear SVM (<http://orkg.org/orkg/resource/R123041>) |

[Assistant]
The DRI Corpus is linked to the "Linear SVM" model (R123041) via the "model" property. Evaluation metrics are likely associated with this model when applied to the DRI Corpus.

Next, let's search for properties related to "evaluation metric" for the Linear SVM model (R123041).

[Tool]
No property items found

No other items found

[Assistant]
There is no direct "evaluation metric" property for the Linear SVM model either. However, evaluation metrics might be listed under more general properties like "metric", "performance", or "score".

Let's try searching for properties related to "metric" for the Linear SVM model (R123041).

[Tool]
No property items found

No other items found

[Assistant]
No direct "metric" property is found for the Linear SVM model either. Since direct links are not available, evaluation metrics might be represented as part of a contribution or result node associated with the model or dataset.

Let's try a broader approach: search for entities related to "evaluation metric" in ORKG, and see if any are linked to DRI Corpus or Linear SVM.

[Tool]
Top 10 entity alternatives:
1. Evaluation metric (<http://orkg.org/orkg/resource/R147048>): has type Contribution Template
2. Evaluation Metric (<http://orkg.org/orkg/resource/R8420>)
3. Evaluation Metric (<http://orkg.org/orkg/resource/R145782>): Metric measure or term that can express quality of a system/meth...mean-squared error, robustness, compile time, time complexity...
4. Evaluation metric (orkgc:C20129)
5. Evaluation metrics (orkgp:P41532): Relates the contribution to the used evaluation metrics to assess the contribution. (e.g.  Precision, Recall, Hits@N, AUC etc.)
6. Evaluation metrics (<http://orkg.org/orkg/resource/R218467>)
7. example-based evaluation metrics (<http://orkg.org/orkg/resource/R217073>)
8. Has evaluation metrics (orkgp:P35217): Relates the contribution to the used evaluation metrics to assess the contribution. (e.g.  Precision, Recall, Hits@N, AUC etc.)
9. Automatic Evaluation Metrics (orkgp:P37517)
10. label-based evaluation metrics (<http://orkg.org/orkg/resource/R217074>)

[Assistant]
There are several entities and properties related to "evaluation metric" in ORKG. Of particular interest are:
- Evaluation metrics (orkgp:P41532): property relating contributions to evaluation metrics (e.g., Precision, Recall, etc.)
- Has evaluation metrics (orkgp:P35217): similar property
- Automatic Evaluation Metrics (orkgp:P37517): property

Let's check if the DRI Corpus (R209701) or the Linear SVM model (R123041) are linked via any of these properties. I'll start by listing triples where DRI Corpus is the subject and the property is "Evaluation metrics" (orkgp:P41532).

[Tool] Got no rows and 3 columns

[Assistant] There are no direct links from DRI Corpus to evaluation metrics using the "Evaluation metrics" property. Let's try the same for the Linear SVM model (R123041).

[Tool] Got no rows and 3 columns

[Assistant]
There are no direct links from the Linear SVM model to evaluation metrics using the "Evaluation metrics" property either.

Since direct links are not found, evaluation metrics might be associated with contributions (e.g., papers or experiments) that use both the DRI Corpus and a model. Let's search for contributions that mention both the DRI Corpus and a model, and see if they specify evaluation metrics.

First, let's search for contributions (papers/experiments) that mention the DRI Corpus.

[Tool]
Top 10 entity alternatives:
1. DRI Corpus (<http://orkg.org/orkg/resource/R209701>): has type Benchmark Dataset, has type Contribution, has type Dataset
2. DRI Corpus (<http://orkg.org/orkg/resource/R114640>): has type Dataset
3. Corpus (<http://orkg.org/orkg/resource/R166530>): has type Contribution, has type Dataset
4. NNE Corpus (<http://orkg.org/orkg/resource/R211526>): has type Contribution, has type Dataset
5. SoMeSci Corpus (<http://orkg.org/orkg/resource/R166456>): has type Contribution, has type Dataset
6. AAN Corpus (<http://orkg.org/orkg/resource/R147659>): has type Benchmark Dataset, has type Contribution, has type Dataset
7. HiNER corpus (<http://orkg.org/orkg/resource/R213188>): has type Contribution, has type Dataset
8. GPRO corpus (<http://orkg.org/orkg/resource/R171966>): has type Contribution, has type Dataset
9. NEMO corpus (<http://orkg.org/orkg/resource/R211924>): has type Contribution, has type Dataset
10. SpanishADR corpus (<http://orkg.org/orkg/resource/R214111>): has type Contribution, has type Dataset

[Tool] Got no rows and 3 columns

[Assistant]
It appears there are no direct triples using the "instance of" property to list contributions. Instead, let's try searching for contributions that use the DRI Corpus as a dataset. This is often represented by a property like "uses dataset" or similar.

I'll search for properties related to "dataset" for the DRI Corpus entity to see if any contributions are linked this way.

[Tool]
Top 10 property alternatives:
1. Data coverage (orkgp:P41872)
2. Data domains (orkgp:P41878)
3. research problem (orkgp:P32)
4. curated (orkgp:P67000)
5. Has-statistics (orkgp:P71116)
6. model (orkgp:P1004)
7. Concept types (orkgp:P34062)
8. description (<http://orkg.org/orkg/predicate/SCHEMAORG:description>)
9. url (<http://orkg.org/orkg/predicate/SCHEMAORG:url>)
10. inter-annotator agreement (orkgp:P44159)

No other items found

[Assistant]
No property directly links contributions to the DRI Corpus as a dataset. Since the DRI Corpus is itself typed as a "Contribution," perhaps evaluation metrics are attached to it directly.

Given the lack of direct links, let's try a different approach: search for entities in ORKG that are evaluation metrics commonly used for classification tasks (since DRI Corpus is used for "Classifying sentences into categories"), such as "accuracy," "precision," "recall," and "f1-score." Then, check if these metrics are linked to DRI Corpus or its associated models.

Let's start by searching for the entity "accuracy" in ORKG.

[Tool]
Top 10 entity alternatives:
1. accuracy (<http://orkg.org/orkg/resource/R211293>): has type Metric
2. accuracy (<http://orkg.org/orkg/resource/R227989>): has type Contribution Template
3. accuracy (<http://orkg.org/orkg/resource/wikidata:Q1298969>): has type External
4. Accuracy (<http://orkg.org/orkg/resource/R163734>)
5. Accuracy (<http://orkg.org/orkg/resource/R194862>)
6. Accuracy (<http://orkg.org/orkg/resource/R195391>)
7. Accuracy (<http://orkg.org/orkg/resource/R195548>)
8. Accuracy (<http://orkg.org/orkg/resource/R142103>): the overall percentage of correctly\nclassified examples
9. Accuracy (<http://orkg.org/orkg/resource/R196710>): has type Metric
10. Accuracy (<http://orkg.org/orkg/resource/R225345>)

[Assistant]
There are several entities for "accuracy" as a metric in ORKG, such as:
- accuracy (<http://orkg.org/orkg/resource/R211293>): has type Metric
- Accuracy (<http://orkg.org/orkg/resource/R196710>): has type Metric

Let's check if any contributions or models using the DRI Corpus reference these accuracy metric entities. I'll search for triples where the object is "accuracy" (R211293) and see if the subject is related to DRI Corpus or its models.

[Tool]
Got 2 rows and 3 columns:
| subject                                                    | property             | object                                             |
| ---------------------------------------------------------- | -------------------- | -------------------------------------------------- |
| machine learning (<http://orkg.org/orkg/resource/R211292>) | metric (orkgp:P2006) | accuracy (<http://orkg.org/orkg/resource/R211293>) |
| descriptive (<http://orkg.org/orkg/resource/R211335>)      | metric (orkgp:P2006) | accuracy (<http://orkg.org/orkg/resource/R211293>) |

[Assistant]
The "accuracy" metric is linked to "machine learning" and "descriptive" entities, but not directly to DRI Corpus or its models.

Given the structure of the ORKG data, it seems that evaluation metrics are not directly linked to the DRI Corpus dataset or its models in a straightforward way. However, based on the information retrieved:
- The DRI Corpus is used for classification tasks.
- The only model explicitly linked is Linear SVM.
- The most common evaluation metrics for classification tasks (and likely for DRI Corpus) are accuracy, precision, recall, and F1-score.

To provide a comprehensive answer, I will write a SPARQL query that retrieves all metrics (entities of type Metric) that are used in the context of the DRI Corpus dataset, either directly or via associated models or contributions, if such links exist.

Here is the query:

```sparql
SELECT DISTINCT ?metric ?metricLabel WHERE {
  {
    # Metrics directly linked to DRI Corpus
    <http://orkg.org/orkg/resource/R209701> ?p ?metric .
    ?metric a <http://orkg.org/orkg/class/Metric> .
    OPTIONAL { ?metric rdfs:label ?metricLabel }
  }
  UNION
  {
    # Metrics linked via models
    <http://orkg.org/orkg/resource/R209701> orkgp:P1004 ?model .
    ?model ?p2 ?metric .
    ?metric a <http://orkg.org/orkg/class/Metric> .
    OPTIONAL { ?metric rdfs:label ?metricLabel }
  }
  UNION
  {
    # Metrics linked via contributions mentioning DRI Corpus
    ?contrib ?p3 <http://orkg.org/orkg/resource/R209701> .
    ?contrib ?p4 ?metric .
    ?metric a <http://orkg.org/orkg/class/Metric> .
    OPTIONAL { ?metric rdfs:label ?metricLabel }
  }
}
```

This query will return all metrics (with their IRIs and optional labels) that are associated with the DRI Corpus dataset, either directly, via models, or via contributions.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is well-structured and attempts to cover multiple ways metrics could be associated with the DRI Corpus dataset (directly, via models, or via contributions). However, the query returned no results, which suggests that either the associations are modeled differently in the ORKG, or the dataset/resource IRIs or property IRIs used are incorrect or incomplete.

Suggestions for refinement:
1. Double-check that <http://orkg.org/orkg/resource/R209701> is indeed the correct IRI for the DRI Corpus dataset in the ORKG.
2. Consider expanding the search to include more properties that could link the dataset to models or metrics (e.g., look for properties like 'evaluated on', 'uses dataset', 'has metric', etc.).
3. Instead of only looking for direct links, consider traversing more steps (e.g., dataset -> model -> evaluation -> metric) if the ORKG schema supports such relations.
4. Check if metrics are linked via other intermediate entities such as 'Evaluation', 'Benchmark', or 'Experiment'.

In summary, the query logic is sensible, but it needs refinement to better match the actual structure of the ORKG data.

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

**Step 1: Double-check the IRI for DRI Corpus**
From the previous search, <http://orkg.org/orkg/resource/R209701> is indeed labeled "DRI Corpus" and typed as Benchmark Dataset, Contribution, and Dataset. This seems correct.

**Step 2: Expand search for linking properties**
I will search for properties in ORKG that could plausibly link datasets to models, evaluations, or metrics, such as "evaluated on", "uses dataset", "has metric", "benchmark", "experiment", or "evaluation".

Let’s start by searching for properties related to "evaluation", "experiment", and "benchmark".

[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)

[Assistant]
There are several promising properties:
- evaluation (orkgp:P5091, orkgp:P34, orkgp:HAS_EVALUATION)
- Evaluated on (orkgp:P41838)
- Means of evaluation (orkgp:P36082)
- Evaluation method (orkgp:P59089)
- Evaluation results (orkgp:P25064)

Let’s check if any of these properties link the DRI Corpus (R209701) to other entities. I’ll start with "Evaluated on" (orkgp:P41838).

[Tool] Got no rows and 3 columns

[Assistant] No entities are linked to DRI Corpus via "Evaluated on". Let's try the "evaluation" properties next, starting with orkgp:P5091.

[Tool] Got no rows and 3 columns

[Assistant] No entities are linked to DRI Corpus via "evaluation" (orkgp:P5091). Let's try "evaluation" (orkgp:P34) next.

[Tool] Got no rows and 3 columns

[Assistant] No entities are linked to DRI Corpus via "evaluation" (orkgp:P34). Let's try "evaluation" (orkgp:HAS_EVALUATION) next.

[Tool] Got no rows and 3 columns

[Assistant] No entities are linked to DRI Corpus via "HAS_EVALUATION". Let's try "Evaluation results" (orkgp:P25064) as a last attempt among the evaluation-related properties.

[Tool] Got no rows and 3 columns

[Assistant]
None of the evaluation-related properties directly link to the DRI Corpus.

**Step 3: Try traversing via models or contributions**
Since we previously found that DRI Corpus is linked to the Linear SVM model, perhaps the evaluation metrics are attached to the model or to an intermediate "experiment" or "contribution" entity.

Let’s search for entities of type "Experiment" or "Benchmark" that mention DRI Corpus. First, I’ll search for the class "Experiment" in ORKG.

[Tool]
Top 10 entity alternatives:
1. Experiment (<http://orkg.org/orkg/resource/R74694>)
2. Experiment (<http://orkg.org/orkg/resource/R74758>): has type Contribution Template
3. Experiment (<http://orkg.org/orkg/resource/R74598>)
4. experiment (<http://orkg.org/orkg/resource/R281198>): has type R186737
5. experiment (<http://orkg.org/orkg/resource/R280778>): has type R186737
6. experiment (<http://orkg.org/orkg/resource/R280770>): has type R186737
7. experiment (<http://orkg.org/orkg/resource/R280742>): has type R186737
8. experiment (<http://orkg.org/orkg/resource/R280516>): has type R186737
9. experiment (<http://orkg.org/orkg/resource/R280506>): has type R186737
10. experiment (<http://orkg.org/orkg/resource/R280505>): has type R186737

[Assistant]
There are several entities labeled "Experiment" in ORKG, such as <http://orkg.org/orkg/resource/R74694> and <http://orkg.org/orkg/resource/R74598>.

Let's see if any experiments are linked to the DRI Corpus (R209701) as a dataset. I'll look for triples where DRI Corpus is the object and the subject is an experiment.

[Tool]
Got 6 rows and 3 columns:
| subject                                                                                | property                                         | object                                               |
| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------------------------------- |
| Dr. Inventor Framework: Extracti...ion from Scientific Publications (<http://orkg.org/orkg/resource/R209698>) | contribution (orkgp:P31)                         | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Datasets for NLP tasks on scholarly articles (<http://orkg.org/orkg/resource/R235595>)                        | compare contribution (orkgp:compareContribution) | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Benchmark (<http://orkg.org/orkg/resource/R227574>)                                                           | has dataset (orkgp:HAS_DATASET)                  | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Datasets for NLP tasks on scholarly articles (<http://orkg.org/orkg/resource/R280105>)                        | compare contribution (orkgp:compareContribution) | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Datasets for NLP tasks on scholarly articles (<http://orkg.org/orkg/resource/R280106>)                        | compare contribution (orkgp:compareContribution) | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Datasets for NLP tasks on scholarly articles (<http://orkg.org/orkg/resource/R280270>)                        | compare contribution (orkgp:compareContribution) | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |

[Assistant]
We found several entities linked to DRI Corpus, including:
- Dr. Inventor Framework: Extraction from Scientific Publications (R209698) via "contribution"
- Benchmark (R227574) via "has dataset"
- Several "Datasets for NLP tasks on scholarly articles" via "compare contribution"

The "Benchmark" entity (R227574) with "has dataset" is promising. Let's see what properties are associated with this Benchmark entity, especially those that might link to evaluation metrics.

[Tool]
Got 5 rows and 3 columns:
| subject                                             | property                          | object                                               |
| --------------------------------------------------- | --------------------------------- | ---------------------------------------------------- |
| Benchmark (<http://orkg.org/orkg/resource/R227574>) | evaluation (orkgp:HAS_EVALUATION) | Quantity (<http://orkg.org/orkg/resource/R227575>)   |
| Benchmark (<http://orkg.org/orkg/resource/R227574>) | has dataset (orkgp:HAS_DATASET)   | DRI Corpus (<http://orkg.org/orkg/resource/R209701>) |
| Benchmark (<http://orkg.org/orkg/resource/R227574>) | rdf:type                          | Benchmark (orkgc:Benchmark)                          |
| Benchmark (<http://orkg.org/orkg/resource/R227574>) | rdfs:label                        | Benchmark (xsd:string)                               |
| Benchmark (<http://orkg.org/orkg/resource/R227574>) | rdf:type                          | orkgc:Resource                                       |

[Assistant]
The Benchmark entity (R227574) has an "evaluation" (orkgp:HAS_EVALUATION) property pointing to Quantity (R227575). This Quantity node may represent an evaluation result or metric.

Let's list the properties of Quantity (R227575) to see if it specifies any evaluation metric.

[Tool]
Got 5 rows and 3 columns:
| subject                                            | property                            | object                                                   |
| -------------------------------------------------- | ----------------------------------- | -------------------------------------------------------- |
| Quantity (<http://orkg.org/orkg/resource/R227575>) | qudt:hasQuantityKind (orkgp:P45074) | F1 score (<http://orkg.org/orkg/resource/R211540>)       |
| Quantity (<http://orkg.org/orkg/resource/R227575>) | qudt:quantityValue (orkgp:P45073)   | Quantity Value (<http://orkg.org/orkg/resource/R227576>) |
| Quantity (<http://orkg.org/orkg/resource/R227575>) | rdf:type                            | qudt:Quantity (orkgc:C23007)                             |
| Quantity (<http://orkg.org/orkg/resource/R227575>) | rdfs:label                          | Quantity (xsd:string)                                    |
| Quantity (<http://orkg.org/orkg/resource/R227575>) | rdf:type                            | orkgc:Resource                                           |

[Assistant]
The Quantity node (R227575) is linked to "F1 score" (R211540) via the property qudt:hasQuantityKind (orkgp:P45074). This means that F1 score is used as an evaluation metric in the context of benchmarking on the DRI Corpus.

To generalize, let's write a SPARQL query that retrieves all metrics (via qudt:hasQuantityKind or similar) used in benchmarks that include the DRI Corpus dataset.

Here is the refined query:

```sparql
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R209701> .
  ?benchmark orkgp:HAS_EVALUATION ?quantity .
  ?quantity orkgp:P45074 ?metric .
  OPTIONAL { ?metric rdfs:label ?metricLabel }
}
```

This query will return all evaluation metrics (and their labels, if available) used in benchmarks involving the DRI Corpus dataset.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is on the right track and returns the IRIs of the evaluation metrics, which is correct. However, it currently only retrieves metrics that are linked via the property orkgp:P45074 from the evaluation node. There may be other properties used to link evaluation metrics in the ORKG, such as orkgp:HAS_METRIC or similar, depending on the schema. To ensure completeness, the query should be refined to include all possible properties that could link evaluation metrics to the evaluation node. Additionally, the query should not assume a single property for metrics unless it is certain that this is the only one used in the knowledge graph. Please review the schema or expand the query to cover other possible metric properties.

[Assistant]
Thank you for the feedback! To ensure completeness, I will:

1. Identify all properties in ORKG that could link an evaluation node to a metric (e.g., orkgp:P45074, orkgp:HAS_METRIC, orkgp:P35217, orkgp:P41532, etc.).
2. Expand the SPARQL query to include all such properties in a UNION, so we capture all possible ways metrics might be linked to evaluations in benchmarks involving the DRI Corpus.

**Step 1: Search for relevant metric-linking properties**
Let’s search for properties with "metric" in their name.

[Tool]
Top 10 property alternatives:
1. metric (orkgp:P2006)
2. metric (orkgp:HAS_METRIC)
3. Uses metric (orkgp:P24010)
4. is measured in (orkgp:P44022)
5. is measured in (orkgp:P57078)
6. measured by (orkgp:P7069)
7. Measured in (orkgp:P43238)
8. measured (orkgp:P36033)
9. Measurement type (orkgp:P15511)
10. measure (orkgp:P4006)

[Assistant]
We found several relevant properties:
- metric (orkgp:P2006)
- metric (orkgp:HAS_METRIC)
- Uses metric (orkgp:P24010)
- is measured in (orkgp:P44022, orkgp:P57078)
- measured by (orkgp:P7069)
- Measured in (orkgp:P43238)
- measured (orkgp:P36033)
- Measurement type (orkgp:P15511)
- measure (orkgp:P4006)

Previously, we also saw orkgp:P45074 (qudt:hasQuantityKind) being used.

**Step 2: Expand the query**
I will now write a query that includes all these properties as possible links from the evaluation node to the metric.

Here is the refined query:

```sparql
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R209701> .
  ?benchmark orkgp:HAS_EVALUATION ?quantity .
  {
    ?quantity orkgp:P45074 ?metric .
  } UNION {
    ?quantity orkgp:P2006 ?metric .
  } UNION {
    ?quantity orkgp:HAS_METRIC ?metric .
  } UNION {
    ?quantity orkgp:P24010 ?metric .
  } UNION {
    ?quantity orkgp:P44022 ?metric .
  } UNION {
    ?quantity orkgp:P57078 ?metric .
  } UNION {
    ?quantity orkgp:P7069 ?metric .
  } UNION {
    ?quantity orkgp:P43238 ?metric .
  } UNION {
    ?quantity orkgp:P36033 ?metric .
  } UNION {
    ?quantity orkgp:P15511 ?metric .
  } UNION {
    ?quantity orkgp:P4006 ?metric .
  }
  OPTIONAL { ?metric rdfs:label ?metricLabel }
}
```

This query will comprehensively retrieve all evaluation metrics used in benchmarks involving the DRI Corpus, regardless of which property is used to link the metric.

[Tool] Stopped generation process