PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?benchmark ?evaluation ?metric ?value ?metricLabel WHERE {
  {
    SELECT ( MAX( xsd:float ( ?v ) ) AS ?maxValue ) WHERE {
      ?b orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R114163> ; orkgp:HAS_EVALUATION ?e .
      ?e orkgp:HAS_VALUE ?v .
    }
  }
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R114163> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric ; orkgp:HAS_VALUE ?value .
  FILTER ( xsd:float ( ?value ) = ?maxValue ) OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The highest benchmark result achieved on the FSNS - Test dataset is a Sequence error of 27.54.

[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 highest benchmark result achieved on the FSNS - Test dataset, including the metric and its value?

[Assistant]
Let's break down the question:

1. We are looking for benchmark results achieved on the "FSNS - Test" dataset.
2. For each result, we want the metric and its value.
3. We want the highest result (so, likely, the maximum value for a given metric).

Step 1: Search for the "FSNS - Test" dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. FSNS - Test (<http://orkg.org/orkg/resource/R114163>): has type Dataset
2. Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>): has type Benchmark
3. Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>): has type Benchmark
4. Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>): has type Benchmark
5. state - of - the - art test performance (<http://orkg.org/orkg/resource/R46421>)
6. intermediate - based on ESL placement test (<http://orkg.org/orkg/resource/R197135>)
7. test (<http://orkg.org/orkg/resource/R215110>): has type Comparison, test
8. test (<http://orkg.org/orkg/resource/R239547>): has type Comparison, test
9. test (<http://orkg.org/orkg/resource/R239548>): has type Comparison, tset
10. test (<http://orkg.org/orkg/resource/R240000>): has type Comparison, test

[Assistant]
The entity "FSNS - Test" (<http://orkg.org/orkg/resource/R114163>) is identified as the dataset. Now, I need to find benchmark results achieved on this dataset, including the metric and its value.

Next, I will search for properties related to "benchmark result" or similar for this dataset.

[Tool]
No property items found

No other items found

[Assistant]
No direct "benchmark" property is found for the "FSNS - Test" dataset entity. However, benchmarks are often represented as separate resources linked to the dataset.

Given that, I will look for statements where "FSNS - Test" is used as an object, to find benchmarks or results associated with it.

[Tool]
Got 3 rows and 3 columns:
| subject                                                         | property                        | object                                                |
| --------------------------------------------------------------- | ------------------------------- | ----------------------------------------------------- |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | has dataset (orkgp:HAS_DATASET) | FSNS - Test (<http://orkg.org/orkg/resource/R114163>) |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | has dataset (orkgp:HAS_DATASET) | FSNS - Test (<http://orkg.org/orkg/resource/R114163>) |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | has dataset (orkgp:HAS_DATASET) | FSNS - Test (<http://orkg.org/orkg/resource/R114163>) |

[Assistant]
There are three benchmark resources associated with the "FSNS - Test" dataset:

- <http://orkg.org/orkg/resource/R129321>
- <http://orkg.org/orkg/resource/R129325>
- <http://orkg.org/orkg/resource/R129329>

Next, I will inspect these benchmark resources to find their result metrics and values. I'll start by listing the properties for one of them to understand the structure.

[Tool]
Got 5 rows and 3 columns:
| subject                                                         | property                          | object                                                                   |
| --------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------ |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | has dataset (orkgp:HAS_DATASET)   | FSNS - Test (<http://orkg.org/orkg/resource/R114163>)                    |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                              |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | rdfs:label                        | Benchmark FSNS - Test (xsd:string)                                       |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129321>) | rdf:type                          | orkgc:Resource                                                           |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                  | property                  | object                                                   |
| ------------------------------------------------------------------------ | ------------------------- | -------------------------------------------------------- |
| Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) | value (orkgp:HAS_VALUE)   | 15.8 (xsd:string)                                        |
| Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) | metric (orkgp:HAS_METRIC) | Sequence error (<http://orkg.org/orkg/resource/R114165>) |
| Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) | rdf:type                  | Evaluation (orkgc:Evaluation)                            |
| Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) | rdfs:label                | Evaluation Sequence error_15.8 (xsd:string)              |
| Evaluation Sequence error_15.8 (<http://orkg.org/orkg/resource/R129322>) | rdf:type                  | orkgc:Resource                                           |

[Tool]
Got 5 rows and 3 columns:
| subject                                                         | property                          | object                                                                 |
| --------------------------------------------------------------- | --------------------------------- | ---------------------------------------------------------------------- |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | has dataset (orkgp:HAS_DATASET)   | FSNS - Test (<http://orkg.org/orkg/resource/R114163>)                  |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                            |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | rdfs:label                        | Benchmark FSNS - Test (xsd:string)                                     |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129325>) | rdf:type                          | orkgc:Resource                                                         |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                  | object                                                   |
| ---------------------------------------------------------------------- | ------------------------- | -------------------------------------------------------- |
| Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) | value (orkgp:HAS_VALUE)   | 22 (xsd:string)                                          |
| Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) | metric (orkgp:HAS_METRIC) | Sequence error (<http://orkg.org/orkg/resource/R114165>) |
| Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) | rdf:type                  | Evaluation (orkgc:Evaluation)                            |
| Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) | rdfs:label                | Evaluation Sequence error_22 (xsd:string)                |
| Evaluation Sequence error_22 (<http://orkg.org/orkg/resource/R129326>) | rdf:type                  | orkgc:Resource                                           |

[Tool]
Got 5 rows and 3 columns:
| subject                                                         | property                          | object                                                                    |
| --------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | has dataset (orkgp:HAS_DATASET)   | FSNS - Test (<http://orkg.org/orkg/resource/R114163>)                     |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | rdfs:label                        | Benchmark FSNS - Test (xsd:string)                                        |
| Benchmark FSNS - Test (<http://orkg.org/orkg/resource/R129329>) | rdf:type                          | orkgc:Resource                                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                   |
| ------------------------------------------------------------------------- | ------------------------- | -------------------------------------------------------- |
| Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) | value (orkgp:HAS_VALUE)   | 27.54 (xsd:string)                                       |
| Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) | metric (orkgp:HAS_METRIC) | Sequence error (<http://orkg.org/orkg/resource/R114165>) |
| Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) | rdf:type                  | Evaluation (orkgc:Evaluation)                            |
| Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) | rdfs:label                | Evaluation Sequence error_27.54 (xsd:string)             |
| Evaluation Sequence error_27.54 (<http://orkg.org/orkg/resource/R129330>) | rdf:type                  | orkgc:Resource                                           |

[Assistant]
All three benchmarks use the metric "Sequence error" and have values: 15.8, 22, and 27.54.

Since the question asks for the highest benchmark result, and assuming lower sequence error is better (as is typical), but the question specifically asks for the "highest" value, I will return the maximum value.

Now, I will write a SPARQL query to retrieve all benchmark results on the FSNS - Test dataset, their metric, and value, and select the one with the highest value.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is invalid due to a syntax error. The FILTER clause attempts to compare a value to the result of a subquery, but the syntax is incorrect for SPARQL 1.1. To find the highest benchmark result, you should use a subquery to select the maximum value per metric, then join this back to the main query. Additionally, ensure that the query returns the IRIs of the items (benchmark, evaluation, metric) as required. Please rework the query to use a valid SPARQL 1.1 pattern for selecting the maximum value and retrieving the associated metric and benchmark.

[Assistant]
Thank you for the feedback. You are correct: in SPARQL 1.1, we cannot use a FILTER to compare a value to the result of a subquery directly. Instead, we should use a subquery to compute the maximum value, then join it back to the main query to retrieve the associated benchmark, evaluation, and metric.

Here’s my revised plan:
1. Write a subquery to find the maximum value of all benchmark results for the FSNS - Test dataset.
2. Join this maximum value back to the main query to retrieve the corresponding benchmark, evaluation, metric, and their IRIs.

I will now write the corrected SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is invalid due to a syntax error: BIND is used outside of a WHERE clause, which is not allowed. Additionally, the subquery attempts to use a variable (?vf) outside its scope. To fix this, first select the maximum value in a subquery, then join it with the main query to retrieve the corresponding benchmark, metric, and value. Please rewrite the query to ensure correct subquery usage and variable scoping, and ensure the query executes successfully.

[Assistant]
Thank you for the detailed feedback. You are correct: BIND cannot be used outside a WHERE clause, and variables from a subquery cannot be directly referenced outside their scope. The correct approach is:

1. Use a subquery to select the maximum value (?maxValue) for the FSNS - Test dataset.
2. In the main query, join on this maximum value to retrieve the associated benchmark, evaluation, metric, and value.

Let me now write a SPARQL query that follows this pattern.

[Tool] Stopped generation process


PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark a orkgc:Benchmark ; rdfs:label ?label .
  FILTER regex( ?label , "Atari 2600 Fishing Derby" , "i" ) ?benchmark orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

This query retrieves all metrics used to evaluate models on any benchmark whose label contains 'Atari 2600 Fishing Derby'. The metrics are those linked via the HAS_METRIC property in evaluations of these benchmarks.

[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] List the metrics that are used to evaluate models on the Atari 2600 Fishing Derby benchmark dataset?

[Assistant]
Let's break down the question:

1. The user is asking for metrics used to evaluate models on the "Atari 2600 Fishing Derby benchmark dataset".
2. We need to identify the entity for the "Atari 2600 Fishing Derby benchmark dataset".
3. Then, we need to find properties or relations that connect this dataset to evaluation metrics.
4. Finally, we need to list the metrics (as IRIs, optionally with labels).

Step 1: Search for the entity "Atari 2600 Fishing Derby benchmark dataset" in ORKG.

[Tool]
Top 10 entity alternatives:
1. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>): has type Benchmark
2. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133981>): has type Benchmark
3. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133869>): has type Benchmark
4. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133753>): has type Benchmark
5. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133652>): has type Benchmark
6. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133551>): has type Benchmark
7. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133449>): has type Benchmark
8. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133253>): has type Benchmark
9. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133153>): has type Benchmark
10. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133055>): has type Benchmark

[Assistant]
There are multiple entities named "Benchmark Atari 2600 Fishing Derby" in the ORKG, each with a unique IRI. Since the question is about the metrics used to evaluate models on this benchmark, it is likely that the metrics are associated with one or more of these entities.

Next, I need to determine which properties connect these benchmark entities to evaluation metrics. Typically, such properties might be named "metric", "evaluation metric", or similar.

I will search for properties related to "metric" in the ORKG.

[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]
There are several properties related to "metric", with the most direct ones being:
- orkgp:P2006 (metric)
- orkgp:HAS_METRIC (metric)
- orkgp:P24010 (Uses metric)

To determine which of these properties are actually used with the "Benchmark Atari 2600 Fishing Derby" entities, I will list triples for one of these entities (let's pick the first: <http://orkg.org/orkg/resource/R132852>) and see which properties connect it to metrics or metric-like entities.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                      | property                          | object                                                             |
| ---------------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------ |
| Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>)    |
| Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>) | has dataset (orkgp:HAS_DATASET)   | Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R124984>) |
| Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                        |
| Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>) | rdfs:label                        | Benchmark Atari 2600 Fishing Derby (xsd:string)                    |
| Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>) | rdf:type                          | orkgc:Resource                                                     |

[Assistant]
The property orkgp:HAS_EVALUATION connects the benchmark entity to an "Evaluation Score" entity (<http://orkg.org/orkg/resource/R132853>). To find the metric(s) used, I need to inspect the properties of this evaluation entity and see if it is linked to a metric.

Next, I will list the triples for <http://orkg.org/orkg/resource/R132853> to see if it connects to any metric entities or literals.

[Tool]
Got 5 rows and 3 columns:
| subject                                                         | property                  | object                                          |
| --------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>) | value (orkgp:HAS_VALUE)   | -1.6 (xsd:string)                               |
| Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>) | rdfs:label                | Evaluation Score_-1.6 (xsd:string)              |
| Evaluation Score_-1.6 (<http://orkg.org/orkg/resource/R132853>) | rdf:type                  | orkgc:Resource                                  |

[Assistant]
The property orkgp:HAS_METRIC connects the evaluation entity to a metric entity: "Score" (<http://orkg.org/orkg/resource/R119875>). This suggests that the metric used for this benchmark is "Score".

However, since there are multiple "Benchmark Atari 2600 Fishing Derby" entities, and possibly multiple metrics, I should generalize the query to:
- Find all metrics (via orkgp:HAS_METRIC) used in evaluations (via orkgp:HAS_EVALUATION) of any "Benchmark Atari 2600 Fishing Derby" entity.

I will now construct a SPARQL query that:
1. Finds all entities with a label "Benchmark Atari 2600 Fishing Derby".
2. Follows orkgp:HAS_EVALUATION to evaluations.
3. Follows orkgp:HAS_METRIC to metrics.
4. Returns the metric IRIs and optionally their labels.

Here is the SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is structurally correct for retrieving metrics used in evaluations of the specified benchmark, but the execution result is empty, indicating that either the benchmark with the exact label "Benchmark Atari 2600 Fishing Derby" does not exist, or the relationships are not present as expected in the knowledge graph. The system should try to:

1. Check for alternative labels or naming conventions for the benchmark (e.g., without the word "Benchmark", or different capitalization).
2. Consider searching for the benchmark entity by partial label match or other properties if available.
3. If the benchmark exists, verify that it is linked to evaluations and metrics as expected.

Please refine the query to address these possibilities and try again.

[Assistant]
Thank you for the feedback. The empty result suggests that the label "Benchmark Atari 2600 Fishing Derby" may not match exactly, or the relationships are not as expected. Here’s my step-by-step plan to address this:

1. Search for entities with the label containing "Atari 2600 Fishing Derby" (without "Benchmark") to check for alternative naming conventions.
2. If found, check how these entities are linked to evaluations and metrics.
3. Adjust the SPARQL query to use a partial label match (FILTER with CONTAINS or regex) to be more flexible.

Step 1: Search for entities with "Atari 2600 Fishing Derby" in the label.

[Tool]
Top 10 entity alternatives:
1. Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R124984>): has type Dataset
2. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R132852>): has type Benchmark
3. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133981>): has type Benchmark
4. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133869>): has type Benchmark
5. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133753>): has type Benchmark
6. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133652>): has type Benchmark
7. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133551>): has type Benchmark
8. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133449>): has type Benchmark
9. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133253>): has type Benchmark
10. Benchmark Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R133153>): has type Benchmark

[Assistant]
There is an entity "Atari 2600 Fishing Derby" (http://orkg.org/orkg/resource/R124984) of type Dataset, and several "Benchmark Atari 2600 Fishing Derby" entities of type Benchmark.

Since the dataset entity might be linked to benchmarks or evaluations, and the benchmarks may have different labels, I will now:
- Check if any of these entities (dataset or benchmarks) are linked to evaluations and metrics.
- Use a more flexible SPARQL query with FILTER regex to match any label containing "Atari 2600 Fishing Derby".

I will first check how the dataset entity (<http://orkg.org/orkg/resource/R124984>) is linked to other entities.

[Tool]
Got 3 rows and 3 columns:
| subject                                                            | property   | object                                |
| ------------------------------------------------------------------ | ---------- | ------------------------------------- |
| Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R124984>) | rdf:type   | Dataset (orkgc:Dataset)               |
| Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R124984>) | rdfs:label | Atari 2600 Fishing Derby (xsd:string) |
| Atari 2600 Fishing Derby (<http://orkg.org/orkg/resource/R124984>) | rdf:type   | orkgc:Resource                        |

[Assistant]
The dataset entity "Atari 2600 Fishing Derby" is not directly linked to any evaluations or metrics. Therefore, the relevant connections are likely through the "Benchmark Atari 2600 Fishing Derby" entities.

Since there are multiple benchmark entities with similar names, I will write a SPARQL query that:
- Finds all entities whose label contains "Atari 2600 Fishing Derby" and are of type Benchmark.
- Follows any evaluation and metric links from those entities.
- Returns the metric IRIs and optionally their labels.

Here is the refined SPARQL query using FILTER regex for label matching:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all metrics used to evaluate models on benchmarks whose label contains 'Atari 2600 Fishing Derby'. It returns the metric IRIs and optionally their labels, as required. The answer is complete and follows the guidelines, including returning the actual identifiers. No further refinement is needed.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?metric ?metricLabel WHERE {
  VALUES ?agnews {
    <http://orkg.org/orkg/resource/R116787> <http://orkg.org/orkg/resource/R130150>
  }
  ?agnews orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The only metric currently used to evaluate models on the AG News benchmark dataset in ORKG is 'Error' (http://orkg.org/orkg/resource/R119433).

[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] List the metrics that are used to evaluate models on the AG News benchmark dataset?

[Assistant]
Let's break down the question:

1. The user wants to know which metrics are used to evaluate models on the AG News benchmark dataset.
2. This involves identifying the AG News dataset entity, and then finding evaluation metrics associated with it, specifically in the context of model evaluation.

Step 1: Search for the AG News benchmark dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. AG News (<http://orkg.org/orkg/resource/R219360>)
2. AG News (<http://orkg.org/orkg/resource/R116787>): has type Dataset
3. Benchmark AG News (<http://orkg.org/orkg/resource/R130150>): has type Benchmark
4. AG’s News (<http://orkg.org/orkg/resource/R218417>)
5. News agency (<http://orkg.org/orkg/resource/R166056>)
6. AG News (200 Labels) (<http://orkg.org/orkg/resource/R126008>): has type Dataset
7. ACN Catalan news agency (<http://orkg.org/orkg/resource/R213981>)
8. EFE Spanish news agency (<http://orkg.org/orkg/resource/R213979>)
9. Spanish EFE News Agency (<http://orkg.org/orkg/resource/R162726>)
10. XIN (Xinhua News Agency) (<http://orkg.org/orkg/resource/R215496>)

[Assistant]
I found several entities related to "AG News" in ORKG, including:

- AG News (<http://orkg.org/orkg/resource/R219360>)
- AG News (<http://orkg.org/orkg/resource/R116787>) (type: Dataset)
- Benchmark AG News (<http://orkg.org/orkg/resource/R130150>) (type: Benchmark)
- AG News (200 Labels) (<http://orkg.org/orkg/resource/R126008>) (type: Dataset)

Since the question is about the "AG News benchmark dataset," the most relevant entities are likely:
- AG News (<http://orkg.org/orkg/resource/R116787>) (Dataset)
- Benchmark AG News (<http://orkg.org/orkg/resource/R130150>) (Benchmark)

To be thorough, I will check both entities for associated evaluation metrics.

Step 2: Search for properties related to "metric" for these entities, starting with the dataset entity.

[Tool]
No property items found

No other items found

[Assistant]
No properties related to "metric" were found directly for the AG News dataset entity (<http://orkg.org/orkg/resource/R116787>).

Next, I will check the "Benchmark AG News" entity (<http://orkg.org/orkg/resource/R130150>) for properties related to "metric".

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

No other items found

[Assistant]
For the "Benchmark AG News" entity, I found a property called "evaluation" (orkgp:HAS_EVALUATION), which is likely related to evaluation metrics.

Step 3: List triples where "Benchmark AG News" is the subject and the property is "evaluation" to see what evaluations (and possibly metrics) are linked.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                          | object                                                          |
| ----------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------- |
| Benchmark AG News (<http://orkg.org/orkg/resource/R130150>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) |

[Assistant]
The "Benchmark AG News" entity has an evaluation linked: "Evaluation Error_4.45" (<http://orkg.org/orkg/resource/R130151>).

Step 4: Let's explore this evaluation entity to see if it specifies which metric(s) it uses. I'll list all properties for this evaluation entity.

[Tool]
Got 5 rows and 3 columns:
| subject                                                         | property                  | object                                          |
| --------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) | value (orkgp:HAS_VALUE)   | 4.45 (xsd:string)                               |
| Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) | metric (orkgp:HAS_METRIC) | Error (<http://orkg.org/orkg/resource/R119433>) |
| Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) | rdfs:label                | Evaluation Error_4.45 (xsd:string)              |
| Evaluation Error_4.45 (<http://orkg.org/orkg/resource/R130151>) | rdf:type                  | orkgc:Resource                                  |

[Tool]
Got 3 rows and 3 columns:
| subject                                         | property   | object                |
| ----------------------------------------------- | ---------- | --------------------- |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdf:type   | Metric (orkgc:Metric) |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdfs:label | Error (xsd:string)    |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdf:type   | orkgc:Resource        |

[Assistant]
The evaluation linked to the AG News benchmark uses the metric "Error" (<http://orkg.org/orkg/resource/R119433>).

To be thorough, let's check if there are other evaluations (and thus other metrics) associated with the AG News dataset or benchmark. I'll look for all evaluations linked to both the dataset and benchmark entities.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

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

[Assistant]
Listing all evaluations for all entities is too broad and not efficient. Since the only evaluation found directly linked to the AG News benchmark is "Evaluation Error_4.45" using the "Error" metric, it appears that "Error" is the only metric currently associated with model evaluation on the AG News benchmark in ORKG.

Now, I will write a SPARQL query that finds all metrics used in evaluations associated with the AG News benchmark dataset (including both the dataset and benchmark entities, to be thorough).

Here is the query:

```sparql
SELECT DISTINCT ?metric ?metricLabel WHERE {
  VALUES ?agnews {
    <http://orkg.org/orkg/resource/R116787>  # AG News Dataset
    <http://orkg.org/orkg/resource/R130150>  # Benchmark AG News
  }
  ?agnews orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL { ?metric rdfs:label ?metricLabel }
}
```

This will return all metrics (and their labels, if available) used to evaluate models on the AG News benchmark dataset in ORKG.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the metrics used to evaluate models on the AG News benchmark dataset by following the evaluation and metric relationships. It returns the actual metric IRIs and optionally includes their labels, as per best practices. The answer is accurate and complete given the current data in the ORKG knowledge graph.


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/R125986> .
  ?benchmark orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The metric of evaluation over the PubMed 20k RCT dataset recorded in ORKG is F1 (http://orkg.org/orkg/resource/R114483), with the label 'F1'.

[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 metrics of evaluation over the PubMed 20k RCT dataset?

[Assistant]
Let's break down the question:

1. The user wants to know the "metrics of evaluation" used "over the PubMed 20k RCT dataset".
2. So, we need to identify:
   - The entity for "PubMed 20k RCT dataset".
   - The property for "metrics of evaluation" (likely something like "evaluation metric", "metric", or "evaluation").

First, I will search for the entity "PubMed 20k RCT dataset" in the ORKG.

[Tool]
Top 10 entity alternatives:
1. PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>): has type Dataset
2. Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>): has type Benchmark
3. Dataset (<http://orkg.org/orkg/resource/R178304>): has type Contribution Template
4. Dataset (<http://orkg.org/orkg/resource/R216575>): has type Contribution, has type R216526
5. Dataset (<http://orkg.org/orkg/resource/R182198>): has type Contribution
6. Dataset (<http://orkg.org/orkg/resource/R70898>): has type Contribution Template
7. Dataset (<http://orkg.org/orkg/resource/R11002>): has type Contribution
8. Dataset (<http://orkg.org/orkg/resource/R11006>): has type Contribution
9. Dataset (<http://orkg.org/orkg/resource/R12082>): has type Contribution
10. Dataset (<http://orkg.org/orkg/resource/R11015>): has type Contribution

[Assistant]
The top result is "PubMed 20k RCT" with IRI <http://orkg.org/orkg/resource/R125986>, and it is of type Dataset. This is likely the correct entity.

Next, I need to find the property that links datasets to their evaluation metrics. I will search for properties related to "metric" in the ORKG.

[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]
There are several properties related to "metric". The most relevant ones seem to be:
- metric (orkgp:P2006)
- metric (orkgp:HAS_METRIC)
- Uses metric (orkgp:P24010)

To determine which property is actually used with the "PubMed 20k RCT" dataset, I will list all triples where <http://orkg.org/orkg/resource/R125986> is the subject. This will show which properties are used with this dataset.

[Tool]
Got 3 rows and 3 columns:
| subject                                                  | property   | object                      |
| -------------------------------------------------------- | ---------- | --------------------------- |
| PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>) | rdf:type   | Dataset (orkgc:Dataset)     |
| PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>) | rdfs:label | PubMed 20k RCT (xsd:string) |
| PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>) | rdf:type   | orkgc:Resource              |

[Assistant]
The "PubMed 20k RCT" dataset entity does not directly list any evaluation metrics or related properties. However, in ORKG, evaluation metrics might be linked indirectly, for example, through contributions, papers, or benchmarks that use this dataset.

The next step is to check if there are any contributions or resources that reference the "PubMed 20k RCT" dataset and list their properties to see if evaluation metrics are mentioned.

I will search for triples where the object is <http://orkg.org/orkg/resource/R125986> to see which resources reference this dataset.

[Tool]
Got 1 row and 3 columns:
| subject                                                            | property                        | object                                                   |
| ------------------------------------------------------------------ | ------------------------------- | -------------------------------------------------------- |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | has dataset (orkgp:HAS_DATASET) | PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>) |

[Assistant]
There is a "Benchmark PubMed 20k RCT" (<http://orkg.org/orkg/resource/R129452>) that references the "PubMed 20k RCT" dataset via the property "has dataset" (orkgp:HAS_DATASET).

It is likely that evaluation metrics are associated with this benchmark. I will now list all triples where <http://orkg.org/orkg/resource/R129452> is the subject to see if any evaluation metrics are specified.

[Tool]
Got 5 rows and 3 columns:
| subject                                                            | property                          | object                                                        |
| ------------------------------------------------------------------ | --------------------------------- | ------------------------------------------------------------- |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | has dataset (orkgp:HAS_DATASET)   | PubMed 20k RCT (<http://orkg.org/orkg/resource/R125986>)      |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                   |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | rdfs:label                        | Benchmark PubMed 20k RCT (xsd:string)                         |
| Benchmark PubMed 20k RCT (<http://orkg.org/orkg/resource/R129452>) | rdf:type                          | orkgc:Resource                                                |

[Assistant]
The benchmark references an evaluation entity: "Evaluation F1_86.81" (<http://orkg.org/orkg/resource/R129453>) via the property "evaluation" (orkgp:HAS_EVALUATION).

To find out the metric(s) used in this evaluation, I will list all triples where <http://orkg.org/orkg/resource/R129453> is the subject.

[Tool]
Got 5 rows and 3 columns:
| subject                                                       | property                  | object                                       |
| ------------------------------------------------------------- | ------------------------- | -------------------------------------------- |
| Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) | value (orkgp:HAS_VALUE)   | 86.81 (xsd:string)                           |
| Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) | metric (orkgp:HAS_METRIC) | F1 (<http://orkg.org/orkg/resource/R114483>) |
| Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) | rdf:type                  | Evaluation (orkgc:Evaluation)                |
| Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) | rdfs:label                | Evaluation F1_86.81 (xsd:string)             |
| Evaluation F1_86.81 (<http://orkg.org/orkg/resource/R129453>) | rdf:type                  | orkgc:Resource                               |

[Tool]
Got 3 rows and 3 columns:
| subject                                      | property   | object                |
| -------------------------------------------- | ---------- | --------------------- |
| F1 (<http://orkg.org/orkg/resource/R114483>) | rdf:type   | Metric (orkgc:Metric) |
| F1 (<http://orkg.org/orkg/resource/R114483>) | rdfs:label | F1 (xsd:string)       |
| F1 (<http://orkg.org/orkg/resource/R114483>) | rdf:type   | orkgc:Resource        |

[Assistant]
The evaluation entity is linked to the metric "F1" (<http://orkg.org/orkg/resource/R114483>), which is of type Metric.

To answer the user's question comprehensively, I should check if there are other metrics associated with the PubMed 20k RCT dataset, possibly through other benchmarks or evaluations. I will search for all benchmarks that use the PubMed 20k RCT dataset and retrieve all metrics used in their evaluations.

I will write a SPARQL query that:
- Finds all benchmarks that have the PubMed 20k RCT dataset.
- Follows their evaluations to the metrics used.

This will ensure completeness.

[Tool]
Got 1 row and 2 columns:
| metric                                       | metricLabel     |
| -------------------------------------------- | --------------- |
| F1 (<http://orkg.org/orkg/resource/R114483>) | F1 (xsd:string) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves the metrics used for evaluation over the PubMed 20k RCT dataset by following the dataset-to-benchmark-to-evaluation-to-metric path. However, the query only returns the metric IRI and not its human-readable label. For completeness and better usability, the query should optionally include the metric's label using rdfs:label or a similar property. This would help users understand the result without needing to dereference the IRI. Please refine the query to include the metric label as an optional variable.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and follows all the required rules. It returns the actual identifier (IRI) of the metric and optionally includes the human-readable label. The query structure is appropriate for the question, and the answer is correctly derived from the query result. No further refinement is needed.


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 ?accuracy WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124062> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC <http://orkg.org/orkg/resource/R111697> ; orkgp:HAS_VALUE ?accuracy .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark ; orkgp:HAS_MODEL ?model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel
  }
}
ORDER BY DESC ( xsd:float ( ?accuracy ) )
LIMIT 1

The top performing model in terms of Accuracy score on the MLDoc Zero-Shot English-to-French dataset is XLMft UDA.

[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 Accuracy score when benchmarked on the MLDoc Zero-Shot English-to-French dataset?

[Assistant]
Let's break down the question:

1. We are looking for models that have been benchmarked on the "MLDoc Zero-Shot English-to-French" dataset.
2. We want to find the model with the highest "Accuracy" score on this benchmark.
3. We want the name of that top performing model.

Step 1: Search for the "MLDoc Zero-Shot English-to-French" dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>): has type Dataset
2. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>): has type Benchmark
3. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>): has type Benchmark
4. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>): has type Benchmark
5. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>): has type Benchmark
6. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>): has type Benchmark
7. Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>): has type Benchmark
8. MLDoc Zero-Shot English-to-Japanese (<http://orkg.org/orkg/resource/R124050>): has type Dataset
9. MLDoc Zero-Shot German-to-French (<http://orkg.org/orkg/resource/R124057>): has type Dataset
10. MLDoc Zero-Shot English-to-Spanish (<http://orkg.org/orkg/resource/R124058>): has type Dataset

[Assistant]
I found several entities related to "MLDoc Zero-Shot English-to-French" in the ORKG, including a dataset entity (<http://orkg.org/orkg/resource/R124062>) and multiple benchmark entities. Since the question is about benchmarking (i.e., model performance), it is likely that the relevant results are associated with one or more of the benchmark entities.

Next, I will check what properties are associated with one of these benchmark entities (let's start with <http://orkg.org/orkg/resource/R131692>) to see how models and accuracy scores are represented.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | rdf:type                          | orkgc:Resource                                                              |

[Assistant]
The benchmark entity <http://orkg.org/orkg/resource/R131692> has an evaluation result: "Evaluation Accuracy_74.52" (<http://orkg.org/orkg/resource/R131693>). This suggests that each benchmark is linked to one or more evaluation results, which likely contain accuracy scores.

To find the top performing model, I need to:
1. Identify all benchmark entities for "MLDoc Zero-Shot English-to-French".
2. For each benchmark, find the associated evaluation(s) and their accuracy scores.
3. For each evaluation, determine the model being evaluated.
4. Select the model with the highest accuracy score.

First, let's list the evaluation results for all the benchmark entities found earlier.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that querying for all triples with the property "orkgp:HAS_EVALUATION" did not return any results, possibly due to the need for more specific subject constraints or a different property for linking evaluations.

Since we already know that <http://orkg.org/orkg/resource/R131692> uses "orkgp:HAS_EVALUATION" to link to its evaluation, let's check the properties of the evaluation entity <http://orkg.org/orkg/resource/R131693> to see how the accuracy score and model are represented.

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>) | value (orkgp:HAS_VALUE)   | 74.52 (xsd:string)                                 |
| Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>) | rdfs:label                | Evaluation Accuracy_74.52 (xsd:string)             |
| Evaluation Accuracy_74.52 (<http://orkg.org/orkg/resource/R131693>) | rdf:type                  | orkgc:Resource                                     |

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

[Assistant]
From the previous steps, I see that the evaluation entity contains the accuracy value and metric, but it does not directly indicate the model. The "HAS_EVALUATION" property is used to link a subject (possibly a model or a contribution) to an evaluation.

To find the top performing model, I need to:
- Identify all benchmarks for "MLDoc Zero-Shot English-to-French".
- For each benchmark, get the evaluations with the "Accuracy" metric.
- For each evaluation, trace back to the subject (likely the model or contribution).
- Find the highest accuracy and the corresponding model.

Since there are multiple benchmark entities for "MLDoc Zero-Shot English-to-French", I will list their evaluations and try to find the associated models.

Let's list the properties for another benchmark entity, e.g., <http://orkg.org/orkg/resource/R131706>.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>) | value (orkgp:HAS_VALUE)   | 77.95 (xsd:string)                                 |
| Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>) | rdfs:label                | Evaluation Accuracy_77.95 (xsd:string)             |
| Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_77.95 (<http://orkg.org/orkg/resource/R131707>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 6 rows and 3 columns:
| subject                                                                               | property                        | object                                                                      |
| ------------------------------------------------------------------------------------- | ------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131692>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131706>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131654>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>) | value (orkgp:HAS_VALUE)   | 89.42 (xsd:string)                                 |
| Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>) | rdfs:label                | Evaluation Accuracy_89.42 (xsd:string)             |
| Evaluation Accuracy_89.42 (<http://orkg.org/orkg/resource/R131655>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131670>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>) | value (orkgp:HAS_VALUE)   | 72.38 (xsd:string)                                 |
| Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>) | rdfs:label                | Evaluation Accuracy_72.38 (xsd:string)             |
| Evaluation Accuracy_72.38 (<http://orkg.org/orkg/resource/R131671>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131681>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>) | value (orkgp:HAS_VALUE)   | 72.83 (xsd:string)                                 |
| Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>) | rdfs:label                | Evaluation Accuracy_72.83 (xsd:string)             |
| Evaluation Accuracy_72.83 (<http://orkg.org/orkg/resource/R131682>) | rdf:type                  | orkgc:Resource                                     |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                               | property                          | object                                                                      |
| ------------------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>)         |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R124062>) |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                 |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-French (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) | rdf:type                          | orkgc:Resource                                                              |

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>) | value (orkgp:HAS_VALUE)   | 96.05 (xsd:string)                                 |
| Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>) | rdfs:label                | Evaluation Accuracy_96.05 (xsd:string)             |
| Evaluation Accuracy_96.05 (<http://orkg.org/orkg/resource/R131729>) | rdf:type                  | orkgc:Resource                                     |

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

No other items found

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

No other items found

[Tool]
Got 1 row and 3 columns:
| subject                                                           | property                            | object                                                                                |
| ----------------------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------- |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>) |

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                                           | property                                | object                                                                                |
| ----------------------------------------------------------------- | --------------------------------------- | -------------------------------------------------------------------------------------- |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | research problem (orkgp:P32)            | Cross-Lingual Document Classification (<http://orkg.org/orkg/resource/R124044>)        |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/laiguokun/xlu-data (xsd:string)                                     |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-German (<http://orkg.org/orkg/resource/R131720>)  |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has model (orkgp:HAS_MODEL)             | XLMft UDA (<http://orkg.org/orkg/resource/R124055>)                                    |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | rdf:type                                | Contribution (orkgc:Contribution)                                                      |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | rdfs:label                              | Contribution 	XLMft UDA (xsd:string)                                                   |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-Spanish (<http://orkg.org/orkg/resource/R131722>) |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131724>) |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-Chinese (<http://orkg.org/orkg/resource/R131726>) |
| Contribution  XLMft UDA (<http://orkg.org/orkg/resource/R131719>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark MLDoc Zero-Shot English-to-French (<http://orkg.org/orkg/resource/R131728>)  |
| ...                                                               | ...                                     | ...                                                                                |

[Tool]
Got 3 rows and 3 columns:
| subject                                             | property   | object                 |
| --------------------------------------------------- | ---------- | ---------------------- |
| XLMft UDA (<http://orkg.org/orkg/resource/R124055>) | rdf:type   | Model (orkgc:Model)    |
| XLMft UDA (<http://orkg.org/orkg/resource/R124055>) | rdfs:label | XLMft UDA (xsd:string) |
| XLMft UDA (<http://orkg.org/orkg/resource/R124055>) | rdf:type   | orkgc:Resource         |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies the top performing model by ordering the results by the accuracy score in descending order and limiting the output to one result. The query returns the model's IRI, its label (optionally), and the accuracy value, which matches the user's request. The answer provided is accurate and complete according to the query result. All SPARQL best practices are followed.