PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?paper ?title WHERE {
  ?frostbite_benchmark rdfs:label ?label .
  FILTER ( CONTAINS( LCASE( STR( ?label ) ) , "frostbite" ) ) ?contribution ?benchmark_pred ?frostbite_benchmark .
  FILTER ( ?benchmark_pred IN ( orkgp:P38001 , orkgp:hasBenchmark , orkgp:P34103 ) ) ?contribution orkgp:P15330 ?paper .
  OPTIONAL {
    ?paper <http://orkg.org/orkg/predicate/wikidata:P1476> ?title
  }
}

This query will robustly retrieve the ID and title of research papers that contain a benchmark over any entity labeled with 'Frostbite', including all possible variants of the Atari 2600 Frostbite benchmark.

[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 title and ID of research papers that contain a benchmark over the Atari 2600 Frostbite dataset?

[Assistant]
Let's break down the question:

1. We are looking for research papers (entities of type "research paper").
2. These papers should "contain a benchmark" (likely a property relating papers to benchmarks or datasets).
3. The benchmark should be "over the Atari 2600 Frostbite dataset" (so we need to identify the entity for this dataset).

Step 1: Search for the "Atari 2600 Frostbite" dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R125005>): has type Dataset
2. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R134246>): has type Benchmark
3. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R131230>): has type Benchmark
4. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R133077>): has type Benchmark
5. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132977>): has type Benchmark
6. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132876>): has type Benchmark
7. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132775>): has type Benchmark
8. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132676>): has type Benchmark
9. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132571>): has type Benchmark
10. Benchmark Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R132470>): has type Benchmark

[Assistant]
We found the following relevant entities:
- Atari 2600 Frostbite dataset: http://orkg.org/orkg/resource/R125005
- Multiple "Benchmark Atari 2600 Frostbite" entities, each with a unique IRI.

To answer the question, we should look for research papers that are linked to benchmarks over the Atari 2600 Frostbite dataset. The most direct approach is to look for research papers that reference either the dataset entity or any of the benchmark entities.

Step 2: Let's search for properties that link research papers to benchmarks or datasets, such as "evaluates", "uses dataset", or "contains benchmark".

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

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

[Tool]
Top 10 property alternatives:
1. Paper (orkgp:P15330)
2. has paper (orkgp:HasPaper)
3. Paper type (orkgp:P41847)
4. type of the paper (orkgp:P91000)
5. Has ORKG Paper (orkgp:P35034)
6. paper:type (orkgp:P41745)
7. Papers (orkgp:P15336)
8. paper: Theory / Concept / Model (orkgp:P43091)
9. paper: Theory / Construct / Model (orkgp:P43142)
10. paper:journal (orkgp:P37235)

[Tool]
Top 10 property alternatives:
1. title (<http://orkg.org/orkg/predicate/wikidata:P1476>)
2. Source title (orkgp:P35075)
3. Objective (orkgp:P67098)
4. Objective (orkgp:P15051)
5. Booktitle (orkgp:P37142)
6. Content (orkgp:P34137)
7. title metadata (orkgp:P91001)
8. Notes (orkgp:P9039)
9. published by (orkgp:P41566)
10. performance title (orkgp:P41316)

[Tool]
Got 5 rows and 3 columns:
| subject                                                       | property                           | object                                                                       |
| ------------------------------------------------------------- | ---------------------------------- | ---------------------------------------------------------------------------- |
| (Main) Contribution (<http://orkg.org/orkg/resource/R186169>) | has benchmark (orkgp:hasBenchmark) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R135532>) |
| Static ordering (<http://orkg.org/orkg/resource/R109830>)     | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| Static ordering (<http://orkg.org/orkg/resource/R109830>)     | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)    |
| Dynamic ordering (<http://orkg.org/orkg/resource/R135319>)    | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| Dynamic ordering (<http://orkg.org/orkg/resource/R135319>)    | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)    |

[Tool]
Got 3 rows and 3 columns:
| subject                                                       | property                     | object                                                                         |
| ------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------ |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)      |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)      |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | University Ontology Benchmark (UOBM) (<http://orkg.org/orkg/resource/R135307>) |

[Tool]
Got 165 rows and 3 columns, showing the first 10 rows below:
| subject                                                                  | property                      | object                                                                       |
| ------------------------------------------------------------------------ | ----------------------------- | ---------------------------------------------------------------------------- |
| Performance Study (<http://orkg.org/orkg/resource/R111492>)              | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R111634>) |
| Contribution1-All (<http://orkg.org/orkg/resource/R191263>)              | uses benchmark (orkgp:P38001) | Drug-Drug Interactions (<http://orkg.org/orkg/resource/R114522>)             |
| Contribution (<http://orkg.org/orkg/resource/R111215>)                   | uses benchmark (orkgp:P38001) | Custom Benchmark (<http://orkg.org/orkg/resource/R135737>)                   |
| LUBM-Q1 2-hop (<http://orkg.org/orkg/resource/R188221>)                  | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R135532>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R209029>)                 | uses benchmark (orkgp:P38001) | true (xsd:string)                                                            |
| LUBM R3F Q1 Setting 3 (<http://orkg.org/orkg/resource/R114112>)          | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R77032>)  |
| YAGO-QSC1 (<http://orkg.org/orkg/resource/R135212>)                      | uses benchmark (orkgp:P38001) | YAGO (<http://orkg.org/orkg/resource/R78321>)                                |
| DBLP-QSC1 (<http://orkg.org/orkg/resource/R135213>)                      | uses benchmark (orkgp:P38001) | DBLP (<http://orkg.org/orkg/resource/R114515>)                               |
| LUBM(1.0)-Q09 Cluster Sampling (<http://orkg.org/orkg/resource/R135327>) | uses benchmark (orkgp:P38001) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| SP2Bench SP6 HSP (<http://orkg.org/orkg/resource/R135463>)               | uses benchmark (orkgp:P38001) | SP2Bench (<http://orkg.org/orkg/resource/R135465>)                           |
| ...                                                                      | ...                           | ...                                                                          |

[Tool]
Got 27 rows and 3 columns, showing the first 10 rows below:
| subject                                              | property                             | object                                                                           |
| ---------------------------------------------------- | ------------------------------------ | -------------------------------------------------------------------------------- |
| Evaluation (<http://orkg.org/orkg/resource/R197472>) | Benchmark Performance (orkgp:P59252) | [{'text': 'nan'}] (xsd:string)                                                   |
| Evaluation (<http://orkg.org/orkg/resource/R197475>) | Benchmark Performance (orkgp:P59252) | nan (xsd:string)                                                                 |
| Evaluation (<http://orkg.org/orkg/resource/R197479>) | Benchmark Performance (orkgp:P59252) | Multi-Person Pose Estimation COC...on CrowdPose, 71.3 mAP @0.5:0.95 (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197529>) | Benchmark Performance (orkgp:P59252) | Ordinal Value Benchmark, 27% sem...al Value Benchmark, 55% accuracy (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197537>) | Benchmark Performance (orkgp:P59252) | Actor 38.04 Texas 80.27 Wisconsi...92.05 Photos 85.16 Physics 93.28 (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197598>) | Benchmark Performance (orkgp:P59252) | Multi-Task/Game Procgen w/ Rainb...ch efficiency over random search (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197483>) | Benchmark Performance (orkgp:P59252) | Multi-Person Pose Estimation COC...on CrowdPose, 71.3 mAP @0.5:0.95 (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197487>) | Benchmark Performance (orkgp:P59252) | Multi-Person Pose Estimation COC...on CrowdPose, 71.3 mAP @0.5:0.95 (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197504>) | Benchmark Performance (orkgp:P59252) | Multi-Person Pose Estimation COC...on CrowdPose, 71.3 mAP @0.5:0.95 (xsd:string) |
| Evaluation (<http://orkg.org/orkg/resource/R197508>) | Benchmark Performance (orkgp:P59252) | Multi-Person Pose Estimation COC...on CrowdPose, 71.3 mAP @0.5:0.95 (xsd:string) |
| ...                                                  | ...                                  | ...                                                                              |

[Tool]
Got 101 rows and 3 columns, showing the first 10 rows below:
| subject                                                  | property                    | object                                                                    |
| -------------------------------------------------------- | --------------------------- | ------------------------------------------------------------------------- |
| FedX (<http://orkg.org/orkg/resource/R114017>)           | Uses dataset (orkgp:P18087) | DBpedia (<http://orkg.org/orkg/resource/R114046>)                         |
| Contribution 1 (<http://orkg.org/orkg/resource/R38909>)  | Uses dataset (orkgp:P18087) | DRIVE (<http://orkg.org/orkg/resource/R41432>)                            |
| Contribution 1 (<http://orkg.org/orkg/resource/R140140>) | Uses dataset (orkgp:P18087) | DBpedia (<http://orkg.org/orkg/resource/R125897>)                         |
| Contribution 1 (<http://orkg.org/orkg/resource/R140179>) | Uses dataset (orkgp:P18087) | Gene Ontology (GO) (<http://orkg.org/orkg/resource/R140298>)              |
| Contribution 1 (<http://orkg.org/orkg/resource/R140160>) | Uses dataset (orkgp:P18087) | WN18 (filtered) (<http://orkg.org/orkg/resource/R114520>)                 |
| Contribution 1 (<http://orkg.org/orkg/resource/R140137>) | Uses dataset (orkgp:P18087) | protein-protein interactions (<http://orkg.org/orkg/resource/R114514>)    |
| Contribution 1 (<http://orkg.org/orkg/resource/R140173>) | Uses dataset (orkgp:P18087) | DBpedia OWL (<http://orkg.org/orkg/resource/R140314>)                     |
| Contribution 1 (<http://orkg.org/orkg/resource/R140214>) | Uses dataset (orkgp:P18087) | YAGO37 (<http://orkg.org/orkg/resource/R114559>)                          |
| Contribution 1 (<http://orkg.org/orkg/resource/R140182>) | Uses dataset (orkgp:P18087) | NELL-995 (<http://orkg.org/orkg/resource/R114523>)                        |
| Contribution 1 (<http://orkg.org/orkg/resource/R140155>) | Uses dataset (orkgp:P18087) | 20 million clinical notes at Stanford Hospitals and Clinics (xsd:string) |
| ...                                                      | ...                         | ...                                                                       |

[Tool]
Got 109 rows and 3 columns, showing the first 10 rows below:
| subject                                                  | property                    | object                                                                           |
| -------------------------------------------------------- | --------------------------- | -------------------------------------------------------------------------------- |
| RNTN (<http://orkg.org/orkg/resource/R150015>)           | Dataset used (orkgp:P25063) | SST (<http://orkg.org/orkg/resource/R150016>)                                    |
| Contribution 1 (<http://orkg.org/orkg/resource/R196336>) | Dataset used (orkgp:P25063) | COVID-19 Fake News Dataset (<http://orkg.org/orkg/resource/R123177>)             |
| Contribution 1 (<http://orkg.org/orkg/resource/R196658>) | Dataset used (orkgp:P25063) | COVID-19 Fake News Dataset (<http://orkg.org/orkg/resource/R196473>)             |
| Contribution 1 (<http://orkg.org/orkg/resource/R46612>)  | Dataset used (orkgp:P25063) | 4800 English, 870 German, and 15...les are collected from Wikipedia (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R46562>)  | Dataset used (orkgp:P25063) | Dataset is collected from GENIA ...gainst AIMed and GENETAG dataset (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R46474>)  | Dataset used (orkgp:P25063) | Annotated corpora is collected from the ACE and the ATB (xsd:string)             |
| Contribution 1 (<http://orkg.org/orkg/resource/R46476>)  | Dataset used (orkgp:P25063) | 2262 documents from Becker Riaz ...s containing short new sarticles (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R46478>)  | Dataset used (orkgp:P25063) | 50 Punjabi news documents (xsd:string)                                           |
| Contribution 1 (<http://orkg.org/orkg/resource/R46480>)  | Dataset used (orkgp:P25063) | Two datasets consisting of 12,03...s are collected from BBC website (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R46482>)  | Dataset used (orkgp:P25063) | User-generated web-based corpus ...orums, product review sites etc. (xsd:string) |
| ...                                                      | ...                         | ...                                                                              |

[Tool]
Got 9,929 rows and 3 columns, showing the first 10 rows below:
| subject                                                                      | property              | object                                                                                |
| ---------------------------------------------------------------------------- | --------------------- | ------------------------------------------------------------------------------------------------------------- |
| UIDCD 2003 (<http://orkg.org/orkg/resource/R203934>)                         | dataset (orkgp:P2005) | Existing foods databases (<http://orkg.org/orkg/resource/R203953>)                                            |
| Tables of Food Composition Morocco (<http://orkg.org/orkg/resource/R203339>) | dataset (orkgp:P2005) | Scientific literature (<http://orkg.org/orkg/resource/R203470>)                                               |
| LEBTAB (<http://orkg.org/orkg/resource/R203961>)                             | dataset (orkgp:P2005) | German food composition data (<http://orkg.org/orkg/resource/R204375>)                                        |
| Table of food composition Cameroon (<http://orkg.org/orkg/resource/R203693>) | dataset (orkgp:P2005) | 60 scientific papers (<http://orkg.org/orkg/resource/R203866>)                                                |
| Contribution 1 (<http://orkg.org/orkg/resource/R109145>)                     | dataset (orkgp:P2005) | http://dbpedia.org (<http://orkg.org/orkg/resource/R109214>)                                                  |
| Genome Evolutionary Analysis (<http://orkg.org/orkg/resource/R75376>)        | dataset (orkgp:P2005) | congo-gisaid_hcov-19_2020_05_05_09.fasta (<http://orkg.org/orkg/resource/R75392>)                             |
| Contribution 1 (<http://orkg.org/orkg/resource/R235544>)                     | dataset (orkgp:P2005) | Scalability in Perception for Au...mous Driving: Waymo Open Dataset (<http://orkg.org/orkg/resource/R235540>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R6126>)                       | dataset (orkgp:P2005) | INSPIRE (<http://orkg.org/orkg/resource/R6121>)                                                               |
| Contribution 1 (<http://orkg.org/orkg/resource/R6043>)                       | dataset (orkgp:P2005) | IEEE Explore Vietnamese authors (<http://orkg.org/orkg/resource/R6037>)                                       |
| Contribution 1 (<http://orkg.org/orkg/resource/R142510>)                     | dataset (orkgp:P2005) | Trafic thesaurus (<http://orkg.org/orkg/resource/R142526>)                                                    |
| ...                                                                          | ...                   | ...                                                                                |

[Tool]
Got 2 rows and 3 columns:
| subject                                                  | property               | object                                                 |
| -------------------------------------------------------- | ---------------------- | ------------------------------------------------------ |
| Contribution 1 (<http://orkg.org/orkg/resource/R255331>) | Dataset (orkgp:P96045) | 400 papers in the Computer Science domain (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R255331>) | Dataset (orkgp:P96045) | http://dit.unitn.it/~krapivin/ (xsd:string)            |

[Assistant]
Based on the previous steps, the most promising properties to link research papers (or their contributions) to benchmarks or datasets are:
- uses benchmark (orkgp:P38001)
- has benchmark (orkgp:hasBenchmark)
- Has benchmark (orkgp:P34103)
- Uses dataset (orkgp:P18087)
- Dataset used (orkgp:P25063)
- dataset (orkgp:P2005)

The entity for the Atari 2600 Frostbite dataset is: http://orkg.org/orkg/resource/R125005
There are also multiple "Benchmark Atari 2600 Frostbite" entities.

The next step is to check if any contributions or papers are linked to these entities via the above properties. Let's start by searching for contributions that use the Atari 2600 Frostbite dataset (R125005) via these properties.

I'll check for "uses benchmark" (orkgp:P38001) with the object as the Atari 2600 Frostbite dataset.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
Despite checking all relevant properties and benchmark/dataset entities, there are no direct links in the ORKG between research papers (or their contributions) and the Atari 2600 Frostbite dataset or its associated benchmarks.

Therefore, I cannot provide a SPARQL query that would return the title and ID of research papers containing a benchmark over the Atari 2600 Frostbite dataset, as there is no such data currently available in the ORKG.

I will now cancel with a detailed explanation.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The explanation is partially correct in that the query as written does not reliably return research papers containing a benchmark over the Atari 2600 Frostbite dataset. The current SPARQL query is too broad: it retrieves any entity with a property pointing to something whose label contains 'frostbite', which includes not only papers but also benchmarks and contributions, leading to many irrelevant results.

To improve, the query should:
- Restrict ?paper to actual research paper entities (e.g., instances of a 'Paper' or 'ResearchPaper' class, or those with a title).
- More precisely follow the ORKG schema for linking papers to benchmarks/datasets (e.g., via 'has benchmark', 'uses dataset', 'has contribution', etc.).
- Optionally, filter out entities that are not papers by checking for the presence of a title or a specific type.

Therefore, the output needs refinement to better target the user's question and avoid irrelevant results.

[Assistant]
Thank you for the feedback. I agree that the previous approach was too broad and did not sufficiently restrict the results to actual research papers. To refine the query, I will:

1. Restrict ?paper to entities that are likely research papers, either by type or by the presence of a title.
2. Follow the ORKG schema more closely: In ORKG, papers often have "contributions" (e.g., via orkgp:hasContribution), and contributions are linked to benchmarks or datasets (e.g., via orkgp:hasBenchmark, orkgp:P38001, orkgp:P18087, etc.).
3. Target benchmarks/datasets specifically labeled as "Atari 2600 Frostbite" or its benchmark variants.

Step 1: Find how papers are linked to contributions in ORKG (likely via orkgp:hasContribution).

[Tool]
Top 10 property alternatives:
1. contribution (orkgp:P31)
2. Contribution description (orkgp:P41880)
3. contribution facet (orkgp:P97005)
4. contributor (orkgp:P49016)
5. has contribution type (orkgp:P40065)
6. compare contribution (orkgp:compareContribution)
7. Major Contributions (orkgp:P15063)
8. Donor (orkgp:P41897)
9. include (orkgp:P20088)
10. contribution:research_aplication (orkgp:P37219)

[Tool]
Top 10 property alternatives:
1. has contribution type (orkgp:P40065)
2. contribution (orkgp:P31)
3. Contribution description (orkgp:P41880)
4. has (orkgp:P18055)
5. has (orkgp:P18022)
6. Has (orkgp:P18071)
7. has name (orkgp:P7111)
8. has stakeholder (orkgp:P41259)
9. Has participant (orkgp:P18058)
10. has reference (orkgp:P7102)

[Tool]
Top 10 property alternatives:
1. Paper (orkgp:P15330)
2. has paper (orkgp:HasPaper)
3. Paper type (orkgp:P41847)
4. type of the paper (orkgp:P91000)
5. Has ORKG Paper (orkgp:P35034)
6. paper:type (orkgp:P41745)
7. Papers (orkgp:P15336)
8. paper: Theory / Concept / Model (orkgp:P43091)
9. paper: Theory / Construct / Model (orkgp:P43142)
10. paper:journal (orkgp:P37235)

[Tool]
Got 47 rows and 3 columns, showing the first 10 rows below:
| subject                                                 | property             | object                                       |
| ------------------------------------------------------- | -------------------- | -------------------------------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R29115>) | Paper (orkgp:P15330) | 189 (<http://orkg.org/orkg/resource/R29112>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R29124>) | Paper (orkgp:P15330) | 739 (<http://orkg.org/orkg/resource/R29122>) |
| Contribution 2 (<http://orkg.org/orkg/resource/R29127>) | Paper (orkgp:P15330) | 76 (<http://orkg.org/orkg/resource/R29126>)  |
| Contribution 1 (<http://orkg.org/orkg/resource/R29134>) | Paper (orkgp:P15330) | 49 (<http://orkg.org/orkg/resource/R29132>)  |
| Contribution 1 (<http://orkg.org/orkg/resource/R29138>) | Paper (orkgp:P15330) | 238 (<http://orkg.org/orkg/resource/R29136>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R29141>) | Paper (orkgp:P15330) | 449 (<http://orkg.org/orkg/resource/R29139>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R29150>) | Paper (orkgp:P15330) | 885 (<http://orkg.org/orkg/resource/R29148>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R29192>) | Paper (orkgp:P15330) | 10 (xsd:string)                              |
| Contribution 1 (<http://orkg.org/orkg/resource/R29199>) | Paper (orkgp:P15330) | 45 (xsd:string)                              |
| Contribution 1 (<http://orkg.org/orkg/resource/R29202>) | Paper (orkgp:P15330) | 75 (xsd:string)                              |
| ...                                                     | ...                  | ...                                          |

[Tool]
Top 10 property alternatives:
1. has contribution type (orkgp:P40065)
2. contribution (orkgp:P31)
3. Contribution description (orkgp:P41880)
4. has (orkgp:P18055)
5. has (orkgp:P18022)
6. Has (orkgp:P18071)
7. has name (orkgp:P7111)
8. has stakeholder (orkgp:P41259)
9. Has participant (orkgp:P18058)
10. has reference (orkgp:P7102)

[Tool]
Top 10 property alternatives:
1. Has Citations (orkgp:P37113)
2. has source (orkgp:P15003)
3. has content (orkgp:hasContent)
4. has reference (orkgp:P7102)
5. has reference (orkgp:HasReference)
6. has entity (orkgp:HasEntity)
7. has (orkgp:P18055)
8. has (orkgp:P18022)
9. Has (orkgp:P18071)
10. has attribute (orkgp:P52014)

[Tool]
Top 10 property alternatives:
1. contribution (orkgp:P31)
2. Contribution description (orkgp:P41880)
3. contribution facet (orkgp:P97005)
4. contributor (orkgp:P49016)
5. has contribution type (orkgp:P40065)
6. compare contribution (orkgp:compareContribution)
7. Major Contributions (orkgp:P15063)
8. Donor (orkgp:P41897)
9. include (orkgp:P20088)
10. contribution:research_aplication (orkgp:P37219)

[Tool]
Top 10 property alternatives:
1. Has Citations (orkgp:P37113)
2. has source (orkgp:P15003)
3. has content (orkgp:hasContent)
4. has reference (orkgp:P7102)
5. has reference (orkgp:HasReference)
6. has entity (orkgp:HasEntity)
7. has (orkgp:P18055)
8. has (orkgp:P18022)
9. Has (orkgp:P18071)
10. has attribute (orkgp:P52014)

[Tool]
Top 10 property alternatives:
1. Major Contributions (orkgp:P15063)
2. contribution (orkgp:P31)
3. Main Result (orkgp:P15190)
4. Contribution description (orkgp:P41880)
5. Main purpose (orkgp:P15524)
6. contributor (orkgp:P49016)
7. Main Products (orkgp:P25020)
8. Main question (orkgp:P15271)
9. contribution:research_aplication (orkgp:P37219)
10. Related work (orkgp:P41612)

[Tool]
Top 10 property alternatives:
1. has contribution type (orkgp:P40065)
2. contribution (orkgp:P31)
3. Contribution description (orkgp:P41880)
4. has (orkgp:P18055)
5. has (orkgp:P18022)
6. Has (orkgp:P18071)
7. has name (orkgp:P7111)
8. has stakeholder (orkgp:P41259)
9. Has participant (orkgp:P18058)
10. has reference (orkgp:P7102)

[Tool]
Top 10 property alternatives:
1. paper:journal (orkgp:P37235)
2. paper:year (orkgp:P97066)
3. paper:abstract (orkgp:P97074)
4. paper:author (orkgp:P43083)
5. paper:published_in (orkgp:P18092)
6. Journals/ (papers) (orkgp:P15331)
7. paper:type (orkgp:P41745)
8. Papers (orkgp:P15336)
9. paper: publication_year (orkgp:P43084)
10. paper:puplication_year (orkgp:P30000)

[Assistant]
It appears that in ORKG, the link between a paper and its contribution is not via a property named "hasContribution" but rather through the contribution entity itself, which may have a property pointing back to the paper (e.g., Paper (orkgp:P15330)), or the contribution may be described as a "Main Contribution".

Given this, a robust approach is:
- Find contributions linked to a benchmark entity for "Atari 2600 Frostbite".
- For each such contribution, find the associated paper (if any) via the "Paper" property (orkgp:P15330).
- Retrieve the paper's title (wikidata:P1476).

Let's construct a SPARQL query that:
- Finds contributions (?contribution) with a property (e.g., uses benchmark, has benchmark, etc.) pointing to any of the "Benchmark Atari 2600 Frostbite" entities.
- For each such contribution, finds the associated paper via orkgp:P15330.
- Optionally, retrieves the paper's title.

I'll use all the known "Benchmark Atari 2600 Frostbite" IRIs as possible objects.

Here is the refined SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is well-structured and follows the ORKG schema for linking papers, contributions, and benchmarks. It also correctly returns the paper ID and optionally the title. However, the query returns no results, which may be due to an overly restrictive VALUES clause for the Frostbite benchmark IRIs. It is possible that the relevant benchmark IRIs are missing or that the dataset is referenced under a different IRI. Consider broadening the search for the Frostbite dataset by using a FILTER with regex on labels or by querying for all benchmarks with labels containing 'Frostbite' or 'Atari 2600 Frostbite'. This would make the query more robust and likely to retrieve relevant results.

[Assistant]
Thank you for the feedback. To address the issue of missing or variant IRIs for the Frostbite benchmark, I will broaden the search by:

- Dynamically identifying all benchmark entities whose rdfs:label contains "Frostbite" (case-insensitive).
- Then, retrieving contributions that reference any of these benchmark entities via the relevant benchmark properties.
- Finally, linking those contributions to their associated papers and retrieving the paper ID and title.

Here is the refined SPARQL query that implements this approach:

[Tool] Stopped generation process


Failed to generate SPARQL query

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

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

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

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

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

[User] What are the metrics of evaluation over the DuIE dataset?

[Assistant]
Let's break down the question:

1. The user wants to know the "metrics of evaluation" used "over the DuIE dataset".
2. So, we need to identify:
   - The entity for the "DuIE dataset".
   - The properties related to "metrics of evaluation" (likely evaluation metrics, performance metrics, etc.).
   - The relationship between the DuIE dataset and its evaluation metrics.

Step 1: Search for the "DuIE dataset" entity in ORKG.
I will search for the entity "DuIE dataset" in the ORKG to find its identifier and ensure we are referencing the correct dataset.

Let's proceed to search for "DuIE dataset".


Failed to generate SPARQL query

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

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

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

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

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

[User] What is the best performing model benchmarking the iNaturalist 2018 dataset in terms of Top-1 Accuracy metric?

[Assistant]
Let's break down the question:

1. We are looking for models that have benchmarked the "iNaturalist 2018" dataset.
2. We are interested in the "Top-1 Accuracy" metric.
3. We want to find the best performing model, i.e., the one with the highest Top-1 Accuracy.

Step 1: Search for the "iNaturalist 2018" dataset entity in ORKG.
I will search for the entity "iNaturalist 2018" in the ORKG knowledge graph to identify its IRI.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?model ?modelLabel WHERE {
  ?contribution orkgp:P18036 <http://orkg.org/orkg/resource/R116678> ; orkgp:METHOD ?model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel
  }
}

Got 5 rows and 2 columns:
| model                                                                      | modelLabel                                    |
| -------------------------------------------------------------------------- | --------------------------------------------- |
| BioLinkBERT-Large (<http://orkg.org/orkg/resource/R191277>)                | BioLinkBERT-Large (xsd:string)                |
| PubMedBERT-LARGE (<http://orkg.org/orkg/resource/R191287>)                 | PubMedBERT-LARGE (xsd:string)                 |
| BioELECTRA-Base — Saama Research (<http://orkg.org/orkg/resource/R191292>) | BioELECTRA-Base — Saama Research (xsd:string) |
| BioBERT (cased) (<http://orkg.org/orkg/resource/R191295>)                  | BioBERT (cased) (xsd:string)                  |
| ClinicalBERT (cased) (<http://orkg.org/orkg/resource/R191300>)             | ClinicalBERT (cased) (xsd:string)             |

This query will return the IRIs and labels of all models that have been evaluated on the GAD dataset according to the ORKG.

[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 models are being evaluated on the GAD dataset?

[Assistant]
Let's break down the question:

1. We are looking for "models" (likely machine learning models or methods).
2. These models are being "evaluated" (so, evaluated on some dataset).
3. The dataset in question is "GAD".

Step 1: Identify the entity for the "GAD dataset" in the ORKG.
I'll search for "GAD" as an entity in the ORKG.

[Tool]
Top 10 entity alternatives:
1. GAD (<http://orkg.org/orkg/resource/R116678>): has type Dataset
2. Benchmark GAD (<http://orkg.org/orkg/resource/R129610>): has type Benchmark
3. GADA (<http://orkg.org/orkg/resource/R33805>)
4. Gadiformes (<http://orkg.org/orkg/resource/R34078>)
5. Gadolinium (<http://orkg.org/orkg/resource/R224158>)
6. Yojana Gadiya (<http://orkg.org/orkg/resource/R71718>): has type Author
7. Gadag Schist Belt (GSB) of Karnataka, Southern India (<http://orkg.org/orkg/resource/R141098>)
8. Gade4all: Developing multi-platf...ges and model-driven engineering (<http://orkg.org/orkg/resource/R25559>): has type Paper
9. Spatial distribution of altered ...yperspectral remote sensing data (<http://orkg.org/orkg/resource/R140823>): has type Paper
10. Mapping the spatial distribution...t Belt (GSB) using Hyperion data (<http://orkg.org/orkg/resource/R140822>): In this work authors attempt to delineate and map the spatial di...B) as these alteration zones are an indicator of mineralisation., has type Problem

[Assistant]
The top result for "GAD" is an entity with IRI <http://orkg.org/orkg/resource/R116678> and type Dataset. This is likely the "GAD dataset" referred to in the question.

Next, I need to find which models are being evaluated on this dataset. This involves:
- Finding evaluations or experiments involving this dataset.
- Identifying the models used in those evaluations.

I'll search for properties related to "evaluation", "model", or "method" for the GAD dataset entity.

[Tool]
No property items found

No other items found

[Tool]
No property items found

No other items found

[Tool]
No property items found

No other items found

[Tool]
Got 7 rows and 3 columns:
| subject                                                             | property                             | object                                        |
| ------------------------------------------------------------------- | ------------------------------------ | --------------------------------------------- |
| Benchmark GAD (<http://orkg.org/orkg/resource/R129610>)             | has dataset (orkgp:HAS_DATASET)      | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>)          | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>)          | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>)          | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>)          | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>)          | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>) |

[Assistant]
I found several contributions and a benchmark that reference the GAD dataset using the property "On evaluation dataset" (orkgp:P18036). These contributions likely contain information about models evaluated on GAD.

Next, I need to check what models or methods are associated with these contributions. I'll start by searching for properties related to "model" or "method" for one of these contributions, e.g., <http://orkg.org/orkg/resource/R191281>.

[Tool]
Top 10 property alternatives:
1. method (orkgp:METHOD)
2. metric (orkgp:HAS_METRIC)
3. Micro F1 (orkgp:P58035)
4. research problem (orkgp:P32)
5. On evaluation dataset (orkgp:P18036)
6. Has evaluation task (orkgp:P41318)

No other items found

[Tool]
Got 12 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                             | object                                                            |
| ---------------------------------------------------------- | ------------------------------------ | ----------------------------------------------------------------- |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | research problem (orkgp:P32)         | Representation Learning (<http://orkg.org/orkg/resource/R191274>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | method (orkgp:METHOD)                | BioLinkBERT-Large (<http://orkg.org/orkg/resource/R191277>)       |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | Has evaluation task (orkgp:P41318)   | Relation Extraction (<http://orkg.org/orkg/resource/R164358>)     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | code repository (orkgp:P49000)       | https://github.com/michiyasunaga/LinkBERT (xsd:string)            |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | Micro F1 (orkgp:P58035)              | 82.74 (xsd:string)                                                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | rdf:type                             | Contribution (orkgc:Contribution)                                 |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | rdfs:label                           | Contribution1-RE (xsd:string)                                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191281>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)                |
| ...                                                        | ...                                  | ...                                                               |

[Tool]
Got 12 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                             | object                                                                     |
| ---------------------------------------------------------- | ------------------------------------ | -------------------------------------------------------------------------- |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | research problem (orkgp:P32)         | Representation Learning (<http://orkg.org/orkg/resource/R191274>)          |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)                         |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | method (orkgp:METHOD)                | BioELECTRA-Base — Saama Research (<http://orkg.org/orkg/resource/R191292>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                              |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | Has evaluation task (orkgp:P41318)   | Relation Extraction (<http://orkg.org/orkg/resource/R164358>)              |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | code repository (orkgp:P49000)       | https://github.com/kamalkraj/BioELECTRA (xsd:string)                       |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | Micro F1 (orkgp:P58035)              | 81.44 (xsd:string)                                                         |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | rdf:type                             | Contribution (orkgc:Contribution)                                          |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | rdfs:label                           | Contribution1-RE (xsd:string)                                              |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191291>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)                         |
| ...                                                        | ...                                  | ...                                                                        |

[Tool]
Got 12 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                             | object                                                            |
| ---------------------------------------------------------- | ------------------------------------ | ----------------------------------------------------------------- |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | research problem (orkgp:P32)         | Representation Learning (<http://orkg.org/orkg/resource/R191274>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | method (orkgp:METHOD)                | BioBERT (cased) (<http://orkg.org/orkg/resource/R191295>)         |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | Has evaluation task (orkgp:P41318)   | Relation Extraction (<http://orkg.org/orkg/resource/R164358>)     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | code repository (orkgp:P49000)       | https://github.com/dmis-lab/biobert (xsd:string)                  |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | Micro F1 (orkgp:P58035)              | 79.79 (xsd:string)                                                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | rdf:type                             | Contribution (orkgc:Contribution)                                 |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | rdfs:label                           | Contribution1-RE (xsd:string)                                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191294>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)                |
| ...                                                        | ...                                  | ...                                                               |

[Tool]
Got 12 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                             | object                                                            |
| ---------------------------------------------------------- | ------------------------------------ | ----------------------------------------------------------------- |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | research problem (orkgp:P32)         | Representation Learning (<http://orkg.org/orkg/resource/R191274>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | method (orkgp:METHOD)                | ClinicalBERT (cased) (<http://orkg.org/orkg/resource/R191300>)    |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | Has evaluation task (orkgp:P41318)   | Relation Extraction (<http://orkg.org/orkg/resource/R164358>)     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | code repository (orkgp:P49000)       | https://github.com/EmilyAlsentzer/clinicalBERT (xsd:string)       |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | Micro F1 (orkgp:P58035)              | 76.91 (xsd:string)                                                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | rdf:type                             | Contribution (orkgc:Contribution)                                 |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | rdfs:label                           | Contribution1-RE (xsd:string)                                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191299>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)                |
| ...                                                        | ...                                  | ...                                                               |

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                                    | property                             | object                                                            |
| ---------------------------------------------------------- | ------------------------------------ | ----------------------------------------------------------------- |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | research problem (orkgp:P32)         | Representation Learning (<http://orkg.org/orkg/resource/R191274>) |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | method (orkgp:METHOD)                | PubMedBERT-LARGE (<http://orkg.org/orkg/resource/R191287>)        |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | Has evaluation task (orkgp:P41318)   | Relation Extraction (<http://orkg.org/orkg/resource/R164358>)     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | Micro F1 (orkgp:P58035)              | 81.77 (xsd:string)                                                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | rdf:type                             | Contribution (orkgc:Contribution)                                 |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | rdfs:label                           | Contribution1-RE (xsd:string)                                     |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)                |
| Contribution1-RE (<http://orkg.org/orkg/resource/R191286>) | On evaluation dataset (orkgp:P18036) | DDI (<http://orkg.org/orkg/resource/R116705>)                     |
| ...                                                        | ...                                  | ...                                                               |

[Tool]
Got 9 rows and 3 columns:
| subject                                                             | property                             | object                                                       |
| ------------------------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------ |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | metric (orkgp:HAS_METRIC)            | Micro F1 (<http://orkg.org/orkg/resource/R114480>)           |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | On evaluation dataset (orkgp:P18036) | GAD (<http://orkg.org/orkg/resource/R116678>)                |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | on task (orkgp:P18035)               | Relation Extraction (<http://orkg.org/orkg/resource/R41313>) |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | Micro F1 (orkgp:P58035)              | 82.74 (xsd:string)                                           |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | rdf:type                             | R38248 (orkgc:C5004)                                         |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | rdfs:label                           | Task Dataset Metric Score (xsd:string)                       |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | On evaluation dataset (orkgp:P18036) | ChemProt (<http://orkg.org/orkg/resource/R116681>)           |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | On evaluation dataset (orkgp:P18036) | DDI (<http://orkg.org/orkg/resource/R116705>)                |
| Task Dataset Metric Score (<http://orkg.org/orkg/resource/R191282>) | rdf:type                             | orkgc:Resource                                               |

[Assistant]
From the above exploration, I see that the models evaluated on the GAD dataset are linked via the property orkgp:METHOD in the contributions that reference GAD as their evaluation dataset.

To answer the question, I will write a SPARQL query that:
- Finds all contributions where the property orkgp:P18036 ("On evaluation dataset") points to the GAD dataset (<http://orkg.org/orkg/resource/R116678>).
- For each such contribution, retrieves the model/method used via orkgp:METHOD.
- Returns the model IRIs and optionally their labels.

Here is the SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs and optional labels of all models evaluated on the GAD dataset, following the user's request. The query uses the appropriate properties and returns identifiers as required. The answer is complete and accurate based on the execution result.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?contribution ?code_link ?label WHERE {
  ?contribution orkgp:HAS_MODEL <http://orkg.org/orkg/resource/R126452> .
  ?contribution orkgp:HAS_SOURCE_CODE ?code_link .
  OPTIONAL {
    ?contribution rdfs:label ?label
  }
}

There is one contribution (likely a paper) in ORKG that has utilized the CvT-21 (384 res) model and provides code links:

- Contribution: http://orkg.org/orkg/resource/R134982 (label: Contribution  CvT-21 (384 res))
  - Code links:
    - https://github.com/lucidrains/vit-pytorch
    - https://github.com/rishikksh20/convolution-vision-transformers

[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] Provide a list of papers that have utilized the CvT-21 (384 res) model and include the links to their code?

[Assistant]
First, I need to identify the entity representing the "CvT-21 (384 res)" model in the ORKG knowledge graph. Then, I will look for papers that have utilized this model and find the property that links papers to their code repositories.

Step 1: Search for the entity "CvT-21 (384 res)" in ORKG.

[Tool]
Top 10 entity alternatives:
1. CvT-21 (384 res) (<http://orkg.org/orkg/resource/R126452>): has type Model
2. Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>): has type Contribution
3. CvT-21 (384 res, ImageNet-22k pretrain) (<http://orkg.org/orkg/resource/R126416>): has type Model
4. Contribution  CvT-21 (384 res, ImageNet-22k pretrain) (<http://orkg.org/orkg/resource/R134979>): has type Contribution
5. CvT-13 (384 res) (<http://orkg.org/orkg/resource/R126461>): has type Model
6. Contribution  CvT-13 (384 res) (<http://orkg.org/orkg/resource/R134986>): has type Contribution
7. CeiT-S (384 finetune res) (<http://orkg.org/orkg/resource/R126259>): has type Model
8. CeiT-T (384 finetune res) (<http://orkg.org/orkg/resource/R126571>): has type Model
9. Swin-B (384x384 res) (<http://orkg.org/orkg/resource/R126428>): has type Model
10. Contribution  CeiT-S (384 finetune res) (<http://orkg.org/orkg/resource/R134704>): has type Contribution

[Assistant]
I have identified the entity for "CvT-21 (384 res)" as http://orkg.org/orkg/resource/R126452, which is of type Model.

Next, I need to find papers that have utilized this model. Typically, this involves finding a property like "uses model" or "utilizes" that links papers to models. I will search for relevant properties related to "uses model".

[Tool]
Top 10 property alternatives:
1. model (orkgp:P20072)
2. model (orkgp:P1004)
3. model type (orkgp:P71176)
4. models (orkgp:P71209)
5. Models (orkgp:P43024)
6. Proposed model (orkgp:P59015)
7. uses Micro-Models (orkgp:P44136)
8. has model (orkgp:hasModel)
9. Has model (orkgp:P9018)
10. has model (orkgp:HAS_MODEL)

[Assistant]
There are several properties related to "model" such as orkgp:P20072, orkgp:P1004, orkgp:P71176, etc. To determine which property is used to indicate that a paper utilizes a model, I will check which of these properties link papers to the "CvT-21 (384 res)" model entity.

I will list triples where the object is http://orkg.org/orkg/resource/R126452 (CvT-21 (384 res)) to see which properties are used.

[Tool]
Got 1 row and 3 columns:
| subject                                                                  | property                    | object                                                     |
| ------------------------------------------------------------------------ | --------------------------- | ---------------------------------------------------------- |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | has model (orkgp:HAS_MODEL) | CvT-21 (384 res) (<http://orkg.org/orkg/resource/R126452>) |

[Assistant]
The property "has model" (orkgp:HAS_MODEL) is used to link contributions (likely from papers) to the "CvT-21 (384 res)" model.

Next, I need to find papers (or their contributions) that have this property pointing to the model, and then find the property that links these contributions or papers to their code repositories.

I will now search for properties related to "code" to identify the correct property for code links.

[Tool]
Top 10 property alternatives:
1. code (orkgp:P16033)
2. coding (orkgp:P59061)
3. code List (orkgp:P4005)
4. source code (orkgp:P4077)
5. CODEN (orkgp:P74032)
6. has source code (orkgp:HAS_SOURCE_CODE)
7. programming language (orkgp:P21)
8. encodes (orkgp:P20179)
9. Inter-code agreement (orkgp:P18074)
10. encoding (<http://orkg.org/orkg/predicate/SCHEMAORG:encoding>)

[Assistant]
There are several properties related to code, such as "code" (orkgp:P16033), "source code" (orkgp:P4077), and "has source code" (orkgp:HAS_SOURCE_CODE). To determine which is used for code links in contributions, I will check which of these properties are used for the contribution entity http://orkg.org/orkg/resource/R134982 (Contribution  CvT-21 (384 res)).

I will list all triples where the subject is http://orkg.org/orkg/resource/R134982.

[Tool]
Got 8 rows and 3 columns:
| subject                                                                  | property                                | object                                                                      |
| ------------------------------------------------------------------------ | --------------------------------------- | --------------------------------------------------------------------------- |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | research problem (orkgp:P32)            | Image Classification (<http://orkg.org/orkg/resource/R38570>)               |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/vit-pytorch (xsd:string)                      |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark ImageNet (<http://orkg.org/orkg/resource/R134983>)                |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | has model (orkgp:HAS_MODEL)             | CvT-21 (384 res) (<http://orkg.org/orkg/resource/R126452>)                  |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | rdf:type                                | Contribution (orkgc:Contribution)                                           |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | rdfs:label                              | Contribution 	CvT-21 (384 res) (xsd:string)                                 |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/rishikksh20/convolution-vision-transformers (xsd:string) |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | rdf:type                                | orkgc:Resource                                                              |

[Assistant]
The property "has source code" (orkgp:HAS_SOURCE_CODE) is used to link contributions to their code repositories. The contribution "Contribution  CvT-21 (384 res)" has two code links.

Now, to answer the question, I need to:
- Find all contributions (or papers) that have "has model" (orkgp:HAS_MODEL) pointing to "CvT-21 (384 res)" (http://orkg.org/orkg/resource/R126452).
- For each such contribution, get the code links via "has source code" (orkgp:HAS_SOURCE_CODE).
- Optionally, retrieve the paper or contribution label.

I will now construct the SPARQL query to retrieve this information.

[Tool]
Got 2 rows and 3 columns:
| contribution                                                             | code_link                                                                   | label                                       |
| ------------------------------------------------------------------------ | --------------------------------------------------------------------------- | ------------------------------------------- |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | https://github.com/lucidrains/vit-pytorch (xsd:string)                      | Contribution 	CvT-21 (384 res) (xsd:string) |
| Contribution  CvT-21 (384 res) (<http://orkg.org/orkg/resource/R134982>) | https://github.com/rishikksh20/convolution-vision-transformers (xsd:string) | Contribution 	CvT-21 (384 res) (xsd:string) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all contributions that have utilized the CvT-21 (384 res) model and provides their associated code links, as well as optionally including the label for each contribution. The query returns the actual IRIs for the contributions, as required, and does not include any unnecessary post-processing or unsupported features. The answer is accurate and complete for the user's question.