PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?coauthor ( COUNT( DISTINCT ?pub ) AS ?numPapers ) WHERE {
  ?pub dblp:authoredBy <https://dblp.org/pid/60/6520> .
  ?pub dblp:authoredBy ?coauthor .
  FILTER ( ?coauthor != <https://dblp.org/pid/60/6520> )
}
GROUP BY ?coauthor
ORDER BY DESC ( ?numPapers )
LIMIT 1

The most frequent co-author of K. Wang is Manfred B. Wischnewsky. They have 3 research papers together.

Name the most frequent co-author of K. Wang and how many research papers do they have together?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Name the most frequent co-author of K. Wang and how many research papers do they have together?

[Assistant]
To answer this question, I need to find the co-authors of K. Wang and count how many research papers each co-author has together with K. Wang. Then I will identify the most frequent co-author and the number of papers they have together.

Step 1: Search for the entity representing "K. Wang" in the dblp knowledge graph.

[Tool]
Top 10 entity alternatives:
1. K. Wang (disambiguation), also known as K. Wang (<https://dblp.org/pid/60/6520>)
2. K. K. Wang, also known as K. K. Wang (<https://dblp.org/pid/59/3787>)
3. Kun Wang 0005, also known as Kun Wang (<https://dblp.org/pid/05/1958-5>)
4. Ke Wang 0003, also known as Ke Wang (<https://dblp.org/pid/181/2613-3>)
5. Kehao Wang 0002, also known as Kehao Wang (<https://dblp.org/pid/62/9365-2>)
6. Kai Wang 0012, also known as Kai Wang (<https://dblp.org/pid/78/2022-12>)
7. Kai Wang 0007, also known as Kai Wang (<https://dblp.org/pid/78/2022-7>)
8. Kai Wang 0052, also known as Kai Wang (<https://dblp.org/pid/78/2022-52>)
9. Kehao Wang 0001, also known as Kehao Wang (<https://dblp.org/pid/62/9365>)
10. Kai Wang 0014, also known as Kai Wang (<https://dblp.org/pid/78/2022-14>)

[Assistant]
I found multiple entities for "K. Wang" in the dblp knowledge graph. The first one is "K. Wang (disambiguation)" with the IRI <https://dblp.org/pid/60/6520>. I will use this entity to find the co-authors and count the number of papers they have together.

Step 2: Find co-authors of K. Wang and count the number of papers they have together.

[Tool]
Got 1 row and 2 columns:
| coauthor                                                | numPapers   |
| ------------------------------------------------------- | ----------- |
| Manfred B. Wischnewsky (<https://dblp.org/pid/29/5360>) | 3 (xsd:int) |

[Tool]
Got 1 row and 1 column:
| coauthorName           |
| ---------------------- |
| Manfred B. Wischnewsky |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?paper ?numAuthors WHERE {
  VALUES ?paper {
    <https://dblp.org/rec/journals/tcyb/MaB21> <https://dblp.org/rec/conf/etfa/Missal0H07>
  }
  ?paper dblp:authoredBy ?author .
  {
    SELECT ?paper ( COUNT( DISTINCT ?author ) AS ?numAuthors ) WHERE {
      ?paper dblp:authoredBy ?author .
    }
    GROUP BY ?paper
  }
}

Got 5 rows and 2 columns:
| paper                                                                                | numAuthors  |
| ----------------------------------------------------------------------------------------------------------------- | ----------- |
| Dirk Missal et al.: Hierarchical... design and verification. (2007) (<https://dblp.org/rec/conf/etfa/Missal0H07>) | 3 (xsd:int) |
| Dirk Missal et al.: Hierarchical... design and verification. (2007) (<https://dblp.org/rec/conf/etfa/Missal0H07>) | 3 (xsd:int) |
| Dirk Missal et al.: Hierarchical... design and verification. (2007) (<https://dblp.org/rec/conf/etfa/Missal0H07>) | 3 (xsd:int) |
| Litao Ma and Wei Bian: A Novel M...uted Convex Optimization. (2021) (<https://dblp.org/rec/journals/tcyb/MaB21>)  | 2 (xsd:int) |
| Litao Ma and Wei Bian: A Novel M...uted Convex Optimization. (2021) (<https://dblp.org/rec/journals/tcyb/MaB21>)  | 2 (xsd:int) |

The paper 'Hierarchical distributed controllers - design and verification' has more co-authors (3) compared to 'A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization' which has 2 co-authors.

Between 'A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization' and 'Hierarchical distributed controllers - design and verification', which paper has more number of co-authors?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Between 'A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization' and 'Hierarchical distributed controllers - design and verification', which paper has more number of co-authors?

[Assistant]
To answer the question, I need to find the number of co-authors for each of the two papers:

1. "A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization"
2. "Hierarchical distributed controllers - design and verification"

I will first search for the entities (IRIs) of these two papers in the dblp knowledge graph by their exact titles. Then, I will count the number of authors for each paper using the property dblp:authoredBy. Finally, I will compare the counts to determine which paper has more co-authors.

[Tool]
Top 10 entity alternatives:
1. Litao Ma and Wei Bian: A Novel M...uted Convex Optimization. (2021) (<https://dblp.org/rec/journals/tcyb/MaB21>)
2. Xinrui Jiang et al.: A penalty-l...uted convex optimization. (2020) (<https://dblp.org/rec/journals/ijon/JiangQX20>)
3. Qingshan Liu et al.: A Collectiv...Constrained Optimization. (2017) (<https://dblp.org/rec/journals/tnn/LiuYW17>)
4. Na Liu and Sitian Qin: A Novel N...seudoconvex Optimization. (2019) (<https://dblp.org/rec/journals/tcyb/LiuQ19>)
5. Wenwen Jia et al.: A collective ...constrained optimization. (2024) (<https://dblp.org/rec/journals/nn/JiaHQ24>)
6. You Zhao et al.: Novel projectio...ined convex optimization. (2022) (<https://dblp.org/rec/journals/nn/ZhaoLH22>)
7. Guocheng Li et al.: A Neurodynam...ine Equality Constraints. (2022) (<https://dblp.org/rec/journals/access/LiLQF22>)
8. Yangxia Li et al.: A p-power neu...d nonconvex optimization. (2024) (<https://dblp.org/rec/journals/cnsns/LiX0CA24>)
9. Zheng Yan et al.: A Collective N...ined Global Optimization. (2017) (<https://dblp.org/rec/journals/tnn/YanFW17>)
10. Shaofu Yang et al.: A Collaborat...Distributed Optimization. (2018) (<https://dblp.org/rec/journals/tnn/YangLW18>)

[Tool]
Top 10 entity alternatives:
1. Dirk Missal et al.: Hierarchical... design and verification. (2007) (<https://dblp.org/rec/conf/etfa/Missal0H07>)
2. Albert Fleischmann: Distributed ...esign and implementation. (1994) (<https://dblp.org/rec/books/daglib/0074061>)
3. Karthik Murthy et al.: Design an...n of Distributed Phasers. (2016) (<https://dblp.org/rec/conf/europar/MurthyPMCM16>)
4. Diego Marmsoler: Hierarchical Sp...tectural Design Patterns. (2018) (<https://dblp.org/rec/conf/fase/Marmsoler18>)
5. Pradeep Kumar Sinha: Distributed...ms - concepts and design. (1996) (<https://dblp.org/rec/books/daglib/0085461>)
6. Christina Nissen et al.: Trackin... - Design and Evaluation. (2024) (<https://dblp.org/rec/conf/evoteid/NissenKVFH24>)
7. Luis Guarin et al.: Passenger Sh... Design and Verification. (2014) (<https://dblp.org/rec/conf/hci/GuarinHV14>)
8. Jongwoo Kim et al.: Design and v...controllers for airships. (2003) (<https://dblp.org/rec/conf/iros/KimKK03>)
9. Wenchao Li et al.: Design and ve...rate distributed systems. (2015) (<https://dblp.org/rec/conf/memocode/LiGS15>)
10. Michele Bugliesi and Paolo Modes... Design and Verification. (2010) (<https://dblp.org/rec/conf/ifip1-7/BugliesiM10>)

[Assistant]
I found the following two papers matching the titles:

1. "A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization" with IRI: <https://dblp.org/rec/journals/tcyb/MaB21>
2. "Hierarchical distributed controllers - design and verification" with IRI: <https://dblp.org/rec/conf/etfa/Missal0H07>

Next, I will count the number of co-authors for each paper using the property dblp:authoredBy.

[Tool]
Got 1 row and 1 column:
| numAuthors  |
| ----------- |
| 2 (xsd:int) |

[Tool]
Got 1 row and 1 column:
| numAuthors  |
| ----------- |
| 3 (xsd:int) |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT DISTINCT ?coauthor ?coauthorName ?affiliation WHERE {
  ?pub dblp:authoredBy <https://dblp.org/pid/286/6829> ; dblp:authoredBy ?coauthor ; dblp:yearOfPublication ?year .
  FILTER ( ?year >= YEAR( NOW () ) - 2 && ?coauthor != <https://dblp.org/pid/286/6829> ) ?coauthor dblp:creatorName ?coauthorName .
  OPTIONAL {
    ?coauthor dblp:primaryAffiliation ?affiliation
  }
}

There are no publications by Andersen, N. Engell (Niels Engell Andersen) in the last 2 years in the dblp knowledge graph. Therefore, there are no co-authors to list for this period.

List the co-authors of Andersen, N. Engell in the last 2 years and where are they affiliated.

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 co-authors of Andersen, N. Engell in the last 2 years and where are they affiliated.

[Assistant]
To answer this question, I need to:

1. Identify the author entity for "Andersen, N. Engell" in the dblp knowledge graph.
2. Find publications authored by Andersen, N. Engell in the last 2 years.
3. Identify co-authors of these publications.
4. Retrieve the affiliations of these co-authors.

I will start by searching for the author entity for "Andersen, N. Engell".

[Tool]
Top 10 entity alternatives:
1. Niels Engell Andersen, also known as Niels Engell Andersen (<https://dblp.org/pid/286/6829>)
2. Terje N. Andersen, also known as Terje N. Andersen (<https://dblp.org/pid/95/966>)
3. Frederik N. Andersen, also known as Frederik N. Andersen (<https://dblp.org/pid/377/2374>)
4. Bo N. Andersen, also known as Bo N. Andersen (<https://dblp.org/pid/287/8305>)
5. Njål Andersen, also known as Njål Andersen (<https://dblp.org/pid/279/8194>)
6. Nikolaj Andersen, also known as Nikolaj Andersen (<https://dblp.org/pid/121/4463>)
7. Nils Andersen, also known as Nils Andersen (<https://dblp.org/pid/13/5382>)
8. Patrick N. Andersen, also known as Patrick N. Andersen (<https://dblp.org/pid/347/2463>)
9. Nele Andersen, also known as Nele Andersen (<https://dblp.org/pid/118/4019>)
10. Neal Andersen, also known as Neal Andersen (<https://dblp.org/pid/213/9224>)

[Assistant] I found the author entity for "Niels Engell Andersen" which matches "Andersen, N. Engell" at https://dblp.org/pid/286/6829. Now I will find publications authored by this author in the last 2 years and then find their co-authors and their affiliations.

[Tool] Got no rows and 3 columns

[Assistant]
The query returned no co-authors for publications by Niels Engell Andersen in the last 2 years. This could mean that either there are no publications by this author in the last 2 years or the co-authors are not linked properly.

I will check if there are any publications by this author in the last 2 years first.

[Tool] Got no rows and 3 columns

[Assistant]
There are no publications by Niels Engell Andersen in the last 2 years in the dblp knowledge graph. Therefore, there are no co-authors to list for this period.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
ASK WHERE {
  ?pub dblp:authoredBy <https://dblp.org/pid/p/JianPei> ; dblp:publishedIn "ICDM" .
}

The author Jian Pei has published in the venue ICDM. Therefore, the answer to the question 'Did the author Jian Pei not publish in ICDM?' is No.

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Did the author Jian Pei not publish in ICDM?

[Assistant]
To answer this question, I need to check if there exists any publication authored by Jian Pei that was published in the venue "ICDM". If such a publication exists, the answer to the question "Did the author Jian Pei not publish in ICDM?" would be "No" (meaning Jian Pei did publish in ICDM). If no such publication exists, the answer would be "Yes" (meaning Jian Pei did not publish in ICDM).

Step 1: Find the entity IRI for the author "Jian Pei".
Step 2: Check if there is any publication authored by Jian Pei with the venue "ICDM".

I will start by searching for the author entity for "Jian Pei".

[Tool]
Top 10 entity alternatives:
1. Jian Pei 0001, also known as Jian Pei (<https://dblp.org/pid/p/JianPei>)
2. Jian Pei 0002, also known as Jian Pei (<https://dblp.org/pid/315/0931-2>)
3. Jian Pei 0003, also known as Jian Pei (<https://dblp.org/pid/315/0931-3>)
4. Jian Pei (disambiguation), also known as Jian Pei (<https://dblp.org/pid/315/0931>)
5. Pei Jiang 0006, also known as Pei Jiang (<https://dblp.org/pid/237/1412>)
6. Pei Jiang 0003, also known as Pei Jiang (<https://dblp.org/pid/88/8012-3>)
7. Pei Jiang 0010, also known as Pei Jiang (<https://dblp.org/pid/388/2248>)
8. Pei Jiang 0009, also known as Pei Jiang (<https://dblp.org/pid/384/1858>)
9. Pei Jiang 0008, also known as Pei Jiang (<https://dblp.org/pid/283/3228>)
10. Pei Jiang 0007, also known as Pei Jiang (<https://dblp.org/pid/281/0727>)

[Assistant]
I found multiple entities for "Jian Pei" in the dblp knowledge graph. The most likely correct entity for the author Jian Pei is <https://dblp.org/pid/p/JianPei>.

Next, I will check if there exists any publication authored by Jian Pei (using the entity <https://dblp.org/pid/p/JianPei>) that was published in the venue "ICDM".

[Tool] True

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?venue WHERE {
  <https://dblp.org/rec/conf/isgv/ZouWW021> dblp:publishedIn ?venue .
}

Got 1 row and 1 column:
| venue |
| ----- |
| ISGV  |

The publication 'Coverless Video Steganography Based on Inter Frame Combination' was published in the venue 'ISGV'.

'Coverless Video Steganography Based on Inter Frame Combination' was published in which venue?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] 'Coverless Video Steganography Based on Inter Frame Combination' was published in which venue?

[Assistant] First, I will search for the publication entity in the dblp knowledge graph by its exact title "Coverless Video Steganography Based on Inter Frame Combination" to find its IRI. Then, I will query the venue where this publication was published.

[Tool]
Top 10 entity alternatives:
1. Liming Zou et al.: Coverless Vid... Inter Frame Combination. (2021) (<https://dblp.org/rec/conf/isgv/ZouWW021>)
2. Nan Li et al.: Robust coverless ...-frame keypoint matching. (2023) (<https://dblp.org/rec/journals/istr/LiQXT23>)
3. Laijin Meng et al.: A Robust Cov...milarity of Inter-Frames. (2024) (<https://dblp.org/rec/journals/tmm/MengJSZX24>)
4. Yun Tan et al.: Coverless Stegan...Motion Analysis of Video. (2021) (<https://dblp.org/rec/journals/scn/TanQXZW21>)
5. Laijin Meng et al.: Coverless Vi... Maximum DC Coefficients. (2020) (<https://dblp.org/rec/journals/corr/abs-2012-06809>)
6. Yangping Lin et al.: Robust vide... channel-space attention. (2022) (<https://dblp.org/rec/journals/jei/Lin00CZ022>)
7. Ge Li et al.: Depth Video Inter ...on Deep Frame Generation. (2024) (<https://dblp.org/rec/journals/tbc/LiLPPL24>)
8. Haoyue Tian et al.: Video Frame ...Deformable Kernel Region. (2022) (<https://dblp.org/rec/conf/ijcai/TianGP22>)
9. Haoyue Tian et al.: Video Frame ...Deformable Kernel Region. (2022) (<https://dblp.org/rec/journals/corr/abs-2204-11396>)
10. Yueshuang Jiao et al.: A robust ... on asymmetric structure. (2024) (<https://dblp.org/rec/journals/jvcir/JiaoZLLLL24>)

[Assistant] I found the publication "Coverless Video Steganography Based on Inter Frame Combination" published in 2021 with the IRI <https://dblp.org/rec/conf/isgv/ZouWW021>. Now I will query the venue where this publication was published.

[Tool]
Got 1 row and 1 column:
| venue |
| ----- |
| ISGV  |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?pub ?title ?year WHERE {
  VALUES ?pub {
    <https://dblp.org/rec/journals/cga/Kroeker02> <https://dblp.org/rec/conf/pldi/PhuliaBB20>
  }
  ?pub dblp:title ?title ; dblp:yearOfPublication ?year .
}

Got 2 rows and 3 columns:
| pub                                                                                | title                                                               | year             |
| ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | ---------------- |
| Ankush Phulia et al.: OOElala: o...or compiler optimization. (2020) (<https://dblp.org/rec/conf/pldi/PhuliaBB20>)   | OOElala: order-of-evaluation bas...lysis for compiler optimization. | 2020 (xsd:gYear) |
| Kirk L. Kroeker: Graphics and Se...loring Visual Biometrics. (2002) (<https://dblp.org/rec/journals/cga/Kroeker02>) | Graphics and Security: Exploring Visual Biometrics.                 | 2002 (xsd:gYear) |

The publication 'Graphics and Security: Exploring Visual Biometrics' was published in 2002, and 'OOElala: order-of-evaluation based alias analysis for compiler optimization' was published in 2020. Therefore, 'Graphics and Security: Exploring Visual Biometrics' was published first.

Between 'Graphics and Security: Exploring Visual Biometrics' and 'OOElala: order-of-evaluation based alias analysis for compiler optimization', which one was published first?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Between 'Graphics and Security: Exploring Visual Biometrics' and 'OOElala: order-of-evaluation based alias analysis for compiler optimization', which one was published first?

[Assistant] To determine which publication was published first, I need to find the publication years of both "Graphics and Security: Exploring Visual Biometrics" and "OOElala: order-of-evaluation based alias analysis for compiler optimization" in the dblp knowledge graph. I will first search for the entities corresponding to these publication titles to get their IRIs, then retrieve their publication years for comparison.

[Tool]
Top 10 entity alternatives:
1. Kirk L. Kroeker: Graphics and Se...loring Visual Biometrics. (2002) (<https://dblp.org/rec/journals/cga/Kroeker02>)
2. Graphics and Visual Computing (<https://dblp.org/streams/journals/cgx>)
3. Venu Govindaraju: Biometrics and Security. (2009) (<https://dblp.org/rec/conf/iciss/Govindaraju09>)
4. Computer Graphics and Visual Computing (CGVC) (<https://dblp.org/streams/conf/tpcg>)
5. Computer Graphics, Imaging and Visualization (CGIV) (<https://dblp.org/streams/conf/IEEEcgiv>)
6. Patrizio Campisi: Security and Privacy in Biometrics (2013) (<https://dblp.org/rec/books/daglib/0032278>)
7. Youngmin Kim: Saliency-guided Graphics and Visualization. (2008) (<https://dblp.org/rec/phd/basesearch/Kim08i>)
8. Enhancing Security by Combining Biometrics and Cryptography. (2017) (<https://dblp.org/rec/journals/iacr/PopaS17>)
9. International Symposium on Biome...d Security Technologies (ISBAST) (<https://dblp.org/streams/conf/isbast>)
10. Symposium on Parallel Visualization and Graphics (PVGS) (<https://dblp.org/streams/conf/pvgs>)

[Tool]
Top 10 entity alternatives:
1. Ankush Phulia et al.: OOElala: o...or compiler optimization. (2020) (<https://dblp.org/rec/conf/pldi/PhuliaBB20>)
2. Kenneth G. Walter: Recursion Ana...or Compiler Optimization. (1976) (<https://dblp.org/rec/journals/cacm/Walter76>)
3. Michael Gorbovitski et al.: Alia...ion of dynamic languages. (2010) (<https://dblp.org/rec/conf/dls/GorbovitskiLSRT10>)
4. Khushboo Chitre et al.: The road...s missed by the compiler. (2022) (<https://dblp.org/rec/journals/pacmpl/ChitreKP22>)
5. Wai C. Chu: Gradient-descent bas...near prediction analysis. (2003) (<https://dblp.org/rec/conf/icassp/Chu03>)
6. Guray Ozen: Compiler and runtime... & optimization for GPUs. (2018) (<https://dblp.org/rec/phd/es/Ozen18>)
7. Jin Lin et al.: A compiler frame...alysis and optimizations. (2003) (<https://dblp.org/rec/conf/pldi/LinCHYJNC03>)
8. Kyle Daruwalla et al.: BitSAD v2... for Bitstream Computing. (2020) (<https://dblp.org/rec/journals/taco/DaruwallaZSL20>)
9. Tong Zhan et al.: Optimization T...sis Based on LLM (GPT-3). (2024) (<https://dblp.org/rec/journals/corr/abs-2405-09770>)
10. Ming Zhao et al.: Subspace Analy...AAM Based Face Alignment. (2004) (<https://dblp.org/rec/conf/fgr/ZhaoCLB04>)

[Tool] Got no rows and 3 columns

[Assistant]
The exact titles "Graphics and Security: Exploring Visual Biometrics" and "OOElala: order-of-evaluation based alias analysis for compiler optimization" do not directly appear as publication titles in dblp. However, I found close matches with publication IRIs and years: "Graphics and Security: Exploring Visual Biometrics" corresponds to a 2002 publication by Kirk L. Kroeker, and "OOElala: order-of-evaluation based alias analysis for compiler optimization" corresponds to a 2020 publication by Ankush Phulia et al.

I will now query the publication years for these two identified publications by their IRIs to confirm the publication years and compare which one was published first.

[Tool]
Got 2 rows and 3 columns:
| pub                                                                                | title                                                               | year             |
| ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | ---------------- |
| Ankush Phulia et al.: OOElala: o...or compiler optimization. (2020) (<https://dblp.org/rec/conf/pldi/PhuliaBB20>)   | OOElala: order-of-evaluation bas...lysis for compiler optimization. | 2020 (xsd:gYear) |
| Kirk L. Kroeker: Graphics and Se...loring Visual Biometrics. (2002) (<https://dblp.org/rec/journals/cga/Kroeker02>) | Graphics and Security: Exploring Visual Biometrics.                 | 2002 (xsd:gYear) |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ( AVG( ?count ) AS ?averagePapersPerYear ) WHERE {
  {
    SELECT ?year ( COUNT( ?pub ) AS ?count ) WHERE {
      ?pub dblp:authoredBy <https://dblp.org/pid/45/1135> .
      ?pub dblp:yearOfPublication ?year .
    }
    GROUP BY ?year
  }
}

Got 1 row and 1 column:
| averagePapersPerYear  |
| --------------------- |
| 1.33333 (xsd:decimal) |

The average number of papers published by Richard Deane Terrell per year is approximately 1.33.

What is the average number of papers published by Richard Deane Terrell per year?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 average number of papers published by Richard Deane Terrell per year?

[Assistant]
To answer this question, I need to find the publications authored by Richard Deane Terrell and then calculate the average number of papers he published per year.

Step 1: Find the entity IRI for Richard Deane Terrell in the dblp knowledge graph.
Step 2: Retrieve the publication years of all papers authored by him.
Step 3: Calculate the average number of papers published per year.

I will start with step 1 by searching for the entity of Richard Deane Terrell.

[Tool]
Top 10 entity alternatives:
1. Richard Deane Terrell, also known as Richard Deane Terrell (<https://dblp.org/pid/45/1135>)
2. Richard H. Deane, also known as Richard H. Deane (<https://dblp.org/pid/28/10898>)
3. Richard 0001, also known as Richard (<https://dblp.org/pid/147/5246>)
4. Richard Durbin, also known as Richard Durbin (<https://dblp.org/pid/d/RichardDurbin>)
5. Richard Jiang 0001, also known as Richard Jiang (<https://dblp.org/pid/70/8305>)
6. Richard Bornat, also known as Richard Bornat (<https://dblp.org/pid/49/5280>)
7. Richard Tsai 0001, also known as Richard Tsai (<https://dblp.org/pid/00/6413>)
8. Richard Harper 0001, also known as Richard Harper (<https://dblp.org/pid/56/3025-1>)
9. Richard Gray 0004, also known as Richard Gray (<https://dblp.org/pid/321/2045>)
10. Richard Bellman, also known as Richard Bellman (<https://dblp.org/pid/63/2494>)

[Assistant]
I found the entity IRI for Richard Deane Terrell: <https://dblp.org/pid/45/1135>.

Next, I will retrieve the publication years of all papers authored by Richard Deane Terrell to calculate the average number of papers published per year.

[Tool]
Got 6 rows and 2 columns:
| year             | count       |
| ---------------- | ----------- |
| 1995 (xsd:gYear) | 1 (xsd:int) |
| 2004 (xsd:gYear) | 1 (xsd:int) |
| 2005 (xsd:gYear) | 1 (xsd:int) |
| 2006 (xsd:gYear) | 1 (xsd:int) |
| 2007 (xsd:gYear) | 3 (xsd:int) |
| 2011 (xsd:gYear) | 1 (xsd:int) |

[Assistant]
Richard Deane Terrell has published papers in the years 1995, 2004, 2005, 2006, 2007, and 2011 with counts 1, 1, 1, 1, 3, and 1 respectively.

Now, I will calculate the average number of papers published per year based on these counts.

[Tool]
Got 1 row and 1 column:
| averagePapersPerYear  |
| --------------------- |
| 1.33333 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?venue ?year WHERE {
  <https://dblp.org/rec/journals/corr/abs-2201-12082> dblp:publishedIn ?venue ; dblp:yearOfPublication ?year .
}

The publication titled 'Interplay between depth of neural networks and locality of target functions' was published in the venue 'CoRR' in the year 2022.

Where was 'Interplay between depth of neural networks and locality of target functions' published and when?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Where was 'Interplay between depth of neural networks and locality of target functions' published and when?

[Assistant] To answer the question about where and when the publication titled "Interplay between depth of neural networks and locality of target functions" was published, I will first search for the publication entity by its exact title in the dblp knowledge graph. Then I will retrieve the publication venue and year of publication for that entity.

[Tool]
Top 10 entity alternatives:
1. Takashi Mori and Masahito Ueda: ...lity of target functions. (2022) (<https://dblp.org/rec/journals/corr/abs-2201-12082>)
2. Kei Uchizawa and Haruki Abe: Tra...Depth of Neural Networks. (2024) (<https://dblp.org/rec/journals/neco/UchizawaA24>)
3. Fenglei Fan et al.: Quasi-Equiva...Depth of Neural Networks. (2023) (<https://dblp.org/rec/journals/jmlr/FanLW23>)
4. Andreas Thom: Approximation of functions by neural networks. (2019) (<https://dblp.org/rec/journals/corr/abs-1901-10267>)
5. Jean-Jacques Mariage: Holistic S...ion and Self-Observation. (2006) (<https://dblp.org/rec/journals/ijcomputing/Mariage06>)
6. Fenglei Fan and Ge Wang: Duality...Depth of Neural Networks. (2020) (<https://dblp.org/rec/journals/corr/abs-2002-02515>)
7. Convergence of Deep Neural Netwo...on Functions and Pooling. (2022) (<https://dblp.org/rec/journals/corr/abs-2205-06570>)
8. Will Burstein and John Wilmes: O...ssion of Neural Networks. (2020) (<https://dblp.org/rec/conf/icann/BursteinW20>)
9. Pierre Baldi: Neural networks, o...raic threshold functions. (1988) (<https://dblp.org/rec/journals/tit/Baldi88>)
10. Hanqing Zeng et al.: Decoupling ...of Graph Neural Networks. (2021) (<https://dblp.org/rec/conf/nips/ZengZXSMKPJC21>)

[Assistant]
I found a matching publication titled "Interplay between depth of neural networks and locality of target functions" authored by Takashi Mori and Masahito Ueda from 2022. The publication record I found is https://dblp.org/rec/journals/corr/abs-2201-12082.

Next, I will query the publication venue and year of publication for this publication entity.

[Tool]
Got 1 row and 2 columns:
| venue | year             |
| ----- | ---------------- |
| CoRR  | 2022 (xsd:gYear) |

[Tool] Stopped generation process


PREFIX dblp: <https://dblp.org/rdf/schema#>
SELECT ?orcid WHERE {
  <https://dblp.org/pid/p/FriedrichPillichshammer> dblp:orcid ?orcid
}

Yes, the author Friedrich Pillichshammer has an ORCID: https://orcid.org/0000-0001-6952-9218.

Does the author Friedrich Pillichshammer have an ORCID?

[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:
dblp at https://qlever.cs.uni-freiburg.de/api/dblp with notes:
1. Use dblp:authoredBy to link publications to authors. The inverse (author -> pubs) is not via dblp:author; queries with dblp:author typically return no results.
2. Publication year is dblp:yearOfPublication (xsd:gYear). Avoid dblp:year. For last N years use YEAR(NOW()) in FILTER; use >= for inclusive bounds. Don’t assume current year in prose. MIN/MAX work on gYear; avoid unnecessary casts.
3. Venue is dblp:publishedIn (string like short name) and/or dblp:publishedInStream (entity IRI for series/journal/conference). For simple venue text, use dblp:publishedIn.
4. To list an author’s publications: SELECT ?pub WHERE { ?pub dblp:authoredBy <authorIRI> }. Then OPTIONAL get dblp:title, dblp:publishedIn, dblp:yearOfPublication.
5. For yes/no authorship checks, use ASK with dblp:authoredBy, e.g., ASK { <pubIRI> dblp:authoredBy <authorIRI> }.
6. Titles are via dblp:title on publication entities. rdfs:label often exists but is less reliable for titles; prefer dblp:title.
7. If dblp:authoredBy on a pub yields a disambiguation author node (e.g., “… (disambiguation)”), treat it as the author IRI to use in joins; it still identifies the creator.
8. Use dblp:createdBy as an alternative to dblp:authoredBy when authoredBy is missing; both link pubs to creators. Prefer authoredBy for authorship semantics.
9. When filtering by venue names, dblp:publishedIn is a short string (e.g., ‘WSC’, ‘ICALT’, ‘Gov. Inf. Q.’). Avoid FILTER on ?pub IRI strings; filter on dblp:publishedIn value.
10. Avoid rdf:type for pubs; rely on authoredBy/title/publishedIn/yearOfPublication. When counting, use COUNT(DISTINCT ?pub). For average per year, take AVG over yearly counts (subquery GROUP BY year); don’t divide by year-span.
11. Avoid using foaf:name/rdfs:label on creators for joins. Creators expose dblp:creatorName/primaryCreatorName and dblp:primaryAffiliation (string). Prefer joins on IRIs; fetch names/affiliations separately. Use SELECT DISTINCT ?author to dedup.
12. When a SELECT returns no rows unexpectedly, check schema via search_property/search_property_of_entity or list the entity. Many failures stem from using wrong props (dblp:author vs dblp:authoredBy).
13. Negations: avoid tautological NOT EXISTS. Prefer ASK { positive pattern } and invert in prose. For “also published in V?”, test other pubs by same authors and exclude the given pub via FILTER(?other != <pubIRI>) together with dblp:publishedIn 'V'.
14. Avoid text search on titles with CONTAINS/LCASE for exact identification; prefer search_entity to get the record IRI, then query structured triples. After search_entity, verify exact title via dblp:title; don’t pick a “closest match” blindly.
15. Be careful with complex ASK + FILTER combinations; QLever may error (CartesianProductJoin assertion). Simplify logic or split into separate queries to avoid engine bugs.
16. For number of authors, prefer dblp:numberOfCreators when available; otherwise COUNT(DISTINCT ?a) over dblp:authoredBy.

General notes for all knowledge graphs:
1. Normalize NL negations to a clear existential query. Prefer ASK { positive pattern }. For negated questions, evaluate the positive fact and invert only in prose. Avoid tautological or heavy NOT EXISTS patterns that can be slow or misleading.
2. For relative time windows (“last N years”), always use YEAR(NOW()) in SPARQL and align inclusive/exclusive bounds with the question phrasing. Do not hardcode current year in answers; base the conclusion strictly on query results.

You can use the following SPARQL prefixes implicitly in all functions:
bibtex: <http://purl.org/net/nknouf/ns/bibtex#
cito: <http://purl.org/spar/cito/
datacite: <http://purl.org/spar/datacite/
dblp: <https://dblp.org/rdf/schema#
dblps: <https://dblp.org/rdf/schema-2020-07-01#
literal: <http://purl.org/spar/literal/
owl: <http://www.w3.org/2002/07/owl#
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
schema: <http://schema.org/
terms: <http://purl.org/dc/terms/
wd: <http://www.wikidata.org/entity/
wdt: <http://www.wikidata.org/prop/direct/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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#
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Does the author Friedrich Pillichshammer have an ORCID?

[Assistant] To answer the question whether the author Friedrich Pillichshammer has an ORCID, I need to find the author entity for Friedrich Pillichshammer in the dblp knowledge graph and then check if there is an ORCID associated with that author entity. I will start by searching for the author entity of Friedrich Pillichshammer.

[Tool]
Top 10 entity alternatives:
1. Friedrich Pillichshammer, also known as Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>)
2. Tobias Friedrich 0001, also known as Tobias Friedrich (<https://dblp.org/pid/181/2310-1>)
3. Friedrich Eisenbrand, also known as Friedrich Eisenbrand (<https://dblp.org/pid/e/FEisenbrand>)
4. Gottlieb Pirsic and Friedrich Pi...tensible hyperplane nets. (2011) (<https://dblp.org/rec/journals/ffa/PirsicP11>)
5. Friedrich Pukelsheim, also known as Friedrich Pukelsheim (<https://dblp.org/pid/32/4162>)
6. Friedrich Leisch, also known as Friedrich Leisch (<https://dblp.org/pid/48/5002>)
7. Friedrich Götze, also known as Friedrich Götze (<https://dblp.org/pid/178/8285>)
8. Thomas Friedrich 0002, also known as Thomas Friedrich (<https://dblp.org/pid/91/7589-2>)
9. Friedrich Recknagel, also known as Friedrich Recknagel (<https://dblp.org/pid/10/4291>)
10. Paul Friedrich 0002, also known as Paul Friedrich (<https://dblp.org/pid/304/8572-2>)

[Assistant] I found the author entity for Friedrich Pillichshammer in the dblp knowledge graph: <https://dblp.org/pid/p/FriedrichPillichshammer>. Next, I will check if this author entity has an ORCID associated with it. I will look for properties related to ORCID for this author entity.

[Tool]
Got 26 rows and 3 columns, showing the first 10 rows below:
| subject                                                                     | property                                     | object                                      |
| --------------------------------------------------------------------------- | -------------------------------------------- | ------------------------------------------- |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | creatorname (dblp:creatorName)               | Friedrich Pillichshammer                    |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | primaryCreatorName (dblp:primaryCreatorName) | Friedrich Pillichshammer                    |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | wikidata (dblp:wikidata)                     | wd:Q64667268                                |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | affiliation (dblp:affiliation)               | Johannes Kepler Universität Linz, Austria   |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | orcid (dblp:orcid)                           | <https://orcid.org/0000-0001-6952-9218>     |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | primaryAffiliation (dblp:primaryAffiliation) | Johannes Kepler Universität Linz, Austria   |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | webpage (dblp:webpage)                       | <https://mathgenealogy.org/id.php?id=91153> |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | datacite:hasIdentifier                       | _:bn13170037                                |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | rdf:type                                     | Creator (dblp:Creator)                      |
| Friedrich Pillichshammer (<https://dblp.org/pid/p/FriedrichPillichshammer>) | rdfs:label                                   | Friedrich Pillichshammer                    |
| ...                                                                         | ...                                          | ...                                         |

[Tool] Stopped generation process