Results 181 to 190 of about 32,178 (242)

A general strategy for generating expert-guided, simplified views of ontologies. [PDF]

Sci Data
Caron AR, Puig-Barbe A, Quardokus EM, Balhoff JP, Belfiore J, Chipampe NJ, Hardi J, Herr BW, Kir H, Roncaglia P, Musen MA, Parkinson H, McLaughlin JA, Börner K, Osumi-Sutherland D.   +14 more
europepmc   +1 more source

A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology. [PDF]

Sci Data
Prudhomme T, De Colle G, Liebers A, Sculley A, Xie PK, Cohen S, Beverley J.   +6 more
europepmc   +1 more source

FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis. [PDF]

J Biomed Semantics
Liao X, Ederveen THA, Niehues A, de Visser C, Huang J, Badmus F, Doornbos C, Orlova Y, Kulkarni P, van der Velde KJ, Swertz MA, Brandt M, van Gool AJ, 't Hoen PAC.   +13 more
europepmc   +1 more source

MBI-KG: A knowledge graph of structured and linked economic research data extracted from the 1937 book "Die Maschinen-Industrie im Deutschen Reich". [PDF]

Data Brief
Shigapov R, Schmidt T, Kamlah J, Schumm I, Streb J, Lehmann-Hasemeyer S.   +5 more
europepmc   +1 more source

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Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL

Web Semantics, 2019
Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the ...
Jędrzej Potoniec, Agnieszka Ławrynowicz, C Maria Keet   +2 more
exaly   +2 more sources

Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search

Knowledge and Information Systems, 2021
Xin Hu, Jiangli Duan, Depeng Dang
exaly   +2 more sources

SPARQL Query Language

, 2020
This chapter provides a detailed introduction to the SPARQL Protocol and RDF Query Language (SPARQL 1.1): the standard query language for RDF. After some initial motivation, we delve into the features of the query language, illustrated with concrete examples. We then formally define the semantics of these query features.
Aidan Hogan
semanticscholar   +2 more sources

A Deep Neural Approach to KGQA via SPARQL Silhouette Generation

IEEE International Joint Conference on Neural Network, 2022
Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large scale Knowledge Graphs (KGs).
Sukannya Purkayastha, Saswati Dana, Dinesh Garg, Dinesh Khandelwal, G. Bhargav   +4 more
semanticscholar   +1 more source

SPARQL-vision

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
The wide adaptation of the Semantic Web and the Resource Description Framework (RDF) has made available many important datasets. The SPARQL query language facilitates the exploration of this information, which is available in a semi-structured way that does not comply with relational data models, deviating from exploration techniques that most ...
Maria Krommyda, Verena Kantere
openaire   +1 more source

AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL

International Conference on Information and Knowledge Management, 2022
This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL ...
Guanming Xiong, Junwei Bao, Wen Zhao, Youzheng Wu, Xiaodong He   +4 more
semanticscholar   +1 more source