2019/03/04 by Gerhard Wohlgenannt, Wohlgenannt, Gerhard, Nikolay Klimov +12
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Text Analysis Techniques #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Semantic Web and Ontologies #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1903.01275
arxiv created 2019/03/04 · openalex publication_date 2019/03/04 · arxiv updated 2019/03/05 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for non-technical users. Ontodia provides support for diagrammatic data exploration, showcased in this publication in combination with the Wikidata dataset. We present improvements to the natural language interface regarding exploring and querying Linked Data entities. The method uses models of distributional semantics to find and rank entity properties related to user input in Ontodia. Various word embedding types and model settings are evaluated, and the results show that user experience in visual data exploration benefits from the proposed approach.