{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sematch-semantic-entity-search-from-knowledge","title":"Sematch: Semantic Entity Search from Knowledge Graph","arxiv_id":null,"date":"2015-06-01","proceeding":"Joint Proceedings of the 1st International Workshop on Summarizing and Presenting Entities and Ontologies and the 3rd International Workshop on Human Semantic Web Interfaces (SumPre 2015, HSWI 2015) co-located with the 12th Extended Semantic Web Conferen | SumPre 2015 - 1st International Workshop on Summarizing and Presenting Entities and Ontologies | 1/06/2015 | Portoroz, Slovenia 2015 6","authors":["Ganggao Zhu and Carlos A. Iglesias"],"abstract":"As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query processing, entity linking, entity type linking and semantic similarity based query expansion. The system has\r\nbeen validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL.","url_abs":"https://ceur-ws.org/Vol-1556/paper2.pdf","url_pdf":"https://ceur-ws.org/Vol-1556/paper2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sematch-semantic-entity-search-from-knowledge","repo_url":"https://github.com/gsi-upm/sematch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"natural-language-queries","task_name":"Natural Language Queries"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}