Sematch: Semantic Entity Search from Knowledge Graph

2015-06-01Joint 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 2015Code Available0· sign in to hype

Ganggao Zhu and Carlos A. Iglesias

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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 been validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL.

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Entity Linking Natural Language Queries Semantic Similarity Semantic Textual Similarity

Sematch: Semantic Entity Search from Knowledge Graph

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