Open AccessOntology‐lexicon–based question answering over linked data
Author(s) -
Jabalameli Mehdi,
Nematbakhsh Mohammadali,
Zaeri Ahmad
Publication year - 2020
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0312
Subject(s) - computer science , sparql , lexicon , question answering , natural language processing , ontology , knowledge base , artificial intelligence , information retrieval , linked data , parsing , knowledge representation and reasoning , benchmark (surveying) , set (abstract data type) , dependency (uml) , semantic web , rdf , programming language , philosophy , geodesy , epistemology , geography
Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.