文章基本信息

标题：Ontology‐lexicon–based question answering over linked data
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作者：Mehdi Jabalameli ; Mohammadali Nematbakhsh ; Ahmad Zaeri 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2020
卷号：42
期号：2
页码：239-246
DOI：10.4218/etrij.2018-0312
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：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.