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Semantic Querying of News Articles With Natural Language Questions

Semantic Querying of News Articles With Natural Language Questions

Tuan-Dung Cao, Quang-Minh Nguyen
Copyright: © 2021 |Volume: 14 |Issue: 3 |Pages: 20
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781799860020|DOI: 10.4018/JITR.2021070103
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MLA

Cao, Tuan-Dung, and Quang-Minh Nguyen. "Semantic Querying of News Articles With Natural Language Questions." JITR vol.14, no.3 2021: pp.38-57. http://doi.org/10.4018/JITR.2021070103

APA

Cao, T. & Nguyen, Q. (2021). Semantic Querying of News Articles With Natural Language Questions. Journal of Information Technology Research (JITR), 14(3), 38-57. http://doi.org/10.4018/JITR.2021070103

Chicago

Cao, Tuan-Dung, and Quang-Minh Nguyen. "Semantic Querying of News Articles With Natural Language Questions," Journal of Information Technology Research (JITR) 14, no.3: 38-57. http://doi.org/10.4018/JITR.2021070103

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Abstract

The heterogeneity and the increasing amount of the news published on the web create challenges in accessing them. In the authors' previous studies, they introduced a semantic web-based sports news aggregation system called BKSport, which manages to generate metadata for every news item. Providing an intuitive and expressive way to retrieve information and exploiting the advantages of semantic search technique is within their consideration. In this paper, they propose a method to transform natural language questions into SPARQL queries, which could be applied to existing semantic data. This method is mainly based on the following tasks: the construction of a semantic model representing a question, detection of ontology vocabularies and knowledge base elements in question, and their mapping to generate a query. Experiments are performed on a set of questions belonging to various categories, and the results show that the proposed method provides high precision.

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