Systematic review of question answering over knowledge bases

Autor: Arnaldo Pereira, Alina Trifan, Rui Pedro Lopes, José Luís Oliveira
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IET Software, Vol 16, Iss 1, Pp 1-13 (2022)
Druh dokumentu: article
ISSN: 1751-8814
1751-8806
DOI: 10.1049/sfw2.12028
Popis: Abstract Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical user’s expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this difficulty have suggested using question answering (QA) systems to provide user‐friendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English full‐text articles published since 2015 on methods and systems for KBQAs. Sixty‐six articles were reviewed to describe their underlying reference architectures.
Databáze: Directory of Open Access Journals
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