Ontology-based Question Answering Systems over Knowledge Bases: A Survey

Abstract

Searching relevant, specific information in big data volumes is quite a challenging task. Despite the numerous strategies in the literature to tackle this problem, this task is usually carried out by resorting to a Question Answering (QA) systems. There are many ways to build a QA system, such as heuristic approaches, machine learning, and ontologies. Recent research focused their efforts on ontology-based methods since the resulting QA systems can benefit from knowledge modeling. In this paper, we present a systematic literature survey on ontology-based QA systems regarding any questions. We also detail the evaluation process carried out in these systems and discuss how each approach differs from the others in terms of the challenges faced and strategies employed. Finally, we present the most prominent research issues still open in the field. QA SYSTEM TYPESGiven the full range of topics involved in QA system, this paper focuses on an existing classification based on the type of response expected to be found (Latifi, 2018): Closed-Domain Question Answering (QADR), QA for Comprehension Reading (QACR), Community Question Answering (QAC), 532

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