Abstract
For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.
Highlights
Technological advancements in the field of information communication technology (ICT) support the conversion of the conventional web structures of internet documents to semantic web-linking data, enabling novel semantic web data representation and integration retrieval systems [1]
Evaluation of the question answering (QA) system method is a crucial component of QA systems, and as QA techniques are rapidly designed, a trustworthy evaluation measurement to review these applications is required
The present study addressed the research questions posed, and the ontology-based approach to QA systems identified is illustrated in Table 3, which shows that the majority of the extant studies in the field targeted a specific domain
Summary
Technological advancements in the field of information communication technology (ICT) support the conversion of the conventional web structures of internet documents to semantic web-linking data, enabling novel semantic web data representation and integration retrieval systems [1]. Millions of internet searches are made every minute, and obtaining precise answers to queries can be challenging, due to the continually expanding volume of information available [2]. The user receives a list of related documents that may contain the information required. IR techniques can be highly successful and retrieve relevant information, users face complex linguistic challenges to extract the desired information. Question answering (QA) techniques can address this by enabling users to express and extract precise information and knowledge in natural language (NL)
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