Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review
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, ra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/5/200 |
| _version_ | 1827693531624374272 |
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| author | Ammar Arbaaeen Asadullah Shah |
| author_facet | Ammar Arbaaeen Asadullah Shah |
| author_sort | Ammar Arbaaeen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:45:51Z |
| format | Article |
| id | doaj.art-ff69d07261904e16a996d704635c7a08 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T11:45:51Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ff69d07261904e16a996d704635c7a082023-11-21T18:08:25ZengMDPI AGInformation2078-24892021-05-0112520010.3390/info12050200Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A ReviewAmmar Arbaaeen0Asadullah Shah1Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaFaculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaFor 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.https://www.mdpi.com/2078-2489/12/5/200knowledge-basednatural language processingontology-based approachquestion answering systemssemantic similarity |
| spellingShingle | Ammar Arbaaeen Asadullah Shah Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review Information knowledge-based natural language processing ontology-based approach question answering systems semantic similarity |
| title | Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review |
| title_full | Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review |
| title_fullStr | Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review |
| title_full_unstemmed | Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review |
| title_short | Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review |
| title_sort | ontology based approach to semantically enhanced question answering for closed domain a review |
| topic | knowledge-based natural language processing ontology-based approach question answering systems semantic similarity |
| url | https://www.mdpi.com/2078-2489/12/5/200 |
| work_keys_str_mv | AT ammararbaaeen ontologybasedapproachtosemanticallyenhancedquestionansweringforcloseddomainareview AT asadullahshah ontologybasedapproachtosemanticallyenhancedquestionansweringforcloseddomainareview |