Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems
Domain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose a methodology to extract concept re...
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| Format: | Article |
| Language: | English |
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Elsevier
2015-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157814000020 |
| _version_ | 1818133460710064128 |
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| author | G. Suresh kumar G. Zayaraz |
| author_facet | G. Suresh kumar G. Zayaraz |
| author_sort | G. Suresh kumar |
| collection | DOAJ |
| description | Domain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose a methodology to extract concept relations from unstructured text using a syntactic and semantic probability-based Naïve Bayes classifier. We propose an algorithm to iteratively extract a list of attributes and associations for the given seed concept from which the rough schema is conceptualized. A set of hand-coded dependency parsing pattern rules and a binary decision tree-based rule engine were developed for this purpose. This ontology construction process is initiated through a question answering process. For each new query submitted, the required concept is dynamically constructed, and ontology is updated. The proposed relation extraction method was evaluated using benchmark data sets. The performance of the constructed ontology was evaluated using gold standard evaluation and compared with similar well-performing methods. The experimental results reveal that the proposed approach can be used to effectively construct a generic domain ontology with higher accuracy. Furthermore, the ontology construction method was integrated into the question answering framework, which was evaluated using the entailment method. |
| first_indexed | 2024-12-11T08:53:05Z |
| format | Article |
| id | doaj.art-b5966bf919684fb492ac09fdd071011b |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-11T08:53:05Z |
| publishDate | 2015-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-b5966bf919684fb492ac09fdd071011b2022-12-22T01:13:58ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782015-01-01271132410.1016/j.jksuci.2014.03.001Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systemsG. Suresh kumar0G. Zayaraz1Department of Computer Science, Pondicherry University, IndiaDepartment of Computer Science and Engineering, Pondicherry Engineering College, IndiaDomain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose a methodology to extract concept relations from unstructured text using a syntactic and semantic probability-based Naïve Bayes classifier. We propose an algorithm to iteratively extract a list of attributes and associations for the given seed concept from which the rough schema is conceptualized. A set of hand-coded dependency parsing pattern rules and a binary decision tree-based rule engine were developed for this purpose. This ontology construction process is initiated through a question answering process. For each new query submitted, the required concept is dynamically constructed, and ontology is updated. The proposed relation extraction method was evaluated using benchmark data sets. The performance of the constructed ontology was evaluated using gold standard evaluation and compared with similar well-performing methods. The experimental results reveal that the proposed approach can be used to effectively construct a generic domain ontology with higher accuracy. Furthermore, the ontology construction method was integrated into the question answering framework, which was evaluated using the entailment method.http://www.sciencedirect.com/science/article/pii/S1319157814000020Relation extractionOntology developmentDependency parsingQuestion answering system |
| spellingShingle | G. Suresh kumar G. Zayaraz Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems Journal of King Saud University: Computer and Information Sciences Relation extraction Ontology development Dependency parsing Question answering system |
| title | Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems |
| title_full | Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems |
| title_fullStr | Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems |
| title_full_unstemmed | Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems |
| title_short | Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems |
| title_sort | concept relation extraction using naive bayes classifier for ontology based question answering systems |
| topic | Relation extraction Ontology development Dependency parsing Question answering system |
| url | http://www.sciencedirect.com/science/article/pii/S1319157814000020 |
| work_keys_str_mv | AT gsureshkumar conceptrelationextractionusingnaivebayesclassifierforontologybasedquestionansweringsystems AT gzayaraz conceptrelationextractionusingnaivebayesclassifierforontologybasedquestionansweringsystems |