Concept Relation Extraction Using Naive Bayes Classifier for Ontology Based Question Answering Systems
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | kumar, G. Suresh (Author) |
| مؤلفين آخرين: | Zayaraz, G. (Co-Author) |
| المجلد/العدد: | مج27, ع1 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2015
|
| الصفحات: | 13 - 24 |
| DOI: |
10.33948/0584-027-001-002 |
| ISSN: |
1319-1578 |
| رقم MD: | 973479 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Relation Extraction | Ontology Development | Dependency Parsing | Question Answering System
|
| رابط المحتوى: |
| المستخلص: |
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 Naive 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 |
|---|---|
| ISSN: |
1319-1578 |
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