A hybrid question answering system based on ontology and topic modeling

Kwong Seng, Fong and Chih How, Bong (2017) A hybrid question answering system based on ontology and topic modeling. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-10). pp. 151-158. ISSN 2180-1843

[img]
Preview
PDF
A hybrid question answering system based on ontology and topic modeling (abstrak).pdf

Download (891kB) | Preview
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

A Question Answering (QA) system is an application which could provide accurate answer in response to the natural language questions. However, some QA systems have their weaknesses, especially for the QA system built based on Knowledge-based approach. It requires to pre-define various triple patterns in order to solve different question types. The ultimate goal of this paper is to propose an automated QA system using a hybrid approach, a combination of the knowledge-based and text-based approaches. Our approach only requires two SPARQLs to retrieve the candidate answers from the ontology without defining any question pattern, and then uses the Topic Model to find the most related candidate answers as the answers. We also investigate and evaluate different language models (unigram and bigram). Our results have shown that this proposed QA system is able to perform beyond the random baseline and solve up to 44 out of 80 questions with Mean Reciprocal Rank (MRR) of 38.73% using bigram LDA.

Item Type: Article
Uncontrolled Keywords: Knowledge-Based Approach, Language Model, QA System, Text-Based Approach, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, , research, Universiti Malaysia Sarawak.
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Ibrahim
Date Deposited: 01 Mar 2018 08:24
Last Modified: 01 Mar 2018 08:24
URI: http://ir.unimas.my/id/eprint/19722

Actions (For repository members only: login required)

View Item View Item