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      Ripple Down Rules for Question Answering

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          Abstract

          Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.

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          Most cited references 37

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          A survey on question answering technology from an information retrieval perspective

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            An English language question answering system for a large relational database

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                Author and article information

                Journal
                2014-12-12
                2015-11-04
                Article
                10.3233/SW-150204
                1412.4160

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                V1: 21 pages, 7 figures, 10 tables. V2: 8 figures, 10 tables; shorten section 2; change sections 4.3 and 5.1.2. V3: Accepted for publication in the Semantic Web journal. V4 (Author's manuscript): camera ready version, available from the Semantic Web journal at http://www.semantic-web-journal.net
                cs.CL cs.IR

                Theoretical computer science, Information & Library science

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