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Ryu P.M.,Speech Language Information Research Center | Jang M.-G.,Speech Language Information Research Center | Kim H.-K.,Speech Language Information Research Center | Park S.-Y.,Sangmyung University
IEICE Transactions on Information and Systems | Year: 2013

We propose a novel method for knowledge consolidation based on a knowledge graph as a next step in relation extraction from text. The knowledge consolidation method consists of entity consolidation and relation consolidation. During the entity consolidation process, identical entities are found and merged using both name similarity and relation similarity measures. In the relation consolidation process, incorrect relations are removed using cardinality properties, temporal information and relation weight in given graph structure. In our experiment, we could generate compact and clean knowledge graphs where number of entities and relations are reduced by 6.1% and by 17.4% respectively with increasing relation accuracy from 77.0% to 85.5%. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers. Source


Yoon Y.-C.,Speech Language Information Research Center | Lee C.-K.,Kangwon National University | Kim H.-K.,Speech Language Information Research Center | Jang M.-G.,Speech Language Information Research Center | And 2 more authors.
IEICE Transactions on Information and Systems | Year: 2012

In this paper, we present a supervised learning method to seek out answers to the most frequently asked descriptive questions: reason, method, and definition questions. Most of the previous systems for question answering focus on factoids, lists or definitional questions. However, descriptive questions such as reason questions and method questions are also frequently asked by users. We propose a system for these types of questions. The system conducts an answer search as follows. First, we analyze the user's question and extract search keywords and the expected answer type. Second, information retrieval results are obtained from an existing search engine such as Yahoo or Google. Finally, we rank the results to find snippets containing answers to the questions based on a ranking SVM algorithm. We also propose features to identify snippets containing answers for descriptive questions. The features are adaptable and thus are not dependent on answer type. Experimental results show that the proposed method and features are clearly effective for the task. © 2012 The Institute of Electronics, Information and Communication Engineers. Source

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