| Project/Area Number |
16500085
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Aoyama Gakuin University |
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Principal Investigator |
HARADA Minoru Aoyama Gakuin University, College of Science and Engineering, Professor, 理工学部, 教授 (10218654)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUDA Yoshitatsu Aoyama Gakuin University, College of Science and Engineering, Assistant, 理工学部, 助手 (40433700)
韓 東力 青山学院大学, 理工学部, 助手 (10365033)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2005: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2004: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Question answering / Graph matching / Semantic analysis / Semantic graph / Answer extraction / 内容検索 / SVM / 回答抽出 / 自然語処理 / グラフ推論 / 照応解析 / インターネット検索 |
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| Research Abstract |
We have developed Metis, a question-answering system that finds an answer by matching a question graph with the knowledge graphs. The question graph is obtained as a result of semantic analysis of a question sentence, the knowledge graphs are similarly analyzed from knowledge sentences retrieved from a database using keywords extracted from the question sentence. In retrieving such knowledge sentences, the system searches for and collects them using Lucene, a search engine, based on search keywords extracted from the question graph. To extract the answer, Metis calculates the degrees of similarity between the question and knowledge graphs to conduct precise matching. In this matching, the system calculates the degrees of similarity, which is the relative size of the similarity co-occurrence graph to the question graphs with respect to all combinations of nodes in the knowledge graph corresponding to those in the question graph. The system then chooses the knowledge graph with the highest degree of similarity and extracts from it the portion that corresponds to the given interrogative word. The system presents this portion as the answer. The evaluation experiment was done by using 100 quiz millionaire questions. The precision to obtain the correct answer within the first three answers was 74%. Moreover, it participated in the evaluation contest in NTCIR, and the precision was 18% in QAC which asked factoid question, the precision was 22% in CLQA which asked the reasons, the method and etc.
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