| Project/Area Number |
15200010
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
UEMURA Shunsuke Nara Institute of Science and Technology, School of Information Science, Professor, 情報科学研究科, 教授 (00203480)
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| Co-Investigator(Kenkyū-buntansha) |
MIYAZAKI Jun Nara Institute of Science and Technology, School of Information Science, Associate Professor, 情報科学研究科, 助教授 (40293394)
NAKAJIMA Shinsuke Nara Institute of Science and Technology, School of Information Science, Assistant Professor, 情報科学研究科, 助手 (90399535)
AMAGASA Toshiyuki University of Tsukuba, Center for Computational Sciences, Lecturer, 計算科学研究センター, 講師 (70314531)
HATANO Kenji Doshisha University, Faculty of Culture and Information Science, Lecturer, 文化情報学部, 講師 (80314532)
MAEDA Akira Ritsumeikan University, College of Information Science and Engineering, Associate Professor, 情報理工学部, 助教授 (20351322)
鈴木 優 立命館大学, 情報理工学部, 助手 (40388111)
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| Project Period (FY) |
2003 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥23,660,000 (Direct Cost: ¥18,200,000、Indirect Cost: ¥5,460,000)
Fiscal Year 2006: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2005: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2004: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2003: ¥9,620,000 (Direct Cost: ¥7,400,000、Indirect Cost: ¥2,220,000)
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| Keywords | cross-language information processing / Web directory / semantic Web / information retrieval / linguistic resource / information recommendation / タグ / 多言語処理 / 言語横断検索 / データベース / ディレクトリ / 言語横断情報検索 / セマンティックWeb / オントロジー / RDF / XML / セマンテイックWeb / 言語横断情報検索システム / インデクス / 接尾辞配列 |
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| Research Abstract |
This project has been conducted through the following sub themes : 1. construction and utilization of linguistic resources by using Web directories, 2. storage and query processing for semantic data, 3. scoring and discrimination of data for high precision information recommendation, and 4. automatic analysis of structural documents with links.
As for 1., we proposed a method of constructing the bilingual ontology by translating monolingual ontology using Web directory. Moreover we also proposed to utilize web directory as linguistic resources, and constructed Cross-Language Information Retrieval System utilized this linguistic resources. We verified that use of Web directory as linguistic resource is effective in multi-lingual information access through experimental results.
As for 2., we proposed a method for storing RDF data into relational databases and its query processing. RDF is one of the essential parts of semantic Web in order to express what are written in instances concisely.
… More
Our technique is to divide RDF into two parts : the part of including schema, and the part of including only instances. For the former part, we adopt an interval numbering scheme to identify inheritance, and then, for the latter parts, we adopt a path based storing technique. This idea provided high performance RDF queries which is independent of path lengths.
As for 3., we proposed a framework for context-aware and language independent information recommendation. Specifically, context information is mapped to a multidimensional space, and then, support vector machine (SVM) is applied to the space. With this framework, we realized the information with higher precision compared to previously proposed techniques.
Lastly, we proposed methods for analyzing structural documents with links to extract metadata from them : one example is Web pages, and the other one is blogs. For the former case, we showed how to determine the optimal sub graph for high precision information retrieval. For the latter case, we gave a method to find an agitator who is an opinion leader of an arbitrary blog threads. With these techniques, useful meta-information can be extracted from Web resources without any linguistic information.
These approaches can be combined together and be utilized to next generation cross-language information processing. Less
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