| Presentation | 2006-01-24 Semantic Web Content Mining using Relational Learning Makito NAKAMURA, Ning ZHONG, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | With the rapid growth of the Web, research and development on the Semantic Web as the next generation of the Web have recently received much attention. The Semantic Web has aimed to use information on the Web effectively by agent based problem solving that is based on meta data to become computer readable and processible. To achieve the Semantic Web, not only ontologies but also rules for agent inference are needed. In this paper, we propose a relational learning based approach to learn rules from meta data and Web contents for agent based problem solving on the Web. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Semantic Web / Relational Learning / Inductive Logic Programming / Content Mining |
| Paper # | KBSE2005-33 |
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| Committee | KBSE |
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| Conference Date | 2006/1/17(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Knowledge-Based Software Engineering (KBSE) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Semantic Web Content Mining using Relational Learning |
| Sub Title (in English) | |
| Keyword(1) | Semantic Web |
| Keyword(2) | Relational Learning |
| Keyword(3) | Inductive Logic Programming |
| Keyword(4) | Content Mining |
| 1st Author's Name | Makito NAKAMURA |
| 1st Author's Affiliation | Graduate School of Engineering, Maebashi Institute of Technology() |
| 2nd Author's Name | Ning ZHONG |
| 2nd Author's Affiliation | Graduate School of Engineering, Maebashi Institute of Technology |
| Date | 2006-01-24 |
| Paper # | KBSE2005-33 |
| Volume (vol) | vol.105 |
| Number (no) | 546 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |