2010 Fiscal Year Final Research Report
Hypothesis Discovery via Knowledge Extraction and Generalization
| Project/Area Number |
21700169
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Kobe University |
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Principal Investigator |
SEKI Kazuhiro Kobe University, 自然科学系先端融合研究環, 助教 (30444566)
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| Project Period (FY) |
2009 – 2010
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| Keywords | 仮説生成 / 遺伝子機能アノテーション / イベント類似度 |
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| Research Abstract |
Owing to the recent advances in computer technologies, a large amount of texts have become available. This research attempted to automate highly intellectual tasks analyzing such texts, which usually require labor-intensive work and domain knowledge. Specifically, we investigated gene function annotation and hypothesis discovery. For the former, we proposed an approach based on semantic kernels and achieved both efficient and effective gene annotation as compared with existing works. For the latter, we defined event similarity to spot valid hypotheses. Evaluative experiments revealed that our proposed approach was more stable and effective than previous frequency-based approaches.
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