1995 Fiscal Year Final Research Report Summary
Research on Discovery and Creative Process Modelling in Logic
| Project/Area Number |
05452360
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Keio University |
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Principal Investigator |
FURUKAWA Koichi Keio University, Graduate school of Media and Governance, Professor, 改策・メディア研究科, 教授 (10245615)
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| Co-Investigator(Kenkyū-buntansha) |
IMAI Mutumi Keio University, Faculty of Environmental Information, research associate, 環境情報学部, 助手 (60255601)
MUKAI Kuniaki Keio University, Faculty of Environmental Information, Professor, 環境情報学部, 教授 (80245597)
ISHIZAKI Shun Keio University, Faculty of Environmental Information, Professor, 環境情報学部, 教授 (00245614)
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| Project Period (FY) |
1993 – 1995
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| Keywords | Higher order oncept learning / Inductive logic programming / Data mining / Automatic classification of e-mails / Parallelization of ILP / Language acquisition |
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| Research Abstract |
We built a new framework for reducing higher order concept learning problem into the problem of a new predicate introduction.
Particularly, we showed that we can achieve the higher order concept learning in the framework of first order concept learning by giving the definition of a new predicate corresponding to the higher order predicate as a background knowledge of PROGOL.
Furthermore, we proposed a general framework of utilizing database as a source of input to PROGOL by semi-automatically generating positive examples, negative examples, background knowledge, mode declaration and type information. We built an experimental system called DB-Amp to achieve data mining in database based on the framework.
We developed a knowledge base for an e-mail classification system which outomatically classifies e-mails concerning to equipment malfunction consultation in a real business environment by using DB-Amp. We gave keyword thesaurus as background knowledge DB-Amp and succeeded in sutomatically extracting e-mail classification rules.
We also made an attempt to parallelize PROGOL to increase efficiency of inductive logic programming and built an experimental system in Prolog. Especially, we showed that we could make the program very simple by adopting a parallel theorem prover MGTP (Model Generation Theorem Prover) in the algorithm
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