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Discovering Knowledge from Graph Structured Data by Using Refutably Inductive Inference of Formal Graph Systems
Tetsuhiro MIYAHARA Tomoyuki UCHIDA Takayoshi SHOUDAI Tetsuji KUBOYAMA Kenichi TAKAHASHI Hiroaki UEDA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E84D
No.1
pp.4856 Publication Date: 2001/01/01
Online ISSN:
DOI:
Print ISSN: 09168532 Type of Manuscript: Special Section PAPER (Special Issue on Selected Papers from LA Symposium) Category: Keyword: knowledge discovery, graph structured data, inductive logic programming, refutably inductive inference,
Full Text: PDF(498.5KB)>>
Summary:
We present a new method for discovering knowledge from structured data which are represented by graphs in the framework of Inductive Logic Programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces, and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KDFGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.

