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  

IEICE TRANSACTIONS on Information and Systems   Vol.E84-D   No.1   pp.48-56
Publication Date: 2001/01/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Selected Papers from LA Symposium)
knowledge discovery,  graph structured data,  inductive logic programming,  refutably inductive inference,  

Full Text: PDF(498.5KB)>>
Buy this Article

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 KD-FGS, 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.