Abstract
We present a method for discovering new knowledge from structural 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. Formal Graph System (FGS) is a kind of logic programming system which directly deals with graphs just like first order terms. By employing refutably inductive inference algorithms and graph algorithmic techniques, we are developing a knowledge discovery system KD-FGS, which acquires knowledge directly from graph data by using FGS as a knowledge representation language.
In this paper we develop a logical foundation of our knowledge discovery system. A term tree is a pattern which consists of variables and tree- like structures. We give a polynomial-time algorithm for finding a unifier of a term tree and a tree in order to make consistency checks efficiently. Moreover we give experimental results on some graph theoretical notions with the system. The experiments show that the system is useful for finding new knowledge.
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Miyahara, T., Shoudai, T., Uchida, T., Kuboyama, T., Takahashi, K., Ueda, H. (1999). Discovering New Knowledge from Graph Data Using Inductive Logic Programming. In: Džeroski, S., Flach, P. (eds) Inductive Logic Programming. ILP 1999. Lecture Notes in Computer Science(), vol 1634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48751-4_21
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DOI: https://doi.org/10.1007/3-540-48751-4_21
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