Learning elementary formal systems
Theoretical Computer Science
Short note: procedural semantics and negative information of elementary formal system
Journal of Logic Programming
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
Exact learning of tree patterns from queries and counterexamples
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A System for Approximate Tree Matching
IEEE Transactions on Knowledge and Data Engineering
KD-FGS: A Knowledge Discovery System from Graph Data Using Formal Graph System
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
May we introduce to you: hyperedge replacement
Proceedings of the 3rd International Workshop on Graph-Grammars and Their Application to Computer Science
Learning Unions of Tree Patterns Using Queries
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Data
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Detecting Traffic Problems with ILP
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Polynomial time inductive inference of TTSP graph languages from positive data
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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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 treelike 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.