Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Rich classes inferable from positive data
Information and Computation
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
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
Biochemical Knowledge Discovery Using Inductive Logic Programming
DS '98 Proceedings of the First International Conference on Discovery Science
Designing Graph Drawings by Layout Graph Grammars
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
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We present a new framework for discovering knowledge from two-dimensional structured data by using Inductive Logic Programming. Two-dimensional graph structured data such as image or map data are widely used for representing relations and distances between various objects. First, we define a layout term graph suited for representing two-dimensional graph structured data. A layout term graph is a pattern consisting of variables and two-dimensional graph structures. Moreover, we propose Layout Formal Graph System (LFGS) as a new logic programming system having a layout term graph as a term. LFGS directly deals with graphs having positional relations just like first order terms. Second, we show that LFGS is more powerful than Layout Graph Grammar, which is a generating system consisting of a context-free graph grammar and positional relations. This indicates that LFGS has the richness and advantage of representing knowledge about two-dimensional structured data. Finally, we design a knowledge discovery system, which uses LFGS as a knowledge representation language and refutably inductive inference as a learning method. In order to give a theoretical foundation of our knowledge discovery system, we give the set of weakly reducing LFGS programs which is a sufficiently large hypothesis space of LFGS programs and show that the hypothesis space is refutably inferable from complete data.