Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
A hierarchy of eNCE families of graph languages
Theoretical Computer Science
Professional Java XML
IEEE Intelligent Systems
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Applying the Subdue Substructure Discovery System to the Chemical Toxicity Domain
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Structural Knowledge Discovery Used to Analyze Earthquake Activity
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Grammatical Inference Based on Hyperedge Replacement
Proceedings of the 4th International Workshop on Graph-Grammars and Their Application to Computer Science
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Characterizing compressibility of disjoint subgraphs with NLC grammars
LATA'11 Proceedings of the 5th international conference on Language and automata theory and applications
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Graph grammars combine the relational aspect of graphs with the iterative and recursive aspects of string grammars, and thus represent an important next step in our ability to discover knowledge from data. In this paper we describe an approach to learning node replacement graph grammars. This approach is based on previous research in frequent isomorphic subgraphs discovery. We extend the search for frequent subgraphs by checking for overlap among the instances of the subgraphs in the input graph. If subgraphs overlap by one node we propose a node replacement grammar production. We also can infer a hierarchy of productions by compressing portions of a graph described by a production and then infer new productions on the compressed graph. We validate this approach in experiments where we generate graphs from known grammars and measure how well our system infers the original grammar from the generated graph. We also describe results on several real-world tasks from chemical mining to XML schema induction. We briefly discuss other grammar inference systems indicating that our study extends classes of learnable graph grammars.