Application of tree mining to matching of knowledge structures of decision tree type
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Abstract: Matching of knowledge structures is generally important for scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, matching commonly occurs among different organizations on the knowledge describing the same domain. In this paper we propose a knowledge matching method which makes use of our previously developed tree mining algorithms for extracting frequent subtrees from a tree structured database. Example decision trees obtained from real world domains are used for experimentation purposes whereby some important issues that arise when extracting shared knowledge through tree mining are discussed. The potential of applying tree mining algorithms for automatic discovery of common knowledge structures is demonstrated.
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Hadzic, Fedja; Dillon, Tharam S.; Chang, Elizabeth (2007)Matching of heterogeneous knowledge sources is of increasing importance in areas such as scientific knowledge management, e-commerce, enterprise application integration, and many emerging Semantic Web applications. With ...
Hadzic, Fedja; Tan, H.; Dillon, Tharam S.; Chang, Elizabeth (2007)Frequent subtree mining has found many useful applications in areas where the domain knowledge is presented in a tree structured form, such as bioinformatics, web mining, scientific knowledge management etc. It involves ...
Tan, H.; Dillon, Tharam S.; Hadzic, Fedja; Chang, Elizabeth (2006)Our work is focused on the task of mining frequent subtrees from a database of rooted ordered labelled subtrees. Previously we have developed an efficient algorithm, MB3 , for mining frequent embedded subtrees from a ...