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Matching XML Documents at Structural and Conceptual Level using Subtree Patterns

Matching XML Documents at Structural and Conceptual Level using Subtree Patterns

Qi Hua Pan, Fedja Hadzic, Tharam S. Dillon
Copyright: © 2012 |Pages: 46
ISBN13: 9781613503560|ISBN10: 1613503563|EISBN13: 9781613503577
DOI: 10.4018/978-1-61350-356-0.ch016
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MLA

Pan, Qi Hua, et al. "Matching XML Documents at Structural and Conceptual Level using Subtree Patterns." XML Data Mining: Models, Methods, and Applications, edited by Andrea Tagarelli, IGI Global, 2012, pp. 378-423. https://doi.org/10.4018/978-1-61350-356-0.ch016

APA

Pan, Q. H., Hadzic, F., & Dillon, T. S. (2012). Matching XML Documents at Structural and Conceptual Level using Subtree Patterns. In A. Tagarelli (Ed.), XML Data Mining: Models, Methods, and Applications (pp. 378-423). IGI Global. https://doi.org/10.4018/978-1-61350-356-0.ch016

Chicago

Pan, Qi Hua, Fedja Hadzic, and Tharam S. Dillon. "Matching XML Documents at Structural and Conceptual Level using Subtree Patterns." In XML Data Mining: Models, Methods, and Applications, edited by Andrea Tagarelli, 378-423. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-356-0.ch016

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Abstract

Knowledge matching is an important problem for many emerging applications in many areas including scientific knowledge management, ontology matching, e-commerce, and enterprise application integration. Matching the concepts of heterogeneous knowledge representations is very challenging due to the difficulty of taking contextual information into account and detecting complex matches. In this chapter, we describe a knowledge matching approach that uses subtree patterns to utilize structural information for matching at the conceptual and structural level. Initially, the algorithm does not take any syntactic information into account, but rather forms candidate mappings according to their structural/contextual relationships in the knowledge structures, which are then validated using online dictionaries and string similarity measures. The approach will then automatically extract the knowledge structure that is shared among all the matched knowledge representations. Experimental evaluation is performed on a number of real world XML schemas, which demonstrates the effectiveness of the proposed approach.

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