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Screening a Case Base for Stroke Disease Detection Read chapter Read first chapter

2016 | OriginalPaper | Chapter

SemSynX: Flexible Similarity Analysis of XML Data via Semantic and Syntactic Heterogeneity/Homogeneity Detection

Authors : Jesús M. Almendros-Jiménez, Alfredo Cuzzocrea

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

In this paper we introduce and experimentally assess SemSynX, a novel technique for supporting similarity analysis of XML data via semantic and syntactic heterogeneity/homogeneity detection. Given two XML trees, SemSynX retrieves a list of semantic and syntactic heterogeneity/homogeneity matches of objects (i.e., elements, values, tags, attributes) occurring in certain paths of the trees. A local score that takes into account the path and value similarity is given for each heterogeneity/homogeneity found. A global score that summarizes the number of equal matches as well as the local scores globally is also provided. The proposed technique is highly customizable, and it permits the specification of thresholds for the requested degree of similarity for paths and values as well as for the degree of relevance for path and value matching. It thus makes possible to “adjust” the similarity analysis depending on the nature of the input XML trees. SemSynX has been implemented in terms of a XQuery library, as to enhance interoperability with other XML processing tools. To complete our analytical contributions, a comprehensive experimental assessment and evaluation of SemSynX over several classes of XML documents is provided.

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Footnotes
1
The use of keys is not mandatory in our approach, but keys are used in the running example to guide similarity search.
 
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Metadata
Title
SemSynX: Flexible Similarity Analysis of XML Data via Semantic and Syntactic Heterogeneity/Homogeneity Detection
Authors
Jesús M. Almendros-Jiménez
Alfredo Cuzzocrea
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_2

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