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A Hybrid Approach for XML Similarity

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4362)

Abstract

In the past few years, XML has been established as an effective means for information management, and has been widely exploited for complex data representation. Owing to an unparalleled increasing use of the XML standard, developing efficient techniques for comparing XML-based documents becomes essential in information retrieval (IR) research. Various algorithms for comparing hierarchically structured data, e.g. XML documents, have been proposed in the literature. However, to our knowledge, most of them focus exclusively on comparing documents based on structural features, overlooking the semantics involved. In this paper, we integrate IR semantic similarity assessment in an edit distance algorithm, seeking to amend similarity judgments when comparing XML-based documents. Our approach comprises of an original edit distance operation cost model, introducing semantic relatedness of XML element/attribute labels, in traditional edit distance computations. A prototype has been developed to evaluate our model’s performance. Experiments yielded notable results.

Keywords

  • Semantic Similarity
  • Semantic Relatedness
  • Edit Distance
  • Edit Operation
  • Semantic Similarity Measure

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-69507-3_68
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Tekli, J., Chbeir, R., Yetongnon, K. (2007). A Hybrid Approach for XML Similarity. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds) SOFSEM 2007: Theory and Practice of Computer Science. SOFSEM 2007. Lecture Notes in Computer Science, vol 4362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69507-3_68

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  • DOI: https://doi.org/10.1007/978-3-540-69507-3_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69506-6

  • Online ISBN: 978-3-540-69507-3

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