Semantic integration of tree-structured data using dimension graphs

  • Authors:

  • Theodore Dalamagas;Dimitri Theodoratos;Antonis Koufopoulos;I-Ting Liu

  • Affiliations:

  • School of Electr. and Comp. Engineering, National Technical University of Athens, Athens;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ;School of Electr. and Comp. Engineering, National Technical University of Athens, Athens;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ

  • Venue:
  • Journal on Data Semantics IV
  • Year:
  • 2005

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Abstract

Nowadays, huge volumes of Web data are organized or exported in tree-structured form. Popular examples of such structures are product catalogs of e-market stores, taxonomies of thematic categories, XML data encodings, etc. Even for a single knowledge domain, name mismatches, structural differences and structural inconsistencies raise difficulties when many data sources need to be integrated and queried in a uniform way. In this paper, we present a method for semantically integrating tree-structured data. We introduce dimensions which are sets of semantically related nodes in tree structures. Based on dimensions, we suggest dimension graphs. Dimension graphs can be automatically extracted from trees and abstract their structural information. They are semantically rich constructs that provide query guidance to pose queries, assist query evaluation and support integration of tree-structured data. We design a query language to query tree-structured data. The language allows full, partial or no specification of the structure of the underlying tree-structured data used to issue queries. Thus, queries in our language are not restricted by the structure of the trees. We provide necessary and sufficient conditions for checking query satisfiability and we present a technique for evaluating satisfiable queries. Finally, we conducted several experiments to compare our method for integrating tree-structured data with one that does not exploit dimension graphs. Our results demonstrate the superiority of our approach.