Stratified Data IntegrationDownload PDF

Mar 08, 2021 (edited Apr 23, 2021)ESWC 2021 Workshop KGCW SubmissionReaders: Everyone
  • Keywords: Semantic Heterogeneity, Knowledge Graph Construction, Stratified Data Integration
  • Abstract: We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual (where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language (where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge (in the form of a graph where nodes are entity types and links are properties), and data (in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.
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