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Structure Inference for Linked Data Sources Using Clustering

  • Klitos ChristodoulouEmail author
  • Norman W. Paton
  • Alvaro A. A. Fernandes
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8990)

Abstract

Linked Data (LD) overlays the World Wide Web of documents with a Web of Data. This is becoming significant as shown in the growth of LD repositories available as part of the Linked Open Data (LOD) cloud. At the instance-level, LD sources use a combination of terms from various vocabularies, expressed as RDFS/OWL, to describe data and publish it to the Web. However, LD sources do not organise data to conform to a specific structure analogous to a relational schema; instead data can adhere to multiple vocabularies. Expressing SPARQL queries over LD sources – usually over a SPARQL endpoint that is presented to the user – requires knowledge of the predicates used so as to allow queries to express user requirements as graph patterns. Although LD provides low barriers to data publication using a single language (i.e., RDF), sources organise data with different structures and terminologies. This paper describes an approach to automatically derive structural summaries over instance-level data expressed as RDF triples. The technique builds on a hierarchical clustering algorithm that organises RDF instance-level data into groups that are then utilised to infer a structural summary over a LD source. The resulting structural summaries are expressed in the form of classes, properties and, relationships. Our experimental evaluation shows good results when applied to different types of LD sources.

Keywords

Schema Linked Data Clustering Query formulation 

Notes

Acknowledgement

Klitos Christodoulou has been supported by funding from the UK Engineering and Physical Sciences Research council, whose support we are pleased to acknowledge.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Klitos Christodoulou
    • 1
    Email author
  • Norman W. Paton
    • 1
  • Alvaro A. A. Fernandes
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

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