Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

  • Kalpa Gunaratna
  • Krishnaprasad Thirunarayan
  • Amit Sheth
  • Gong Cheng
Conference paper

DOI: 10.1007/978-3-319-34129-3_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)
Cite this paper as:
Gunaratna K., Thirunarayan K., Sheth A., Cheng G. (2016) Gleaning Types for Literals in RDF Triples with Application to Entity Summarization. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham

Abstract

Associating meaning with data in a machine-readable format is at the core of the Semantic Web vision, and typing is one such process. Typing (assigning a class selected from schema) information can be attached to URI resources in RDF/S knowledge graphs and datasets to improve quality, reliability, and analysis. There are two types of properties: object properties, and datatype properties. Type information can be made available for object properties as their object values are URIs. Typed object properties allow richer semantic analysis compared to datatype properties, whose object values are literals. In fact, many datatype properties can be analyzed to suggest types selected from a schema similar to object properties, enabling their wider use in applications. In this paper, we propose an approach to glean types for datatype properties by processing their object values. We show the usefulness of generated types by utilizing them to group facts on the basis of their semantics in computing diversified entity summaries by extending a state-of-the-art summarization algorithm.

Keywords

Type inference Datatype properties RDF triples Feature grouping and ranking Entity summarization Dataset enrichment 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kalpa Gunaratna
    • 1
  • Krishnaprasad Thirunarayan
    • 1
  • Amit Sheth
    • 1
  • Gong Cheng
    • 2
  1. 1.Kno.e.sisWright State UniversityDaytonUSA
  2. 2.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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