Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

A key contributor to the success of keyword search systems is a ranking mechanism that considers the importance of the retrieved documents. The notion of importance in graphs is typically computed using centrality measures that highly depend on the degree of the nodes, such as PageRank. However, in RDF graphs, the notion of importance is not necessarily related to the node degree. Therefore, this paper addresses two problems: (1) how to define importance measures in RDF graphs; (2) how to use these measures to help compile and rank results of keyword queries over RDF graphs. To solve these problems, the paper proposes a novel family of measures, called InfoRank, and a keyword search system, called QUIRA, for RDF graphs. Finally, this paper concludes with experiments showing that the proposed solution improves the quality of results in two keyword search benchmarks.

• Publication year

  2019

• Venue

  International Conference on Database and Expert Systems Applications

• Publication date

  2019-08-26

• Fields of study

  Computer Science

• Identifiers
  DOI 10.17771/pucrio.acad.37741
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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