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FORK: Feedback-Aware ObjectRank-Based Keyword Search over Linked Data

  • Takahiro Komamizu
  • Sayami Okumura
  • Toshiyuki Amagasa
  • Hiroyuki Kitagawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10648)

Abstract

Ranking quality for keyword search over Linked Data (LD) is crucial when users look for entities from LD, since datasets in LD have complicated structures as well as much contents. This paper proposes a keyword search method, FORK, which ranks entities in LD by ObjectRank, a well-known link-structure analysis algorithm that can deal with different types of nodes and edges. The first attempt of applying ObjectRank to LD search reveals that ObjectRank with inappropriate settings gives worse ranking results than PageRank which is equivalent to ObjectRank with all the same authority transfer weights. Therefore, deriving appropriate authority transfer weights is the most important issue for encouraging ObjectRank in LD search. FORK involves a relevance feedback algorithm to modify the authority transfer weights according with users’ relevance judgements for ranking results. The experimental evaluation of ranking qualities using an entity search benchmark showcases the effectiveness of FORK, and it proves ObjectRank is more feasible raking method for LD search than PageRank and other comparative baselines including information retrieval techniques and graph analytic methods.

Keywords

Keyword Search over Linked Data ObjectRank-based ranking Relevance feedback Authority transfer graph modification 

Notes

Acknowledgement

This research was partly supported by the program Research and Development on Real World Big Data Integration and Analysis of RIKEN, Japan, and Fujitsu Laboratory, APE29707.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Takahiro Komamizu
    • 1
  • Sayami Okumura
    • 1
  • Toshiyuki Amagasa
    • 1
  • Hiroyuki Kitagawa
    • 1
  1. 1.University of TsukubaTsukubaJapan

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