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SEED V3: Entity-Oriented Exploratory Search in Knowledge Graphs on Tablets

  • Jun Chen
  • Mingrui Shao
  • Yueguo Chen
  • Xiaoyong Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11158)

Abstract

Entity-oriented information access is becoming a key enabler for next-generation information retrieval and exploration systems. Previously, researchers have demonstrated that knowledge graphs allow the exploitation of semantic correlation among entities to improve information access. However, less attention is devoted to user interfaces of tablets for exploring knowledge graphs effectively and efficiently. In this paper, we design and implement a system called SEED to support entity-oriented exploratory search in knowledge graphs on tablets. It utilizes a dataset of hundreds of thousands of film-related entities extracted from DBpedia V3.9, and applies the knowledge embedding derived from a graph embedding model to rank entities and their relevant aspects, as well as explaining the correlation among entities via their links. Moreover, it supports touch-based interactions for formulating queries rapidly.

Keywords

Knowledge Graph Knowledge presentation Graph embedding model Exploratory search User interface Tablet 

References

  1. 1.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)CrossRefGoogle Scholar
  2. 2.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  3. 3.
    Chen, J., Chen, Y., Du, X., Zhang, X., Zhou, X.: SEED: a system for entity exploration and debugging in large-scale knowledge graphs. In: ICDE, pp. 1350–1353 (2016)Google Scholar
  4. 4.
    Chen, J., Jacucci, G., Chen, Y., Ruotsalo, T.: SEED: entity oriented information search and exploration. In: IUI, pp. 137–140 (2017)Google Scholar
  5. 5.
    Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD, pp. 601–610 (2014)Google Scholar
  6. 6.
    Garcia-Molina, H., Ullman, J.D., Widom, J.: Database System Implementation. Prentice-Hall, Upper Saddle River (2000)Google Scholar
  7. 7.
    Meij, E., Balog, K., Odijk, D.: Entity linking and retrieval for semantic search. In: WSDM, pp. 683–684 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jun Chen
    • 1
    • 2
  • Mingrui Shao
    • 1
    • 2
  • Yueguo Chen
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Key Laboratory of Data Engineering and Knowledge Engineering, MOEBeijingChina

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