Towards Exploratory Relationship Search: A Clustering-Based Approach

  • Yanan Zhang
  • Gong Cheng
  • Yuzhong Qu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)


Searching and browsing relationships between entities is an important task in many domains. RDF facilitates searching by explicitly representing a relationship as a path in a graph with meaningful labels. As the Web of RDF data grows, hundreds of relationships can be found between a pair of entities, even under a small length constraint and within a single data source. To support users with various information needs in interactively exploring a large set of relationships, existing efforts mainly group the results into faceted categories. In this paper, we practice another direction of exploratory search, namely clustering. Our approach automatically groups relationships into a dynamically generated hierarchical clustering according to their schematic patterns, which also meaningfully label these clusters to effectively guide exploration and discovery. To demonstrate it, we implement our approach in the RelClus system based on DBpedia, and conduct a preliminary user study as well as a performance testing.


Association discovery Exploratory browsing Hierarchical clustering Path finding Relationship search 



This work was supported in part by the NSFC under Grant 61100040, 61170068, and 61223003, and in part by the JSNSF under Grant BK2012723. We thank the anonymous reviewers for their suggestions.


  1. 1.
    Aleman-Meza, B., Halaschek-Wiener, C., Arpinar, I.B., Ramakrishnan, C., Sheth, A.P.: Ranking complex relationships on the Semantic Web. IEEE Internet Comput. 9(3), 37–44 (2005)CrossRefGoogle Scholar
  2. 2.
    Aleman-Meza, B., Halaschek-Wiener, C., Arpinar, I.B., Sheth, A.: Context-aware semantic association ranking. In: 1st International Workshop on Semantic Web and Databases, pp. 33–50 (2003)Google Scholar
  3. 3.
    Anyanwu, K., Maduko, A., Sheth, A.: SemRank: ranking complex relationship search results on the Semantic Web. In: 14th International Conference on World Wide Web, pp. 117–127. ACM, New York (2005)Google Scholar
  4. 4.
    Anyanwu, K., Sheth, A.: \(\rho \)-Queries: enabling querying for semantic associations on the Semantic Web. In: 12th International Conference on World Wide Web, pp. 690–699. ACM, New York (2003)Google Scholar
  5. 5.
    Brooke, J.: SUS: a ‘quick and dirty’ usability scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClelland, I.L. (eds.) Usability Evaluation in Industry, pp. 189–194. Taylor & Francis, London (1996)Google Scholar
  6. 6.
    Carpineto, C., Osiński, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Comput. Surv. 41(3), 17 (2009)CrossRefGoogle Scholar
  7. 7.
    Chen, N., Prasanna, V.K.: Learning to rank complex semantic relationships. Int. J. Semant. Web Inf. Syst. 8(4), 1–19 (2012)CrossRefzbMATHGoogle Scholar
  8. 8.
    Fang, L., Das Sarma, A., Yu, C., Bohannon, P.: REX: explaining relationships between entity pairs. Proc. VLDB Endow. 5(3), 241–252 (2011)Google Scholar
  9. 9.
    Gubichev, A., Neumann, T.: Path query processing on very large RDF graphs. In: 14th International Workshop on the Web and Databases (2011)Google Scholar
  10. 10.
    Hearst, M.A.: Clustering versus faceted categories for information exploration. Comm. ACM 49(4), 59–61 (2006)CrossRefGoogle Scholar
  11. 11.
    Heim, P., Hellmann, S., Lehmann, J., Lohmann, S., Stegemann, T.: RelFinder: revealing relationships in RDF knowledge bases. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 182–187. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  12. 12.
    Heim, Philipp, Lohmann, Steffen, Stegemann, Timo: Interactive relationship discovery via the Semantic Web. In: Aroyo, Lora, Antoniou, Grigoris, Hyvönen, Eero, ten Teije, Annette, Stuckenschmidt, Heiner, Cabral, Liliana, Tudorache, Tania (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 303–317. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  13. 13.
    Hildebrand, M., van Ossenbruggen, J., Hardman, L.: /facet: a browser for heterogeneous Semantic Web repositories. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 272–285. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  14. 14.
    Janik, M., Kochut, K.J.: BRAHMS: a workbench RDF store and high performance memory system for semantic association discovery. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 431–445. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  15. 15.
    Marchionini, G.: Exploratory search: from finding to understanding. Comm. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  16. 16.
    Oren, E., Delbru, R., Decker, S.: Extending faceted navigation for RDF data. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 559–572. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  17. 17.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on Artificial Intelligence, vol. 1, pp. 448–453. Morgan Kaufmann, San Francisco (1995)Google Scholar
  18. 18.
    Rutledge, L., van Ossenbruggen, J., Hardman, L.: Making RDF presentable: integrated global and local Semantic Web browsing. In: 14th International Conference on World Wide Web, pp. 199–206. ACM, New York (2005)Google Scholar
  19. 19.
    Schraefel, M.C., Wilson, M., Russell, A., Smith, D.A.: mSpace: improving information access to multimedia domains with multimodal exploratory search. Comm. ACM 49(4), 47–49 (2006)CrossRefGoogle Scholar
  20. 20.
    Sinha, V., Karger, D.R.: Magnet: supporting navigation in semistructured data environments. In: 2005 ACM SIGMOD International Conference on Management of Data, pp. 97–106. ACM, New York (2005)Google Scholar
  21. 21.
    Viswanathan, V., Ilango, K.: Finding relevant semantic association paths through user-specific intermediate entities. Hum.-centric Comput. Inf. Sci. 2, 9 (2012)CrossRefGoogle Scholar
  22. 22.
    Viswanathan, V., Ilango, K.: Ranking semantic relationships between two entities using personalization in context specification. Inf. Sci. 207, 35–49 (2012)CrossRefGoogle Scholar
  23. 23.
    Wagner, A., Ladwig, G., Tran, T.: Browsing-oriented semantic faceted search. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 303–319. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  24. 24.
    Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  25. 25.
    Zhou, M., Pan, Y., Wu, Y.: Conkar: constraint keyword-based association discovery. In: 20th ACM International Conference on Information and Knowledge Management, pp. 2553–2556. ACM, New York (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

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