Advertisement

Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs

  • Federico Bianchi
  • Matteo Palmonari
  • Marco Cremaschi
  • Elisabetta Fersini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)

Abstract

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.

Notes

Acknowledgement

We thank our colleague Federico Cabitza for his knowledgeable advises about the creation of the SAMU data set.

References

  1. 1.
    Bikakis, N., Sellis, T.: Exploration and visualization in the web of big linked data: a survey of the state of the art. preprint arXiv:1601.08059 (2016)
  2. 2.
    Redondo-García, J.L., Hildebrand, M., Romero, L.P., Troncy, R.: Augmenting TV newscasts via entity expansion. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 472–476. Springer, Cham (2014). doi: 10.1007/978-3-319-11955-7_69CrossRefGoogle Scholar
  3. 3.
    Tietz, T., Jäger, J., Waitelonis, J., Sack, H.: Semantic annotation and information visualization for blogposts with Refer. In: VOILA 2016, vol. 1704, pp. 28–40 (2016)Google Scholar
  4. 4.
    Palmonari, M., Uboldi, G., Cremaschi, M., Ciminieri, D., Bianchi, F.: DaCENA: serendipitous news reading with data contexts. In: Gandon, F., Guéret, C., Villata, S., Breslin, J., Faron-Zucker, C., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 133–137. Springer, Cham (2015). doi: 10.1007/978-3-319-25639-9_26CrossRefGoogle Scholar
  5. 5.
    Pirrò, G.: Explaining and suggesting relatedness in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 622–639. Springer, Cham (2015). doi: 10.1007/978-3-319-25007-6_36CrossRefGoogle Scholar
  6. 6.
    Cheng, G., Zhang, Y., Qu, Y.: Explass: exploring associations between entities via Top-K ontological patterns and facets. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 422–437. Springer, Cham (2014). doi: 10.1007/978-3-319-11915-1_27CrossRefGoogle Scholar
  7. 7.
    Chen, N., Prasanna, V.K.: Learning to rank complex semantic relationships. IJSWIS 8(4), 1–19 (2012)Google Scholar
  8. 8.
    Gray, J., Chambers, L., Bounegru, L.: The Data Journalism Handbook. O’Reilly Media Inc., Sebastopol (2012)Google Scholar
  9. 9.
    Kang, J., Ryu, K.R., Kwon, H.-C.: Using cluster-based sampling to select initial training set for active learning in text classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 384–388. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24775-3_46CrossRefGoogle Scholar
  10. 10.
    Yuan, W., Han, Y., Guan, D., Lee, S., Lee, Y.K.: Initial training data selection for active learning. In: ICUIMC, p. 5. ACM (2011)Google Scholar
  11. 11.
    Gershman, S.J., Blei, D.M.: A tutorial on bayesian nonparametric models. J. Math. Psychol. 56(1), 1–12 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tan, P.N., et al.: Introduction to Data Mining. Pearson Education India, Upper Saddle River (2006)Google Scholar
  13. 13.
    Lee, C.-P., Lin, C.-J.: Large-scale linear ranksvm. Neural Comput. 26(4), 781–817 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liu, T.-Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)CrossRefGoogle Scholar
  15. 15.
    Donmez, P., Carbonell, J.G.: Active sampling for rank learning via optimizing the area under the ROC curve. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 78–89. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-00958-7_10CrossRefGoogle Scholar
  16. 16.
    Qian, B., Li, H., Wang, J., Wang, X., Davidson, I.: Active learning to rank using pairwise supervision. In: SIAM International Conference Data Mining, pp. 297–305. SIAM (2013)CrossRefGoogle Scholar
  17. 17.
    Thalhammer, A., Rettinger, A.: PageRank on Wikipedia: towards general importance scores for entities. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 227–240. Springer, Cham (2016). doi: 10.1007/978-3-319-47602-5_41CrossRefGoogle Scholar
  18. 18.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. JACM 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Cabitza, F., Locoro, A.: Questionnaires in the design and evaluation of community-oriented technologies. Int. J. Web-Based Commun. 13(1), 4–35 (2017)CrossRefGoogle Scholar
  20. 20.
    Gwet, K.L.: Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. Advanced Analytics, LLC (2014)Google Scholar
  21. 21.
    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). doi: 10.1007/978-3-642-10543-2_21CrossRefGoogle Scholar
  22. 22.
    Fang, L., Sarma, A.D., Yu, C., Bohannon, P.: Rex: explaining relationships between entity pairs. Proc. VLDB 5(3), 241–252 (2011)CrossRefGoogle Scholar
  23. 23.
    Tiddi, I., d’Aquin, M., Motta, E.: Learning to assess linked data relationships using genetic programming. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 581–597. Springer, Cham (2016). doi: 10.1007/978-3-319-46523-4_35CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Federico Bianchi
    • 1
  • Matteo Palmonari
    • 1
  • Marco Cremaschi
    • 1
  • Elisabetta Fersini
    • 1
  1. 1.University of Milan - BicoccaMilanItaly

Personalised recommendations