World Wide Web

, Volume 18, Issue 5, pp 1301–1329 | Cite as

Multi-relational PageRank for tree structure sense ranking

  • Roberto Interdonato
  • Andrea TagarelliEmail author


In this paper, we study the problem of structural sense ranking for tree data using a multi-relational PageRank approach. By considering multiple types of structural relations, the original tree structural context is better leveraged and hence used to improve the ranking of the senses associated to the tree elements. Upon this intuition, we advance research on the application of PageRank-style methods to semantic graphs inferred from semistructured/plain text data by developing the first PageRank-based formulations that exploit heterogeneity of links to address the problem of structural sense ranking in tree data. Experiments on a large real-world benchmark have confirmed the performance improvement hypothesis of our proposed multi-relational approach.


Tree-structured data structural sense ranking heterogeneous information networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11280_2014_302_MOESM1_ESM.pdf (818 kb)
(PDF 818 KB)


  1. 1.
    Agirre, E., Soroa, A.: Using the Multilingual Central Repository for Graph-Based Word Sense Disambiguation Proc. Int. Conf. on Language Resources and Evaluation (LREC), pp. 1388–1392 (2008)Google Scholar
  2. 2.
    Agirre, E., Soroa, A.: Personalizing PageRank for Word Sense Disambiguation Proc. 12th Conf. of the European Chapter of the Association for Computational Linguistics (EACL), pp. 33–41 (2009)Google Scholar
  3. 3.
    Avrachenkov, K., Litvak, N., Nemirovsky, D., Osipova, N.: Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient. SIAM J. Numer. Anal. 45 (2), 890–904 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Balmin, A., Hristidis, V., Papakonstantinou, Y.: ObjectRank: Authority-Based Keyword Search in Databases Proc. Int. Conf. on Very Large Data Bases (VLDB), pp. 564–575 (2004)Google Scholar
  5. 5.
    Davis, D.A., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks Proc. Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM), pp. 281–288 (2011)Google Scholar
  6. 6.
    Deng, H., Han, J., Lyu, M.R., King, I.: Modeling and exploiting heterogeneous bibliographic networks for expertise ranking Proc. Int. Joint Conf. on Digital Libraries (JCDL), pp. 71–80 (2012)Google Scholar
  7. 7.
    Erkan, G., Radev, D.R.: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. J. Artif. Intell. Res. (JAIR) 22, 457–479 (2004)Google Scholar
  8. 8.
    Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 1606–1611 (2007)Google Scholar
  9. 9.
    Gollapalli, S.D., Mitra, P., Giles, C.L.: Ranking authors in digital libraries Proc. Int. Joint Conf. on Digital Libraries (JCDL), pp. 251–254 (2011)Google Scholar
  10. 10.
    Gracia, J., Mena, E.: Web-Based Measure of Semantic Relatedness Proc. Int. Conf. on Web Information Systems Engineering (WISE), pp. 136–150 (2008)Google Scholar
  11. 11.
    Helou, S.E., Salzmann, C., Gillet, D.: The 3A Personalized, Contextual and Relation-based Recommender System. J. UCS 16 (16), 2179–2195 (2010)Google Scholar
  12. 12.
    Interdonato, R., Tagarelli, A.: Multi-relational PageRank for Tree Structure Sense Ranking Proc. Int. Conf. on Web Information Systems Engineering (WISE) - Part I, pp. 306–319 (2013)Google Scholar
  13. 13.
    Kashyap, A., Amini, R., Hristidis, V.: SonetRank: leveraging social networks to personalize search Proc. ACM Conf. on Information and Knowledge Management (CIKM), pp. 2045–2049 (2012)Google Scholar
  14. 14.
    Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn 81 (1), 53–67 (2010)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Lee, S., Song, S., Kahng, M., Lee, D., Lee, S.: Random walk based entity ranking on graph for multidimensional recommendation Proc. ACM Conf. on Recommender Systems (RecSys), pp. 93–100 (2011)Google Scholar
  16. 16.
    Li, L., Xu, G., Zhang, Y., Kitsuregawa, M.: Random walk based rank aggregation to improving web search. Knowl. Based Syst. 24 (7), 943–951 (2011)CrossRefGoogle Scholar
  17. 17.
    Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Text Proc. European Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 404–411 (2004)Google Scholar
  18. 18.
    Mihalcea, R., Tarau, P., Figa, E.: PageRank on Semantic Networks, with Application to Word Sense Disambiguation Proc. 20th Int. Conf. on Computational Linguistics (COLING) (2004)Google Scholar
  19. 19.
    Nie, Z., Zhang, Y., Wen, J.R., Ma, W.Y.: Object-level ranking: bringing order to Web objects Proc. ACM Conf. on World Wide Web (WWW), pp. 567–574 (2005)Google Scholar
  20. 20.
    Ramage, D., Rafferty, A.N., Manning, C.D.: Random Walks for Text Semantic Similarity Proc. ACL Workshop on Graph-based Methods for Natural Language Processing, pp. 23–31 (2009)Google Scholar
  21. 21.
    Sun, Y., Han, J.: Mining Heterogeneous Information Networks: Principles and Methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers (2012)Google Scholar
  22. 22.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. Proceedings of the VLDB Endowment (PVLDB) 4 (11), 992–1003 (2011)Google Scholar
  23. 23.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: RankClus: integrating clustering with ranking for heterogeneous information network analysis Proc. Int. Conf. on Extending Database Technology (EDBT), pp. 565–576 (2009)Google Scholar
  24. 24.
    Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema Proc. ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 797–806 (2009)Google Scholar
  25. 25.
    Tagarelli, A.: Exploring Dictionary-based Semantic Relatedness in Labeled Tree Data. Inf. Sci. 220, 244–268 (2013)CrossRefGoogle Scholar
  26. 26.
    Tagarelli, A., Gullo, F.: Evaluating PageRank methods for structural sense ranking in labeled tree data Proc. 2nd Int. Conf. on Web Intelligence, Mining and Semantics (WIMS) 36 (2012)Google Scholar
  27. 27.
    Tsatsaronis, G., Varlamis, I.: Nørvåg, K.: SemanticRank: Ranking Keywords and Sentences Using Semantic Graphs Proc. Int. Conf. on Computational Linguistics (COLING), pp. 1074–1082 (2010)Google Scholar
  28. 28.
    Wang, Y., Lin, X., Zhang, Q.: Towards metric fusion on multi-view data: a cross-view based graph random walk approach Proc. ACM Conf. on Information and Knowledge Management (CIKM), pp. 805–810 (2013)Google Scholar
  29. 29.
    Wu, J., Aberer, K.: Using a layered markov model for distributed web ranking computation Proc. Int. Conf. on Distributed Computing Systems, pp. 533–542 (2005)Google Scholar
  30. 30.
    Zhang, M., Feng, S., Tang, J., Ojokoh, B.A., Liu, G.: Co-Ranking Multiple Entities in a Heterogeneous Network: Integrating Temporal Factor and Users’ Bookmarks Proc. Int. Conf. on Asian Digital Libraries (ICADL), pp. 202–211 (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department Computer Engineering, Modeling, Electronics and Systems SciencesUniversity of CalabriaRende (CS)Italy

Personalised recommendations