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Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

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Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11707))

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Abstract

A key contributor to the success of keyword search systems is a ranking mechanism that considers the importance of the retrieved documents. The notion of importance in graphs is typically computed using centrality measures that highly depend on the degree of the nodes, such as PageRank. However, in RDF graphs, the notion of importance is not necessarily related to the node degree. Therefore, this paper addresses two problems: (1) how to define importance measures in RDF graphs; (2) how to use these measures to help compile and rank results of keyword queries over RDF graphs. To solve these problems, the paper proposes a novel family of measures, called InfoRank, and a keyword search system, called QUIRA, for RDF graphs. Finally, this paper concludes with experiments showing that the proposed solution improves the quality of results in two keyword search benchmarks.

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Notes

  1. 1.

    http://dbpedia.org/sparql.

  2. 2.

    http://dblp.uni-trier.de.

References

  1. Agarwal, A., et al.: Learning to rank networked entities. In: Proceedings 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2006, pp. 14–23 (2006)

    Google Scholar 

  2. Agrawal, S., et al.: DBXplorer: a system for keyword-based search over relational databases. In: Proceedings 18th International Conference Data Engineering, pp. 5–16 (2002)

    Google Scholar 

  3. Balmin, A., et al.: ObjectRank: authority-based keyword search in databases. In: Proceedings 13th International Conference on Very Large Data Bases - Volume 30, pp. 564–575 (2004)

    Google Scholar 

  4. Bast, H., et al.: Semantic Search on Text and Knowledge Bases. Foundation and Trends® in Information Retrieval, vol. 10, no. 2–3, pp. 119–271 (2016)

    Article  Google Scholar 

  5. Bhalotia, G., et al.: Keyword searching and browsing in databases using BANKS. In: Proceedings 18th International Conference on Data Engineering, pp. 431–440. IEEE Computer Society (2002)

    Google Scholar 

  6. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  7. Chirita, P.A., et al.: Beagle ++: semantically enhanced searching and ranking on the desktop. In: The Semantic Web: Research and Applications - ESWC 2006, pp. 348–362 (2006)

    Google Scholar 

  8. Coffman, J., Weaver, A.C.: A framework for evaluating database keyword search strategies. In: Proceedings 19th ACM International Conference on Information and Knowledge Management, pp. 729–738 (2010)

    Google Scholar 

  9. De Oliveira, P., et al.: Ranking Candidate Networks of relations to improve keyword search over relational databases. In: Proceedings 31st International Conference on Data Engineering, pp. 399–410 (2015)

    Google Scholar 

  10. Ding, L., et al.: Swoogle: a search and metadata engine for the semantic web. In: Proceedings 13th ACM Conference on Information and Knowledge Management - CIKM 2004, pp. 652–659 (2004)

    Google Scholar 

  11. Elbassuoni, S., Blanco, R.: Keyword search over RDF graphs. In: Proceedings 20th ACM International Conference on Information and Knowledge Management - CIKM 2011, pp. 237–242 (2011)

    Google Scholar 

  12. Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: ranking semantic web data by tensor decomposition. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213–228. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_14

    Chapter  Google Scholar 

  13. García, G.M., et al.: RDF Keyword-based query technology meets a real-world data set. In: Proceedings 20th International Conference on Extending Database Technology (EDBT), pp. 656–667 (2017)

    Google Scholar 

  14. Graves, A., et al.: A method to rank nodes in an RDF graph. In: Proceedings 7th International Semantic Web Conference, pp. 84–85 (2008)

    Google Scholar 

  15. Harth, A., Kinsella, S., Decker, S.: Using naming authority to rank data and ontologies for web search. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 277–292. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_18

    Chapter  Google Scholar 

  16. He, H., et al.: BLINKS: ranked keyword searches on graphs. In: Proceedings 2007 ACM International Conference on Management of Data - SIGMOD 2007, pp. 305–316 (2007)

    Google Scholar 

  17. Hiemstra, D.: Information retrieval models. In: Information Retrieval: Searching in the 21st Century, pp. 1–17 (2009)

    Google Scholar 

  18. Hogan, A., et al.: ReConRank: a scalable ranking method for semantic web data with context. In: Proceedings 2nd Workshop on Scalable Semantic Web Knowledge Base System (2006)

    Google Scholar 

  19. Hristidis, V., Papakonstantinou, Y.: Discover: keyword search in relational databases. In: Proceedings 28th International Conference on Very Large Databases, pp. 670–681. Elsevier (2002)

    Google Scholar 

  20. Izquierdo, Y.T., García, G.M., Menendez, E.S., Casanova, M.A., Dartayre, F., Levy, C.H.: QUIOW: a keyword-based query processing tool for RDF datasets and relational databases. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11030, pp. 259–269. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98812-2_22

    Chapter  Google Scholar 

  21. Kasneci, G., et al.: NAGA: searching and ranking knowledge. In: Proceedings 2008 IEEE 24th International Conference on Data Engineering, pp. 953–962 (2008)

    Google Scholar 

  22. Kim, J.H., et al.: PageRank revisited: on the relationship between node degrees and node significances in different applications. In: Proceedings 5th International Workshop on Querying Graph Structured Data at EDBT/ICDT, pp. 1–8 (2016)

    Google Scholar 

  23. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  Google Scholar 

  24. Komamizu, T., Okumura, S., Amagasa, T., Kitagawa, H.: FORK: feedback-aware objectrank-based keyword search over linked data. In: Sung, W.K., et al. (eds.) Information Retrieval Technology AIRS 2017. Lecture Notes in Computer Science, vol. 10648, pp. 58–70. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70145-5_5

    Chapter  Google Scholar 

  25. Marx, E., et al.: DBtrends: exploring query logs for ranking RDF data. In: Proceedings 12th International ACM Conference on Semantic Systems, pp. 9–16 (2016)

    Google Scholar 

  26. Mirizzi, R., Ragone, A., Di Noia, T., Di Sciascio, E.: Ranking the linked data: the case of DBpedia. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 337–354. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13911-6_23

    Chapter  Google Scholar 

  27. Nie, Z., et al.: Object-level ranking. In: Proceedings 14th International Conference on World Wide Web - WWW 2005, pp. 567–674 (2005)

    Google Scholar 

  28. Oren, E., et al.: Sindice.com: a document-oriented lookup index for open linked data. Int. J. Metadata Semant. Ontol. 3(1), 37–52 (2008)

    Article  Google Scholar 

  29. Park, H., et al.: A link-based ranking algorithm for semantic web resources. J. Database Manag. 22(1), 1–25 (2011)

    Article  Google Scholar 

  30. Roa-Valverde, A.J., Sicilia, M.-A.: A survey of approaches for ranking on the web of data. Inf. Retr. 17(4), 295–325 (2014)

    Article  Google Scholar 

  31. Tran, T., et al.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In: Proceedings 25th International Conference on Data Engineering, pp. 405–416 (2009)

    Google Scholar 

  32. Turpin, A., Scholer, F.: User performance versus precision measures for simple search tasks. In: Proceedings 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11–18 (2006)

    Google Scholar 

  33. Wei, W., et al.: Rational Research model for ranking semantic entities. Inf. Sci. 181(13), 2823–2840 (2011)

    Article  Google Scholar 

  34. Yu, J.X., et al.: Keyword Search in Databases. Morgan & Claypool, San Francisco (2010)

    MATH  Google Scholar 

  35. Yumusak, S., et al.: A short survey of linked data ranking. In: Proceedings 2014 ACM Southeast Regional Conference on - ACM SE 2014, pp. 1–4 (2014)

    Google Scholar 

  36. Zenz, G., et al.: From keywords to semantic queries - Incremental query construction on the semantic web. Web Semant. Sci. Serv. Agents W.W.W. 7(3), 166–176 (2009)

    Article  Google Scholar 

  37. Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: SPARK: adapting keyword query to semantic search. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 694–707. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_50

    Chapter  Google Scholar 

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Acknowledgments

This work was partly funded by CAPES under grant 88881.134081/2016-01, by CNPq under grants 153908/2015-7, 302303/2017-0 and by FAPERJ under grant E-26-202.818/2017.

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Authors and Affiliations

  1. Department of Informatics, PUC-Rio, Rio de Janeiro, RJ, Brazil

    Elisa S. Menendez & Marco A. Casanova

  2. Computer Science Institute, UFF, Niteroi, RJ, Brazil

    Luiz A. P. Paes Leme

  3. Institut de Recherche en Informatique de Toulouse, IRIT, Toulouse, France

    Elisa S. Menendez & Mohand Boughanem

Authors
  1. Elisa S. Menendez
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  2. Marco A. Casanova
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  3. Luiz A. P. Paes Leme
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  4. Mohand Boughanem
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Corresponding author

Correspondence to Marco A. Casanova .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Menendez, E.S., Casanova, M.A., Paes Leme, L.A.P., Boughanem, M. (2019). Novel Node Importance Measures to Improve Keyword Search over RDF Graphs. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-27618-8_11

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