DBtrends: Exploring Query Logs for Ranking RDF Data

ABSTRACT

Many ranking methods have been proposed for RDF data. These methods often use the structure behind the data to measure its importance. Recently, some of these methods have started to explore information from other sources such as the Wikipedia page graph for better ranking RDF data. In this work, we propose DBtrends, a ranking function based on query logs. We extensively evaluate the application of different ranking functions for entities, classes, and properties across two different countries as well as their combination. Thereafter, we propose MIXED-RANK, a ranking function that combines DBtrends with the best-evaluated entity ranking function. We show that: (i) MIXED-RANK outperforms state-of-the-art entity ranking functions, and; (ii) query logs can be used to improve RDF ranking functions.

References

1. M. Alsarem, P.-E. Portier, S. Calabretto, and H. Kosch. Ranking entities in the age of two webs, an application to semantic snippets. In The Semantic Web. Latest Advances and New Domains, volume 9088 of Lecture Notes in Computer Science, pages 541--555. Springer International Publishing, 2015.
2. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Proceedings of the Seventh International Conference on World Wide Web 7, WWW7, pages 107--117, Amsterdam, The Netherlands, The Netherlands, 1998. Elsevier Science Publishers B. V.
3. G. Cheng, T. Tran, and Y. Qu. RELIN: Relatedness and Informativeness-based Centrality for Entity Summarization. In Proceedings of the 10th International Conference on The Semantic Web -Volume Part I, ISWC'11, pages 114--129, Berlin, Heidelberg, 2011. Springer-Verlag.
4. G. Cheng, D. Xu, and Y. Qu. Summarizing entity descriptions for effective and efficient human-centered entity linking. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 184--194. International World Wide Web Conferences Steering Committee, 2015.
5. S. F. De Araújo and D. Schwabe. Explorator: a tool for exploring RDF data through direct manipulation. In Linked data on the web WWW2009 workshop (LDOW2009), 2009.
6. L. Ding, R. Pan, T. Finin, A. Joshi, Y. Peng, and P. Kolari. The Semantic Web -- ISWC 2005: 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005. Proceedings, chapter Finding and Ranking Knowledge on the Semantic Web, pages 156--170. Springer Berlin Heidelberg, Berlin, Heidelberg, 2005.
7. G. Erkan and D. R. Radev. Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res., 22(1):457--479, Dec. 2004.
8. T. Franz, A. Schultz, S. Sizov, and S. Staab. TripleRank: Ranking Semantic Web Data by Tensor Decomposition. In Proceedings of the 8th International Semantic Web Conference, ISWC '09, pages 213--228, Berlin, Heidelberg, 2009. Springer-Verlag.
9. A. Hogan, A. Harth, and S. Decker. ReConRank: A Scalable Ranking Method for Semantic Web Data with Context. In In 2nd Workshop on Scalable Semantic Web Knowledge Base Systems, 2006.
10. K. Järvelin. Cumulated gain-based evaluation of ir techniques. volume 20, page 2002, 2002.
11. M. Kendall. Rank correlation methods. Griffin, London, 1948.
12. J. M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604--632, Sept. 1999.
13. R. Kumar and S. Vassilvitskii. Generalized distances between rankings. In WWW, WWW '10, pages 571--580. ACM, 2010.
14. S. Lawrence. Context in web search. IEEE Data Eng. Bull., 23(3):25--32, 2000.
15. E. Marx, A. Zaveri, M. Mohammed, S. Rautenberg, J. Lehmann, A.-C. N. Ngomo, and G. Cheng. DBtrends: Publishing and Benchmarking RDF Ranking Functions. In 2nd International Workshop on Summarizing and Presenting Entities and Ontologies, Co-located with the 13th Extended Semantic Web Conference, SUMPRE'16, 2016.
16. N. Matthijs and F. Radlinski. Personalizing web search using long term browsing history. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM '11, pages 25--34, New York, NY, USA, 2011. ACM.
17. A.-C. N. Ngomo and S. Auer. LIMES - a time-efficient approach for large-scale link discovery on the web of data. integration, 15:3, 2011.
18. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab, November 1999. Previous number = SIDL-WP-1999-0120.
19. T. Penin, H. Wang, T. Tran, and Y. Yu. The Semantic Web: 3rd Asian Semantic Web Conference, ASWC 2008, Bangkok, Thailand, December 8-11, 2008. Proceedings., chapter Snippet Generation for Semantic Web Search Engines, pages 493--507. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
20. A. Roa-Valverde and M.-A. Sicilia. A survey of approaches for ranking on the web of data. Information Retrieval, 17(4):295--325, 2014.
21. C. Rocha, D. Schwabe, and M. P. Aragao. A hybrid approach for searching in the semantic web. In Proceedings of the 13th International Conference on World Wide Web, WWW '04, pages 374--383, New York, NY, USA, 2004. ACM.
22. C. Spearman. The proof and measurement of association between two things. American Journal of Psychology, 15:88--103, 1904.
23. A. Thalhammer and A. Rettinger. Browsing DBpedia entities with summaries. In The Semantic Web: ESWC 2014 Satellite Events, pages 511--515. Springer, 2014.
24. V. N. Vapnik and V. Vapnik. Statistical learning theory, volume 1. Wiley New York, 1998.
25. X. Zhang, G. Cheng, and Y. Qu. Ontology summarization based on rdf sentence graph. In Proceedings of the 16th International Conference on World Wide Web, WWW '07, pages 707--716, New York, NY, USA, 2007. ACM.
26. Z. Zhuang and S. Cucerzan. Re-ranking search results using query logs. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM '06, pages 860--861, New York, NY, USA, 2006. ACM.