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Exploratory Professional Search through Semantic Post-Analysis of Search Results

  • Pavlos Fafalios
  • Yannis Tzitzikas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8830)

Abstract

Professional Search is usually a recall-oriented problem. For helping the user to get efficiently a concise overview, to quickly restrict the search space and to make sense of the results, in this article we present an exploratory strategy for professional search that is based on semantic post-analysis of the classical search results (of keyword based queries). The described strategy can exploit the metadata that are already available, as well as the results of textual clustering and entity mining that can be performed at query time. The outcome of this process (i.e. metadata, clusters and entities grouped in categories) complement the ranked list of results produced from the core search engine with useful information for the user. This extra information is useful not only for providing a concise overview of the search results, but also for supporting a faceted and session-based interaction scheme that allows the users to restrict their focus gradually and to explore other related information. To tackle the corresponding configuration requirements of this process, we show how one can exploit the (constantly evolving) Linked Data for specifying the entities of interest and for providing further information about the identified entities. In this article, apart from detailing the steps of this process, we present applications of this approach in the marine domain and in the domain of patent search.

Keywords

exploratory search professional search entity mining and exploration linked data faceted search 

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References

  1. 1.
  2. 2.
    Lupedia enrichment service, ontotext, http://lupedia.ontotext.com/
  3. 3.
    Opencalais, thomson reuters, http://www.opencalais.com/
  4. 4.
  5. 5.
    Sparql 1.1 federated query, w3c recommendation (March 21, 2013), http://www.w3.org/TR/sparql11-federated-query/
  6. 6.
    Sparql query language for rdf, w3c recommendation (January 15, 2008), http://www.w3.org/TR/rdf-sparql-query/
  7. 7.
  8. 8.
    Allocca, C., d’Aquin, M., Motta, E.: Impact of using relationships between ontologies to enhance the ontology search results. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 453–468. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Ambrus, O., Möller, K., Handschuh, S.: Konduit vqb: A visual query builder for sparql on the social semantic desktop. In: Workshop on Visual Interfaces to the Social and Semantic Web (2010)Google Scholar
  10. 10.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Bast, H., Chitea, A., Suchanek, F., Weber, I.: Ester: Efficient search on text, entities, and relations. In: 30th International ACM SIGIR Conference on Research and Development in Information Retrieval (2007)Google Scholar
  12. 12.
    Beckers, T., Dungs, S., Fuhr, N., Jordan, M., Kriewel, S., Tran, V.T.: ezdl: An interactive search and evaluation system. In: SIGIR 2012 Workshop on Open Source Information Retrieval, pp. 9–16 (2012)Google Scholar
  13. 13.
    Bishop, B., Kiryakov, A., Ognyanov, D., Peikov, I., Tashev, Z., Velkov, R.: Factforge: A fast track to the web of data. Semantic Web 2(2), 157–166 (2011)Google Scholar
  14. 14.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. International Journal on Semantic Web and Information Systems (IJSWIS) 5(3), 1–22 (2009)CrossRefGoogle Scholar
  15. 15.
    Bonino, D., Ciaramella, A., Corno, F.: Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Information 32(1) (2010)Google Scholar
  16. 16.
    Bontcheva, K., Tablan, V., Maynard, D., Cunningham, H.: Evolving gate to meet new challenges in language engineering. Natural Language Engineering 10(3-4), 349–373 (2004)CrossRefGoogle Scholar
  17. 17.
    Candela, L., Castelli, D., Pagano, P.: gcube: A service-oriented application framework on the grid. ERCIM News 72, 48–49 (2008)Google Scholar
  18. 18.
    Cardellini, V., Colajanni, M., Yu, P.S.: Dynamic load balancing on web-server systems. IEEE Internet Computing 3(3), 28–39 (1999)CrossRefGoogle Scholar
  19. 19.
    Carpineto, C., DAmico, M., Romano, G.: Evaluating subtopic retrieval methods: Clustering versus diversification of search results. Information Processing and Management 48(2), 358–373 (2012)CrossRefGoogle Scholar
  20. 20.
    Charton, E., Gagnon, M., Ozell, B.: Automatic semantic web annotation of named entities. In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS, vol. 6657, pp. 74–85. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Cheng, T., Chang, K.: Beyond pages: supporting efficient, scalable entity search with dual-inversion index. In: 13th International Conference on Extending Database Technology (2010)Google Scholar
  22. 22.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, ACL 2002 (2002)Google Scholar
  23. 23.
    Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  24. 24.
    Franconi, M.T.E., Guagliardo, P.: Quelo: A nl-based intelligent query interface. In: Procs of the Second Workshop on Controlled Natural Languages (CNL 2010) (2010)Google Scholar
  25. 25.
    Fafalios, P., Kitsos, I., Marketakis, Y., Baldassarre, C., Salampasis, M., Tzitzikas, Y.: Web searching with entity mining at query time. In: Proceedings of the 5th Information Retrieval Facility Conference, Vienna, Austria (July 2012)Google Scholar
  26. 26.
    Fafalios, P., Kitsos, I., Tzitzikas, Y.: Scalable, flexible and generic instant overview search. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 333–336. ACM (2012)Google Scholar
  27. 27.
    Fafalios, P., Salampasis, M., Tzitzikas, Y.: Exploratory patent search with faceted search and configurable entity mining. In: 1st International Workshop on Integrating IR Technologies for Professional Search (ECIR 2013 Workshop) (2013)Google Scholar
  28. 28.
    Fafalios, P., Tzitzikas, Y.: Exploiting available memory and disk for scalable instant overview search. In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds.) WISE 2011. LNCS, vol. 6997, pp. 101–115. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  29. 29.
    Fafalios, P., Tzitzikas, Y.: Post-analysis of keyword-based search results using entity mining, linked data and link analysis at query time. In: 2014 IEEE Eighth International Conference on Semantic Computing (ICSC 2014), Newport Beach, California, USA, June 16-18. IEEE (2014)Google Scholar
  30. 30.
    Ferré, S., Hermann, A.: Semantic search: reconciling expressive querying and exploratory search. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 177–192. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  31. 31.
    Froese, R., Pauly, D.: Fishbase, http://www.fishbase.org/
  32. 32.
    Halevy, A.Y.: Answering queries using views: A survey. The VLDB Journa–The International Journal on Very Large Data Bases 10(4), 270–294 (2001)CrossRefzbMATHGoogle Scholar
  33. 33.
    Jiménez-Ruiz, E., Cuenca Grau, B., Horrocks, I., Berlanga, R.: Ontology integration using mappings: Towards getting the right logical consequences. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 173–187. Springer, Heidelberg (2009)Google Scholar
  34. 34.
    Joho, H., Azzopardi, L., Vanderbauwhede, W.: A survey of patent users: an analysis of tasks, behavior, search functionality and system requirements. In: Procs of the 3rd Symposium on Information Interaction in Context. ACM (2010)Google Scholar
  35. 35.
    Käki, M.: Findex: search result categories help users when document ranking fails. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2005)Google Scholar
  36. 36.
    Käki, M., Aula, A.: Findex: Improving search result use through automatic filtering categories. Interacting with Computers 17(2), 187–206 (2005)CrossRefGoogle Scholar
  37. 37.
    Kitsos, I., Magoutis, K., Tzitzikas, Y.: Scalable entity-based summarization of web search results using mapreduce. Distributed and Parallel Databases, 1–42 (2013)Google Scholar
  38. 38.
    Kohn, A., Bry, F., Manta, A., Ifenthaler, D.: Professional Search: Requirements, Prototype and Preliminary Experience Report, 195–202 (2008)Google Scholar
  39. 39.
    Kopidaki, S., Papadakos, P., Tzitzikas, Y.: STC+ and NM-STC: Two novel online results clustering methods for web searching. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 523–537. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  40. 40.
    Kules, B., Capra, R., Banta, M., Sierra, T.: What do exploratory searchers look at in a faceted search interface? In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 313–322. ACM (2009)Google Scholar
  41. 41.
    Manolis, N., Tzitzikas, Y.: Interactive Exploration of Fuzzy RDF Knowledge Bases. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 1–16. Springer, Heidelberg (2011)Google Scholar
  42. 42.
    Marchionini, G.: Exploratory search: from finding to understanding. Communications of the ACM (2006)Google Scholar
  43. 43.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8. ACM (2011)Google Scholar
  44. 44.
    Papadakos, P., Armenatzoglou, N., Kopidaki, S., Tzitzikas, Y.: On exploiting static and dynamically mined metadata for exploratory web searching. Knowledge and Information Systems 30, 493–525 (2012)CrossRefGoogle Scholar
  45. 45.
    Papadakos, P., Kopidaki, S., Armenatzoglou, N., Tzitzikas, Y.: Exploratory web searching with dynamic taxonomies and results clustering. In: Agosti, M., Borbinha, J., Kapidakis, S., Papatheodorou, C., Tsakonas, G. (eds.) ECDL 2009. LNCS, vol. 5714, pp. 106–118. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  46. 46.
    Piroi, F., Lupu, M., Hanbury, A., Zenz, V.: Clef-ip 2011: Retrieval in the intellectual property domain. In: CLEF (Notebook Papers/Labs/Workshop) (2011)Google Scholar
  47. 47.
    Pratt, W., Fagan, L.: The usefulness of dynamically categorizing search results. Journal of the American Medical Informatics Association 7(6), 605–617 (2000)CrossRefGoogle Scholar
  48. 48.
    Russell, A., Smart, P.R., Braines, D., Shadbolt, N.R.: Nitelight: A graphical tool for semantic query construction. In: Semantic Web User Interaction Workshop (SWUI 2008) (April 2008)Google Scholar
  49. 49.
    Sacco, G., Tzitzikas, Y.: Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience, vol. 25. Springer (2009)Google Scholar
  50. 50.
    Sahami, M., Yusufali, S., Baldonaldo, M.Q.: Sonia: A service for organizing networked information autonomously. In: Proceedings of the Third ACM Conference on Digital Libraries, pp. 200–209. ACM (1998)Google Scholar
  51. 51.
    Salampasis, M., Hanbury, A.: A generalized framework for integrated professional search systems. In: Lupu, M., Kanoulas, E., Loizides, F. (eds.) IRFC 2013. LNCS, vol. 8201, pp. 99–110. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  52. 52.
    Suchanek, F., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: Procs of the 16th World Wide Web Conf., pp. 697–706 (2007)Google Scholar
  53. 53.
    Tzitzikas, Y., et al.: Integrating heterogeneous and distributed information about marine species through a top level ontology. In: Garoufallou, E., Greenberg, J. (eds.) MTSR 2013. CCIS, vol. 390, pp. 289–301. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  54. 54.
    Tzitzikas, Y., Meghini, C.: Ostensive automatic schema mapping for taxonomy-based peer-to-peer systems. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS (LNAI), vol. 2782, pp. 78–92. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  55. 55.
    Tzitzikas, Y., Spyratos, N., Constantopoulos, P.: Mediators over taxonomy-based information sources. The VLDB Journal–The International Journal on Very Large Data Bases 14(1), 112–136 (2005)CrossRefGoogle Scholar
  56. 56.
    Umbrich, J., Karnstedt, M., Hogan, A., Parreira, J.X.: Hybrid sparql queries: fresh vs. fast results. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 608–624. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  57. 57.
    Weninger, T., Danilevsky, M., Fumarola, F., Hailpern, J., Han, J., Johnston, T., Kallumadi, S., Kim, H., Li, Z., McCloskey, D., et al.: Winacs: Construction and analysis of web-based computer science information networks. In: ACM SIGMOD International Conference on Management of Data (2011)Google Scholar
  58. 58.
    White, R., Kules, B., Drucker, S., Schraefel, M.: Supporting exploratory search. Communications of the ACM 49(4) (2006)Google Scholar
  59. 59.
    Wilson, M., et al.: A longitudinal study of exploratory and keyword search. In: Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2008, pp. 52–56. ACM (2008)Google Scholar
  60. 60.
    Zamir, O., Etzioni, O.: Web document clustering: A feasibility demonstration. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pavlos Fafalios
    • 1
    • 2
  • Yannis Tzitzikas
    • 1
    • 2
  1. 1.Institute of Computer ScienceFORTH-ICSGreece
  2. 2.Computer Science DepartmentUniversity of CreteGreece

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