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International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources

IKC 2016: Semantic Keyword-Based Search on Structured Data Sources pp 49–61Cite as

Back to the Sketch-Board: Integrating Keyword Search, Semantics, and Information Retrieval

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10151))

Abstract

We reproduce recent research results combining semantic and information retrieval methods. Additionally, we expand the existing state of the art by combining the semantic representations with IR methods from the probabilistic relevance framework. We demonstrate a significant increase in performance, as measured by standard evaluation metrics.

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Notes

  1. 1.

    http://pikes.fbk.eu/.

  2. 2.

    http://www.alchemyapi.com/.

References

  1. Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. TOIS (2002)

    Google Scholar 

  2. Bergamaschi, S., Guerra, F., Vincini, M.: A peer-to-peer information system for the semantic web. In: Moro, G., Sartori, C., Singh, M.P. (eds.) AP2PC 2003. LNCS (LNAI), vol. 2872, pp. 113–122. Springer, Heidelberg (2004). doi:10.1007/978-3-540-25840-7_12

    Chapter  MATH  Google Scholar 

  3. Corcoglioniti, F., Dragoni, M., Rospocher, M., Aprosio, A.P.: Knowledge extraction for information retrieval. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 317–333. Springer, Heidelberg (2016). doi:10.1007/978-3-319-34129-3_20

    Chapter  Google Scholar 

  4. Corcoglioniti, F., Rospocher, M., Aprosio, A.P.: A 2-phase frame-based knowledge extraction framework. In: Proceeding of ACM Symposium on Applied Computing (SAC 2016), pp. 354–361 (2016)

    Google Scholar 

  5. Gangemi, A.: A comparison of knowledge extraction tools for the semantic web. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 351–366. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38288-8_24

    Chapter  Google Scholar 

  6. Gonzalo, J., Verdejo, F., Chugur, I., Cigarrán, J.M.: Indexing with wordnet synsets can improve text retrieval. CoRR cmp-lg/9808002 (1998). http://arxiv.org/abs/cmp-lg/9808002

  7. Jones, K.S.: Information Retrieval Experiment. Butterworths (1981)

    Google Scholar 

  8. Lafferty, J.D., Zhai, C.: Probabilistic relevance models based on document and query generation. In: Language modeling and information retrieval (2003)

    Google Scholar 

  9. Lipani, A., Lupu, M., Hanbury, A., Aizawa, A.: Verboseness fission for BM25 document length normalization. In: Proceeding of ICTIR (2015)

    Google Scholar 

  10. Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009)

    Article  Google Scholar 

  11. Maron, M.E., Kuhns, J.L.: On relevance, probabilistic indexing and information retrieval. J. ACM 7(3), 216–244 (1960)

    Article  Google Scholar 

  12. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  13. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 275–281, NY, USA (1998). http://doi.acm.org/10.1145/290941.291008

  14. Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retrieval 3(4), 333–389 (2009)

    Article  Google Scholar 

  15. Robertson, S.E.: The Probability Ranking Principle in IR. Journal of Documentation 33(4) (1977)

    Google Scholar 

  16. Tsatsaronis, G., Panagiotopoulou, V.: A generalized vector space model for text retrieval based on semantic relatedness. In: Lascarides, A., Gardent, C., Nivre, J. (eds.) EACL 2009, 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Athens, Greece, March 30 - April 3, 2009, pp. 70–78. The Association for Computer Linguistics (2009). http://www.aclweb.org/anthology/E09-3009

  17. Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth, London (1979). http://www.dcs.gla.ac.uk/Keith/Preface.html

    MATH  Google Scholar 

  18. Waitelonis, J., Exeler, C., Sack, H.: Linked data enabled generalized vector space model to improve document retrieval. In: Proceeding of 3rd International Workshop on NLP & DBpedia 2015, co-located with ISWC (2015)

    Google Scholar 

  19. Yu, J.X., Qin, L., Chang, L.: Keyword Search in Databases. Morgan & Claypool Pub, Synthesis Lectures on Data Management (2010)

    Google Scholar 

  20. Zhai, C.: Statistical language models for information retrieval a critical review. Found. Trends Inf. Retr. 2(3), 137–213 (2008). http://dx.doi.org/10.1561/1500000008

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This research is partially supported by the ADmIRE Project (FWF P25905-N23) project and the COST IC1302 KEYSTONE Action.

Author information

Authors and Affiliations

  1. University of Malta, Msida, Malta

    Joel Azzopardi

  2. Universitá di Modena e Reggio Emilia, Modena, Italy

    Fabio Benedetti & Francesco Guerra

  3. TU Wien, Vienna, Austria

    Mihai Lupu

Authors
  1. Joel Azzopardi
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  2. Fabio Benedetti
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  3. Francesco Guerra
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  4. Mihai Lupu
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Corresponding author

Correspondence to Joel Azzopardi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, Birkbeck University of London, London, UK

    Andrea Calì

  2. Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Dorian Gorgan

  3. Computer and Decision Engineering (CoDE) Department, Université Libre de Bruxelles, Brussels, Belgium

    Martín Ugarte

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Azzopardi, J., Benedetti, F., Guerra, F., Lupu, M. (2017). Back to the Sketch-Board: Integrating Keyword Search, Semantics, and Information Retrieval. In: Calì, A., Gorgan, D., Ugarte, M. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2016. Lecture Notes in Computer Science(), vol 10151. Springer, Cham. https://doi.org/10.1007/978-3-319-53640-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-53640-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53639-2

  • Online ISBN: 978-3-319-53640-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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