On the Reproducibility of the TAGME Entity Linking System

  • Faegheh Hasibi
  • Krisztian Balog
  • Svein Erik Bratsberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

Reproducibility is a fundamental requirement of scientific research. In this paper, we examine the repeatability, reproducibility, and generalizability of TAGME, one of the most popular entity linking systems. By comparing results obtained from its public API with (re)implementations from scratch, we obtain the following findings. The results reported in the TAGME paper cannot be repeated due to the unavailability of data sources. Part of the results are reproducible through the provided API, while the rest are not reproducible. We further show that the TAGME approach is generalizable to the task of entity linking in queries. Finally, we provide insights gained during this process and formulate lessons learned to inform future reducibility efforts.

References

  1. 1.
    Carmel, D., Chang, M.-W., Gabrilovich, E., Hsu, B.-J.P., Wang, K.: ERD’14: Entity recognition and disambiguation challenge. SIGIR Forum 48(2), 63–77 (2014)CrossRefGoogle Scholar
  2. 2.
    Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Dexter: An open source framework for entity linking. In: Proceedings of the Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval, pp. 17–20 (2013)Google Scholar
  3. 3.
    Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Learning relatedness measures for entity linking. In: Proceedings of CIKM 2013, pp. 139–148 (2013)Google Scholar
  4. 4.
    Chiu, Y.-P., Shih, Y.-S., Lee, Y.-Y., Shao, C.-C., Cai, M.-L., Wei, S.-L., Chen, H.-H.: NTUNLP approaches to recognizing and disambiguating entities in long and short text at the ERD challenge 2014. In: Proceedings of Entity Recognition & Disambiguation Workshop, pp. 3–12 (2014)Google Scholar
  5. 5.
    Cornolti, M., Ferragina, P., Ciaramita, M.: A framework for benchmarking entity-annotation systems. In: Proceedings of WWW 2013, pp. 249–260 (2013)Google Scholar
  6. 6.
    Cornolti, M., Ferragina, P., Ciaramita, M., Schütze, H., Rüd, S.: The SMAPH system for query entity recognition and disambiguation. In: Proceedings of Entity Recognition & Disambiguation Workshop, pp. 25–30 (2014)Google Scholar
  7. 7.
    Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of EMNLP-CoNLL 2007, pp. 708–716 (2007)Google Scholar
  8. 8.
    Ferragina, P., Scaiella, U.: TAGME: On-the-fly annotation of short text fragments (by Wikipedia entities). In: Proceedings of CIKM 2010, pp. 1625–1628 (2010)Google Scholar
  9. 9.
    Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with Wikipedia pages. CoRR (2010). abs/1006.3498Google Scholar
  10. 10.
    Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: A graph-based method. In: Proceedings of SIGIR 2011, pp. 765–774 (2011)Google Scholar
  11. 11.
    Hasibi, F., Balog, K., Bratsberg, S.E.: Entity linking in queries: tasks and evaluation. In: Proceedings of the ICTIR 2015, pp. 171–180 (2015)Google Scholar
  12. 12.
    Kulkarni, S., Singh, A., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of Wikipedia entities in web text. In: Proceedings of KDD 2009, pp. 457–466 (2009)Google Scholar
  13. 13.
    Medelyan, O., Witten, I.H., Milne, D.: Topic indexing with Wikipedia. In: Proceedings of the AAAI WikiAI Workshop, pp. 19–24 (2008)Google Scholar
  14. 14.
    Meij, E., Balog, K., Odijk, D.: Entity linking and retrieval for semantic search. In: Proceedings of WSDM 2014, pp. 683–684 (2014)Google Scholar
  15. 15.
    Mihalcea, R., Csomai, A.: Wikify!: Linking documents to encyclopedic knowledge. In: Proceedings of CIKM 2007, pp. 233–242 (2007)Google Scholar
  16. 16.
    Milne, D., Witten, I.H.: Learning to link with Wikipedia. In: Proceedings of CIKM 2008, pp. 509–518 (2008)Google Scholar
  17. 17.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: Proceedings of AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 25–30 (2008)Google Scholar
  18. 18.
    Usbeck, R., Röder, M., Ngonga Ngomo, A.-C., Baron, C., Both, A., Brümmer, M., Ceccarelli, D., Cornolti, M., Cherix, D., Eickmann, B., Ferragina, P., Lemke, C., Moro, A., Navigli, R., Piccinno, F., Rizzo, G., Sack, H., Speck, R., Troncy, R., Waitelonis, J., Wesemann, L.: GERBIL: General entity annotator benchmarking framework. In: Proceedings of WWW 2015, pp. 1133–1143 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Faegheh Hasibi
    • 1
  • Krisztian Balog
    • 2
  • Svein Erik Bratsberg
    • 1
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.University of StavangerStavangerNorway

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