skip to main content
column

Report on the Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR'14)

Published:23 June 2015Publication History
Skip Abstract Section

Abstract

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. The goal of the ESAIR'14 workshop remained to advance the general research agenda on this core problem, with an explicit focus on one of the most challenging aspects to address in the coming years. The main remaining challenge is on the user's side---the potential of rich document annotations can only be realized if matched by more articulate queries exploiting these powerful retrieval cues---and a more dynamic approach is emerging by exploiting new forms of query autosuggest. How can the query suggestion paradigm be used to encourage searcher to articulate longer queries, with concepts and relations linking their statement of request to existing semantic models? How do entity results and social network data in "graph search" change the classic division between searchers and information and lead to extreme personalization---are you the query? How to leverage transaction logs and recommendation, and how adaptive should we make the system? What are the privacy ramifications and the UX aspects---how to not creep out users.

There was a strong feeling that we made substantial progress. Specifically, the discussion contributed to our understanding of the way forward. First, for notable (head, shoulder, but not tail) entities in semantic search we have reached the level of quality at minimal costs allowing for deployment in major web search engines---the dream has become a reality. Second, entity detection is moving fast into domain specific, personal, and business domains, and has become a vital component for a range of applications. Third, semantic web has exchanged logic for machine learning approaches, and machine learning is the natural unification of semantic web and information retrieval approaches.

References

  1. S. Cotelo, A. Makowski, L. Chiruzzo, and D. Wonsever. Documents search using semantics criteria. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 5--7, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666187. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. P. Cucerzan. Linking to web knowledge bases and applications to web search. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 3--3, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666199. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. De Nies, C. Beecks, W. De Neve, T. Seidl, E. Mannens, and R. Van de Walle. Towards named-entity-based similarity measures: Challenges and opportunities. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 9--11, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. V. Deolalikar. Can corpus similarity-based self-annotation assist information retrieval? In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 13--15, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666191. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Ibrahim, M. Amir Yosef, and G. Weikum. Aida-social: Entity linking on the social stream. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 17--19, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666185. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. E.-E. Jan, K.-Y. Chen, and T. Ide. A probabilistic concept annotation for it service desk tickets. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 21--23, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Z. Jiang, M. Chen, and X. Liu. Semantic annotation with rescoredesa: Rescoring concept features generated from explicit semantic analysis. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 25--27, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Z. Li, P. Exner, and P. Nugues. Using semantic role labeling to predict answer types. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 29--31, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Mao and K. Lu. Leverage the associations between documents, subject headings and terms to enhance retrieval. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 33--35, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. P. Mika. Semantic search at yahoo! In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 1--1, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Verma and D. Ceccarelli. Bringing head closer to the tail with entity linking. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 37--39, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Yang. A fragment-based similarity measure for concept hierarchies and ontologies. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 41--42, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666188. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. G. Zuccon, B. Koopman, and P. Bruza. Exploiting inference from semantic annotations for information retrieval: Reflections from medical ir. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, pages 43--45, New York, NY, USA, 2014. ACM. URL http://doi.acm.org/10.1145/2663712.2666197. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Report on the Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR'14)

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM SIGIR Forum
      ACM SIGIR Forum  Volume 49, Issue 1
      June 2015
      69 pages
      ISSN:0163-5840
      DOI:10.1145/2795403
      Issue’s Table of Contents

      Copyright © 2015 Authors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 23 June 2015

      Check for updates

      Qualifiers

      • column

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader