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Seventh workshop on exploiting semantic annotations in information retrieval (ESAIR’14)
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0003-4042-4919
2014 (English)In: CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, 2094-2095 p.Article in journal (Refereed) Published
Abstract [en]

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 remains 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?

Place, publisher, year, edition, pages
2014. 2094-2095 p.
Keyword [en]
Knowledge management, Semantics, Social networking (online), Document annotation, Dynamic approaches, Graph search, Information access, Personalizations, Query suggest, Query suggestion, Semantic annotations, Information retrieval
National Category
Computer Science Information Systems
URN: urn:nbn:se:kth:diva-174749DOI: 10.1145/2661829.2663539ScopusID: 2-s2.0-84937597776OAI: diva2:859871

QC 20151009

Available from: 2015-10-09 Created: 2015-10-07 Last updated: 2015-10-09Bibliographically approved

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Karlgren, Jussi
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