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Report on the Sixth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR '13)
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0003-4042-4919
2014 (English)In: SIGIR Forum, ISSN 0163-5840, Vol. 48, no 1, 13-20 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. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use.

ESAIR'13 focuses on two of the most challenging aspects to address in the coming years. First, there is a need to include the currently emerging knowledge resources (such as DBpedia, Freebase) as underlying semantic model giving access to an unprecedented scope and detail of factual information. Second, there is a need to include annotations beyond the topical dimension (think of sentiment, reading level, prerequisite level, etc) that contain vital cues for matching the specific needs and profile of the searcher at hand.

There was a strong feeling that we made substantial progress. Specifically, the discussion contributed to our understanding of the way forward. First, emerging large scale knowledge bases form a crucial component for semantic search, providing a unified framework with zillions of entities and relations. Second, in addition to low level factual annotation, non-topical annotation of larger chunks of text can provide powerful cues on the expertise of the search and (un)suitability of information. Third, novel user interfaces are key to unleash powerful structured querying enabled by semantic annotation|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.

Place, publisher, year, edition, pages
2014. Vol. 48, no 1, 13-20 p.
National Category
Language Technology (Computational Linguistics)
URN: urn:nbn:se:kth:diva-149254DOI: 10.1145/2641383.2641387OAI: diva2:738470

QC 20150213

Available from: 2014-08-18 Created: 2014-08-18 Last updated: 2015-02-13Bibliographically approved

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