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Third workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR)
DISTRESS.
RISE, Swedish ICT, SICS. Userware.
DISTRESS.
Number of Authors: 3
2010 (English)Conference paper (Refereed)
Abstract [en]

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, and emerg- ing robust NLP tools. 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 un- derstand how these valuable semantic cues can be put to fruitful use. Unleashing the potential of semantic annotations requires us to think outside the box, by combining the insights of natural lan- guage processing (NLP) to go beyond bags of words, the insights of databases (DB) to use structure efficiently even when aggregating over millions of records, the insights of information retrieval (IR) in effective goal-directed search and evaluation, and the insights of knowledge management (KM) to get grips on the greater whole. The Workshop aims to bring together researchers from these dif- ferent disciplines and work together on one of the greatest chal- lenges in the years to come. The desired result of the workshop will be concrete insight into the potential of semantic annotations, and in concrete steps to take this research forward; synchronize related research happening in NLP, DB, IR, and KM, in ways that combine the strengths of each discipline; and have a lively, interactive work- shop were everyone contributes and that inspires attendees to think “outside the box.”

Place, publisher, year, edition, pages
2010, 12.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-23883OAI: oai:DiVA.org:ri-23883DiVA: diva2:1042961
Conference
19th ACM International Conference on Information and Knowledge Management (CIKM)
Projects
DISTRESSPROMISE
Available from: 2016-10-31 Created: 2016-10-31

Open Access in DiVA

fulltext(87 kB)3 downloads
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File name FULLTEXT01.pdfFile size 87 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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