Exploiting Semantic Annotations in Information Retrieval (Workshop series 2010-2014)

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. 

The ESAIR series of workshops takes as its starting point that 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. To complicate matters, standard text search excels at shallow information needs expressed by short keyword queries, and here semantic annotation contributes very little, if anything.

ESAIR’10 focussed on formulating a framework for viewing annotation as a linking procedure, connecting an analysis of information objects with a semantic model of some sort, expressing relations that contribute to a task of interest to end users.

ESAIR’11 brought together discussions on how to unleash the potential of semantic annotations requires us to think outside the box, by combining the insights of natural language processing (NLP) to go beyond bags of words, the insights of database technologies (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.

ESAIR’12 focussed on how to leverage the rich context currently available, especially in a mobile search scenario, giving powerful new handles to exploit semantic annotations and on how to fruitfully combine classic information retrieval and knowledge intensive approaches, and for the first time work actively toward a unified view on exploiting semantic annotations.

ESAIR’13 focussed 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.

ESAIR’14 focussed on how to elicit more articulate queries or expressions of information need, with concepts and relations linking their statement of request to existing semantic models as offered by emerging knowledge bases. The discussion centered to a large extent on how to provide useful event and entity identification from unstructured streaming information.