ESAIR'14: Seventh International Workshop on

Exploiting Semantic Annotations in Information Retrieval

Workshop Homepage Call for Papers Program Organizers


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.

Articulate Queries and Query Auto Suggest

The goal of the ESAIR'14 was to advance the general research agenda on this core problem, with an explicit focus on two of the most challenging aspects to address in the coming years.

We Need Help!

The Workshop brought together researchers working with semantic annotations, its use cases, its sources (authoring to NLP tools), its users, and its use in DB, IR, KM, or Web research, and work together on one of the greatest challenges in the years to come. We hed a lively and interactive workshop, with the explicit aim to push the boundaries and think outside the box.

ESAIR'14 in action

ESAIR history

Previous ESAIR editions are found at:


July 30, 2014Deadline for Paper Submissions
August 15, 2014Extended Deadline for Paper Submissions
 Prepare your 2+1 page PDF using the ACM format
Submit online using EasyChair
August 30, 2014Notification of Acceptance
 Details of accepted papers published online
September 10, 2014Deadline for Camera Ready Copies
November 7, 2014Workshop day during CIKM 2014!


This workshop will be held as part of the 23rd ACM International Conference on Information and Knowledge Management, Shanghai, 2014. Information on Shanghai can be found in the Wikipedia.