Report on the Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR'14)

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 remained 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. There was a strong feeling that we made substantial progress. Specifically, the discussion contributed to our understanding of the way forward. First, for notable (head, shoulder, but not tail) entities in semantic search we have reached the level of quality at minimal costs allowing for deployment in major web search engines---the dream has become a reality. Second, entity detection is moving fast into domain specific, personal, and business domains, and has become a vital component for a range of applications. Third, semantic web has exchanged logic for machine learning approaches, and machine learning is the natural unification of semantic web and information retrieval approaches.