In recent years we have witnessed substantial exploitation of search technologies, both at web and enterprise scale.
However, the representation of user queries and information in existing search appliances is still almost exclusively achieved by simple syntax-based descriptions (i.e. keyword queries matched against bag-of-words document representation).
While these systems have shown to work well for many common search needs, they work on the basis of rough approximations and usually fail to address more complex tasks such as aggregation and information analytics.
On the other hand, recent advances in the field of semantic technologies have resulted in tools and standards that allow for the articulation of domain knowledge at a high level of expressivity. Semantic repositories and reasoning engines have now advanced to a state where querying and processing of this knowledge can scale to large-scale scenarios.
As such, semantic technologies are posed to provide significant contributions to IR problems.
More expressive descriptions of resources are achieved through the representation of the resource content in terms of concepts and structured data (OWL, RDF).
The recent media interest around Wolfram Alpha, PowerSet (acquired by Microsoft Bing) and Yahoo! SearchMonkey show the expectations regarding the impact of semantic search
The other way around, we have also seen the successful adoption of ideas from IR to the problem of search in semantic (Web) data, which is due to the increasing size of the Semantic Web.
Popular examples include the Linking Open Data project, the large body of data in forms of Microformats and RDFa data associated with text.
Common to these scenarios is that the search is focused not on a document collection, but on semantic data (which may be possibly linked to or embedded in textual information).
Search and ranking large amount of semantic data on the Web is another key topic addressed by this workshop.
In this context, challenges for Semantic Search research will include, among others: