Semantic Search

In recent years, we have witnessed tremendous interest and substantial economic exploitation of search technologies, both at web and enterprise scale. However, the representation of user queries and resource content in existing search appliances is still almost exclusively achieved by simple syntax-based descriptions of the resource content and the information need such as in the predominant keyword-centric paradigm. While systems working on the basis of these rough approximations have shown to work well for topical search, they usually fail to address more complex information needs. Semantic technologies, namely expressive ontology and resource description languages, scalable repositories, reasoning engines and information extraction techniques are now in a mature state such that they can be applied to enable a higher level of semantic underpinning in real-world Information Retrieval (IR) systems. This application of semantic technologies to IR tasks is usually referred to as Semantic Search and the field can be roughly organized along three main topic clusters. Firstly, more expressive descriptions of resources can be achieved through the conceptual representation of the actual resource content and the collaborative annotation of general resource metadata using standard Semantic Web languages. As a result, there is high potential that complex information needs can be supported by the application of Semantic Web technologies to IR, where expressive queries can be matched against expressive resource descriptions. Secondly, in the past year we have also seen the emergence of important results in adapting ideas from IR to …