Previous IR approaches have typically used either full-text indexing or indexing using concepts and few methods exist where the two are combined in a principled manner. We hypothesize that the knowledge captured in concept languages and the associations between concepts and texts (for example, in the form of document-level annotations) can be successfully used to inform IR algorithms. Such algorithms would be able to match queries and documents not only on a textual level, but also on a semantic level. Recent advances in the language modeling for IR framework have enabled the use of rich query representations in the form of query language models. This, in turn, enables the use of the language associated with concepts to be included in the retrieval model in a principled and transparent manner.
Note that we do not pursue a research direction that uses concepts in a language modeling framework. Instead, we investigate how we can employ the actual use of concepts as measured by the language that people use when they discuss them.
Recent developments in the semantic web community, such as DBpedia and the inception of the Linked Open Data cloud, have enabled the association of texts with concepts on a large scale. These developments enable us to move beyond manually assigned concepts in domain-specific contexts and into the general domain. In sum, we will show in the remaining chapters of the thesis how we can successfully apply language modeling techniques in tandem with concepts to improve information access performance.