Originating from the binary assignment of controlled vocabulary terms to documents, all initial IR systems adopted the Boolean model of searching. Here, a user’s search terms are linked by the Boolean logical operators OR, AND, and NOT; OR is used to link synonyms or alternatives, AND to link conjunctively, and NOT to indicate irrelevant terms, i.e., those terms that should not be assigned to the required documents. Such systems typically return an unordered set of results, although in 1958 Joyce and Needham  already proposed the use of a notion of term frequency to sort the list of matching documents. They also suggested the use of aggregated terms (where the set of documents containing the phrase information retrieval is different from the union of the set of documents containing information and retrieval). The imprecise nature of language (as well as “relevance”) have led to a number of developments moving away from the inherently restrictive Boolean model and towards a coordinate-level, ranked output.
A first step was the move towards thesauri that were automatically generated from the documents’ content [95, 291]. Luhn  first addressed automatic keyword indexing, in which the terms in the documents were directly searchable. Maron and Kuhns  were the first to take a probabilistic view on IR, centered on the notion of relevance. This introduced a principled notion of term weighting (although Maron and Kuhns  assumed that human indexers would assign the initial weights). Via advances in automatic speech recognition and the probability ranking principle , term weighting obtained a principal role in retrieval models. Current state-of-the-art retrieval approaches employ models of language to compare queries with documents. In this thesis, we will make extensive use of a process called query modeling, where the query is represented as a language model and various methods and techniques can be used to improve this model. We will show that incorporating evidence captured in concepts and concept languages can be applied to significantly improve retrieval performance over state-of-the-art retrieval methods.