Most of information retrieval (IR) approaches relies on the hypothesis that keywords extracted from a document are sufficient to evaluate the relevance of that document with respect to the query. Such an approach may insufficiently lay bare the semantic contents of the documents. In addition to keywords, automatic indexing methods need external knowledge such as thesauri and ontologies for improving the representation of documents or for expanding queries to related keywords. Moreover, ontologies may be combined with a view of for estimating the relevance of documents, the “proximity” between words, or for expressing flexible queries. In this chapter, we survey several recent approaches. Then, two types of methods are discussed in detail. The first one uses a symbolic pattern matching approach, which is based on possibilistic ontologies (where qualitative necessity and possibility degrees estimate to what extent two terms refer to the same thing). The second type of approaches projects fuzzy set representations of queries and documents on a classical ontology, and compare these projections for rank ordering the documents according to a retrieval status value.