Biomedical ontologies are increasingly being used to improve information retrieval methods. In this paper, we present a novel information retrieval approach that exploits knowledge specified by the Semantic Web ontology and rule languages OWL and SWRL. We evaluate our approach using an autism ontology that has 156 SWRL rules defining 145 autism phenotypes. Our approach uses a vector space model to correlate how well these phenotypes relate to the publications used to define them. We compare a vector space phenotype representation using class hierarchies with one that extends this method to incorporate additional semantics encoded in SWRL rules. From a PubMed-extracted corpus of 75 articles, we show that average rank of a related paper using the class hierarchy method is 4.6 whereas the average rank using the extended rule-based method is 3.3. Our results indicate that incorporating rule-based definitions in information retrieval methods can improve search for relevant publications.