Research in life sciences requires a sophisticated and integrated platform to query and analyze a large volume of data represented in various data. Two major challenges exist in this regard: integrating heterogeneous data sources and providing intuitive methods of accessing and querying data.In this regard, the semantic problem solving environment (SPSE) is created for parasite immunology research. SPSE utilizes semantic Web approaches to integrate and manage heterogeneous data in a knowledge-base called parasite experiment repository (PKR). This dissertation proposes two ontology-based question answering approaches to provide facilities to access and query annotated data. The first approach is an ontology-driven query answering system that guides users in exploring ontology schemas and generating queries in the form of RDF-triples -- Cuebee. Significant enhancements are introduced to the preliminary version of Cuebee to extend its key functionalities and make it more user-friendly and useful. A comprehensive study evaluates ontology-driven querying with conventional DBMS-based querying approaches for life sciences data. The second approach proposes a natural way of obtaining answers from annotated data by introducing a general framework for ontology-based natural language question answering -- OntoNLQA. A key characteristic of OntoNLQA is its capability in capturing semantics of the question and mapping it to the semantics of the annotated data model (ontology and RDF-triples) in order to create computational queries. This framework consists of five components each of which proposes different strategies and methods. This dissertation instantiates OntoNLQA in the context of parasite immunology and develops a system called AskCuebee. This system allows parasitologists to pose genomic, proteomic and pathway questions in natural language form related to the parasite T. cruzi. AskCuebee demonstrates the utility and usefulness of the OntoNLQA by implementing and evaluating its components.