Ontology-driven question answering and ontology quality evaluation
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As more data is being semantically annotated, it is getting more common that researchers in multiple disciplines to rely on semantic repositories that contain large amounts of data in the form of ontologies as a compact source of information. One of the main issues currently facing these researchers is the lack of easy-to-use interfaces for data retrieval, due to the need to use special query languages or applications. In addition, the knowledge in these repositories might not be comprehensive or up-to-date due to several reasons, such as the discovery of new knowledge in the field after the repositories was created. In this dissertation, we present our SemanticQA system that allows users to query semantic data repositories using natural language questions. If a user question cannot be answered solely from the ontology, SemanticQA detects the failing parts and attempts to answer these parts from web documents and plugs in the answers to answer the whole questions, which might involve a repetition of the same process if other parts fail. At the same time, with the large number of ontologies being added constantly, it is difficult for users to find ontologies that are suitable to their work. Therefore, tools for evaluating and ranking the ontologies are needed. For this purpose, we present OntoQA, a tool that evaluates ontologies related to a certain set of terms and then ranks them according a set of metrics that captures different aspects of ontologies. Since there are no global criteria defining how a good ontology should be, OntoQA allows users to tune the ranking towards certain features of ontologies to suit the need of their applications. OntoQA is not only useful for users trying to find suitable ontologies, but for ontology developers who are looking for measures to evaluate their product.