Using the Semantic Web to support knowledge integration, retrieval and expansion for ecoinformatics

Today, the information on the World Wide Web is growing at an astonishing rate providing a rapidly expanding source of valuable data. The abundance of distributed information on the Web increases the importance of efficient organization, sharing and retrieval of available data. This is particularly important in scientific research where efficient collaboration, exchange of results of experiments and observations, fast discovery of relevant information and data integration from different sources are the key elements for success. This thesis investigates how the Semantic Web can be used to facilitate: - combining information from multiple heterogeneous resources into well structured machine-understandable OWL documents that can be used by humans as well as automated intelligent agents - defining OWL ontologies which describe the concepts in Semantic Web representation of data and relationships among those concepts - answering complicated queries about the data - producing new knowledge as a result of applying ontologies and reasoning to the original data - efficient discovery of relevant information and obtaining data in well structured flexible format. We develop a set of ontologies, tools and applications within the Spire (Semantic Prototypes In Research Ecoinformatics) project (http://spire.umbc.edu) that help us to demonstrate how the aforementioned goals can be achieved. In particular, we develop a number of ontologies (SpireEcoConcepts, EthanKeywords, EthanAnimals, EthanPlants) which help us to describe biological data obtained from multiple sources (as well as relationships among different parts of that data) in a machine-understandable way and apply OWL reasoners to that data to find answers for various questions which are of great interest for environmentalists and ecologists. As an effort to exploit popular on-line resources and knowledge generated by hundreds of thousands of people, we designed the Splickr and SPOTter applications to produce Semantic Web content and show how Semantic Web technologies can be used to generate new knowledge from existing resources. The goal of our research is to investigate the advantages and successful use cases of the Semantic Web as well as identify improvements that can be made to boost the expressivity of Semantic Web languages and usefulness of Semantic Web technologies