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We discuss the interplay between applied ontology and the use of web resources in scientific and other domains, and provide an account of how ontologies are implemented computationally. We provide an introduction to the Protégé Ontology Editor, the Semantic Web, the Resource Description Framework (RDF) and the Web Ontology Language (OWL). We illustrated how BFO is used to provide the common architecture for specific domain ontologies, including the Ontology for General Medical Science (OGMS), the Infectious Disease Ontology (IDO), the Information Artifact Ontology (IAO), and the Emotion Ontology (MFO-EM). Before terms and relations provide the starting point for the creation of definition trees in such ontologies according to the Aristotelian strategy for authoring of definitions outlined in Chapter 4. We conclude with a discussion of the role of a top-level ontology such as BFO in facilitating semantic interoperability.

Philosophical ontology (also ‘metaphysics’) explores general questions concerning the nature of being; applied ontology concerns more specific questions pertaining to entities in specific domains. Applied ontology takes over from philosophy the central role of taxonomies and each applied ontology is built around a hierarchy of types and subtypes (an is_a hierarchy). Applied ontologies are organized along the dimensions of 1. greater and lesser generality, and 2. intended use. Concerning 1. we distinguish between top-level and domain ontologies. Top-level ontologies are domain-neutral; including terms such as ‘object’ or ‘process’ having maximally general scope. Domain ontologies are domain specific (comprising terms such as ‘molecule’ or ‘catheter’). Concerning 2. we distinguish between reference and application ontologies. Reference ontologies are designed to be re-used in distinct application ontologies, themselves built to address specific needs. We address the role of these different ontologies in assisting with heterogeneous data-management and promoting interoperability among information systems.

The chapter explains the concept of a Public Sector Information Exchange (PSIE). It then presents a detailed case study of a supply-chain PSIE model: the Australian CrimTrac program that was established to streamline information sharing among Australian police organizations. The chapter then explores the potential for an exchange PSIE model to be implemented in a US public sector agency in the environmental domain. Economic, legal and technical challenges to an exchange-model PSIE are outlined in detail. A case study of the deNovis project shows how semantic interoperability challenges could be overcome.