We present a semantically-driven approach to uncertainties within and across ontologies. Ontologies are widely used not only by the Semantic Web but also by artificial systems in general. They represent and structure a domain with respect to its semantics. Uncertainties, however, have been rarely taken into account in ontological representation, even though they are inevitable when applying ontologies in `real world' applications. In this paper, we analyze why uncertainties are necessary for ontologies, how and where uncertainties have to be represented in ontologies, and what their semantics are. In particular, we investigate which ontology constructions need to address uncertainty issues and which ontology constructions should not be affected by uncertainties on the basis of their semantics. As a result, the use of uncertainties is restricted to appropriate cases, which reduces complexity and guides ontology development. We give examples and motivation from the field of spatially-aware systems in indoor environments.
Ontology languages for the Semantic Web have their strengths and weaknesses, in particular in the light of deploying them for biological and medical information systems. We survey and compare the Description Logics-based OWL languages, and the DL-Lite and DLR families of languages. Language choices that an ontology developer has to make are, among others, expressivity with nary relations (where n 2) and more role properties versus ontology usage for data-intensive tasks. Guidelines are suggested to facilitate choosing the language best fitted for a task.
A semantics of definition category and its sub-categories used in texts, organized in a semantic map, requires a more complex structuring of the domain underlying the meaning representations than is commonly assumed. This paper proposes a three-layer ontology in which the notion of definition takes part and indicates how it can be used in Information Retrieval. The first part describes an automatic process to annotate definitions based on linguistic knowledge, in accordance with a general linguistic ontology, and the second part shows a practical use of its semantic and discourse organizations in retrieving information through the Web.
Editor's Note: An update to this article has been posted here on 7/14/04. As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. And they are doing so without relying on the jumble of rule-based techniques common in earlier knowledge representation efforts. These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information.