Kenneth J. Laskey

PR-OWL: a Bayesian ontology language for the semantic web

This paper addresses a major weakness of current technologies for the Semantic Web, namely the lack of a principled means to represent and reason about uncertainty. This not only hinders the realization of the original vision for the Semantic Web, but also creates a barrier to the development of new, powerful features for general knowledge applications that require proper treatment of uncertain phenomena. We present PR-OWL, a probabilistic extension to the OWL web ontology language that allows legacy ontologies to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond the current limitations of deterministic classical logic to a full first-order probabilistic logic. By providing a principled means of modeling uncertainty in ontologies, PR-OWL can be seen as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the W3Cs vision for the Semantic Web. In order to fully present the concepts behind PR-OWL, we also cover Multi-Entity Bayesian Networks (MEBN), the Bayesian first-order logic supporting the language, and UnBBayes-MEBN, an open source GUI and reasoner that implements PR-OWL concepts. Finally, a use case of PR-OWL probabilistic ontologies is illustrated here in order to provide a grasp of the potential of the framework.

Probabilistic ontology for net-centric fusion

In a net-centric world, systems will be required to fuse data from geographically dispersed, heterogeneous information sources operating asynchronously, to produce up-to-date, mission-relevant knowledge to inform commanders. Realizing this vision requires overcoming a number of technical challenges. Among these is the need for semantic interoperability among systems with different internal data models and vocabularies. Ontologies are seen as a key enabling technology for semantic interoperability. Although information fusion by nature involves reasoning under uncertainty, traditional ontology formalisms provide no principled means of reasoning under uncertainty. This paper proposes the use of probabilistic ontologies within a service-oriented architecture as a means to enable semantic interoperability in net-centric fusion systems.

Considerations for SOA Versioning

Service oriented architecture is a paradigm for bringing together needs and capabilities, where SOA services provide an effective means of connecting consumers and the means to realize desired real world effects. The resources accessed as part of SOA interactions are independently owned and evolved but must be unambiguously identifiable. In cases where the resources are changing, the consumer must be able to evaluate how those changes affect appropriateness for use, whether those are changes to the underlying capabilities, the service access, or the service description. This paper presents early discussions on versioning in the context of a SOA reference architecture.

Metadata Concepts to Support a Net-Centric Data Environment

The term metadata is often defined as “data about data” but that circular reference does little to describe what constitutes metadata and how it is used. Here, we will focus on metadata as conceived to support the concepts of a service-oriented architecture and, in particular, as it relates to the DOD Net-Centric Data Strategy and the NCES core services; more specifically, what types and structure of metadata are implied by current use cases, what functions are implied to support creating. maintaining, and using such metadata, and what is implied about a metadata infrastructure that would support such metadata and its related functions.

Uncertainty Reasoning for the Semantic Web II: International Workshops URSW 2008-2010 Held at ISWC and UniDL 2010 Held at Floc, Revised Selected

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.