Abstract. This paper reports our experience with OWL for the Foundational Model of Anatomy (FMA). We show that converting the FMA from Protégé into OWL DL was possible, with most features of the original FMA captured. The conversion relies on translation and enrichment rules, implemented with flexible options. Unsurprisingly, reasoning with OWL proved to be a real challenge, due to the sheer size and complexity of the FMA. As the entire FMA in OWL DL raised inference problems hard to solve in terms of time and memory, an incremental approach was adopted. A number of various smaller versions that Racer could handle were successfully tested. Some inconsistencies were identified and some classes reclassified. The analysis of the results obtained so far shows the benefits of representing the FMA in OWL and, more generally, the usefulness of DLs reasoning techniques for large-scale biomedical ontologies shared on the Web. 1

When dealing with large, distributed systems that use state-of-the-art components, individual components are usually developed in parallel. As development continues, the decoupling invariably leads to a mismatch between how these components internally represent concepts and how they communicate these representations to other components: representations can get out of synch, contain localized errors, or become manageable only by a small group of experts for each module. In this paper, we describe the use of an ontology as part of a complex distributed virtual human architecture in order to enable better communication between modules while improving the overall flexibility needed to change or extend the system. We focus on the natural language understanding capabilities of this architecture and the relationship between language and concepts within the entire system in general and the ontology in particular. 1.

Executive summary The SPICE platform aims to research and prototype a scalable overlay architecture for the rapid creation and deployment of intelligent and personalized services. In order to support this requirement, the platform must be composed of technology enablers that allow the services to be malleable with regard to their personalisation and customisation. In support of this requirement WP2 augments its main objective, “to develop a component-based middleware layer to ensure the inter-working of distributed service components ” [2], with a set of advanced enablers within its component-based middleware layer to support the property of intelligence. Systems, such as the SPICE platform, can be reasonably referred to as enabling intelligence when the services executing upon it act rationally. A system is considered rational if it does the “right thing, ” given information that it knows [1]. Semantic metadata, conforming to well-defined semantic vocabularies and ontological languages that describe service components, is a key technology building block to enable rationality. This is particularly true due to the fact that most ontology languages have been

Software development and evolution are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant to these resources is typically dispersed over a wide range of artifacts, representation formats, and abstraction levels. In order to stay competitive, organizations are often required to assess and provide evidence that their software meets expected quality requirements. Similarly, the quality assessment of open source or third-party components is a crucial factor in software development. In our research, we focus specifically on modeling and assessing evolvability as a quality factor. We introduce our OntEQAM methodology that supports the integration and semantic analysis of knowledge resources typically found in software ecosystems. We further illustrate how our OntEQAM methodology can be applied to support the automated analysis and assessment of different types of quality attributes related to the evolvability of software ecosystems. 1.

Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated ‘information silos’. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates user, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.

The purpose of this paper is to discuss the major influences on the design of ODM. We first discuss the need for an ontology definition metamodel, provide an overview of the ODM architecture and present basic considerations that were taken in developing ODM. We then discuss in detail several “watershed issues ” that were debated by the co-submission team and whose resolutions guided the design and evolution of ODM.

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