178 research outputs found
Racer: A Core Inference Engine for the Semantic Web
In this paper we describe Racer, which can be considered as a core inference engine for the semantic web. The Racer inference server o#ers two APIs that are already used by at least three di#erent network clients, i.e., the ontology editor OilEd, the visualization tool RICE, and the ontology development environment Protege 2. The Racer server supports the standard DIG protocol via HTTP and a TCP based protocol with extensive query facilities. Racer currently supports the web ontology languages DAML+OIL, RDF, and OWL
A Parallel Computing Architecture for High-Performance OWL Reasoning
The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores
Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications
Requirement volatility is an issue in software engineering in general, and in
Web-based clinical applications in particular, which often originates from an
incomplete knowledge of the domain of interest. With advances in the health
science, many features and functionalities need to be added to, or removed
from, existing software applications in the biomedical domain. At the same
time, the increasing complexity of biomedical systems makes them more difficult
to understand, and consequently it is more difficult to define their
requirements, which contributes considerably to their volatility. In this
paper, we present a novel agent-based approach for analyzing and managing
volatile and dynamic requirements in an ontology-driven laboratory information
management system (LIMS) designed for Web-based case reporting in medical
mycology. The proposed framework is empowered with ontologies and formalized
using category theory to provide a deep and common understanding of the
functional and nonfunctional requirement hierarchies and their interrelations,
and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure
An OWL 2-Based Knowledge Platform Combining the Social and Semantic Webs for an Ambient Childhood Obesity Prevention System
Amid the extremely active Semantic Web community and the Social Web's exceptionally rising popularity, experts believe that an amplified fusion between the two webs will give rise to the next huge advancement in Web intelligence. Such advances can particularly be translated into ambient and ubiquitous systems and applications. In this paper, we delve into the recent advances in knowledge representation, semantic web, natural language processing and online social networking data and concepts, to propose an inclusive platform and framework defining ambient recommender and decision support systems that aim at facilitating cross-sectional analysis of the domain of childhood obesity and generating both generic and customized preventive recommendations
Obol: Integrating Language and Meaning in Bio-Ontologies
Ontologies are intended to capture and formalize a domain of knowledge. The
ontologies comprising the Open Biological Ontologies (OBO) project, which includes
the Gene Ontology (GO), are formalizations of various domains of biological
knowledge. Ontologies within OBO typically lack computable definitions that serve to
differentiate a term from other similar terms. The computer is unable to determine the
meaning of a term, which presents problems for tools such as automated reasoners.
Reasoners can be of enormous benefit in managing a complex ontology. OBO term
names frequently implicitly encode the kind of definitions that can be used by
computational tools, such as automated reasoners. The definitions encoded in the
names are not easily amenable to computation, because the names are ostensibly
natural language phrases designed for human users. These names are highly regular
in their grammar, and can thus be treated as valid sentences in some formal or
computable language.With a description of the rules underlying this formal language,
term names can be parsed to derive computable definitions, which can then be
reasoned over. This paper describes the effort to elucidate that language, called Obol,
and the attempts to reason over the resulting definitions. The current implementation
finds unique non-trivial definitions for around half of the terms in the GO, and
has been used to find 223 missing relationships, which have since been added to
the ontology. Obol has utility as an ontology maintenance tool, and as a means of
generating computable definitions for a whole ontology
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