Search

GALEN seeks to provide re-usable terminology resources for clinical systems. The heart of GALEN is the Common Reference Model (CRM) formulated in a specialised description logic. The CRM is based on a set of principles that have evolved over the period of the project and illustrate key issues to be addressed by any large medical ontology. The principles on which the CRM is based are discussed followed by a more detailed look at the actual mechanisms employed. Finally the structure is compared with other biomedical ontologies in use or proposed. [ABSTRACT FROM AUTHOR]

The life sciences are a promising application area for semantic web technologies as there are large online structured and unstructured data repositories and ontologies, which structure this knowledge. We briefly give an overview over biomedical ontologies and show how they can help to locate, retrieve, and integrate biomedical data. Annotating literature with ontology terms is an important problem to support such ontology-based searches. We review the steps involved in this text mining task and introduce the ontology-based search engine GoPubMed. As the underlying data sources evolve, so do the ontologies. We give a brief overview over different approaches supporting the semi-automatic evolution of ontologies. [ABSTRACT FROM AUTHOR]

Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is conceptualized and formalized (e.g., by means of an ontology) in order to support diversified and automated knowledge processing (e.g., reasoning) performed by a machine. Moreover, through an optimal combination of (cognitive) human reasoning and (automated) machine reasoning and processing, it is possible for humans and machines to share complementary tasks. The spectrum of applications is extremely large and to name a few: corporate portals and knowledge manage-ment, e-commerce, e-work, e-business, healthcare, e-government, natural language understanding and automated translation, information search, data and services integration, social networks and collaborative filtering, knowledge mining, business intelligence and so on. From a social and economic perspective, this emerging technology should contribute to growth in economic wealth, but it must also show clear cut value for everyday activities through technological transparency and efficiency. The penetration of Semantic Web technology in industry and in services is progressing slowly but accelerating as new success stories are reported. In this paper and lecture we present ongoing work in the cross-fertilization between industry and academia. In particular, we present a collection of application fields and use cases from enterprises which are interested in the promises of Semantic Web technology. The use cases are detailed and focused on the key knowledge processing components that will unlock the deployment of the technology in the selected application field. The paper ends with the presentation of the current technology roadmap designed by a team of Academic and Industry researchers. [ABSTRACT FROM AUTHOR]

To master large rule sets in ontologies and other logic-based specifications, the ability to divide them into components plays an important role. While a naive approach treats the rule sets as black-box components and composes them via combinators, their relationships are usually so complicated that this approach fails to be useful in many scenarios. Instead, the components should be "opened" before composition. The paper presents several such "gray-box composition" techniques, namely fragment-based genericity and extension, inline template expansions, semantic macros, and mixin layers. All approaches help to structure large ontologies and rule-based specifications into fine-grained components, from which they can be built up flexibly. [ABSTRACT FROM AUTHOR]

In this paper we address computational aspects of protein structure and function, including prediction of secondary structure, folding, structure determination from Nuclear Magnetic Resonance data, modelling of protein interactions, and metabolic pathways. The subject is introduced with an overview of protein structure and chemistry and the algorithms and representations used to model protein structures. The main focus of the paper is the integration of information from sources relevant to protein structure modelling, such as structure databases and modelling servers, a task made difficult by the heterogeneity of formats, the diversity of data sources, and the sheer volume of information available, making evident the need for a standard framework for data sharing, i.e. the Semantic Web. To help solve this problem, we present tools being developed according to the concept of a Semantic Web. These include the UniProtRDF project and tools currently implemented on the Chemera molecular modelling software which can facilitate the search and application of information available from Internet servers and databases. [ABSTRACT FROM AUTHOR]

This article is firstly an introduction into query languages for the Semantic Web, secondly an in-depth comparison of the languages introduced. Only RDF query languages are considered because, as of the writing of this paper, query languages for other Semantic Web data modeling formalisms, especially OWL, are still an open research issue, and only a very small number of, furthermore incomplete, proposals for querying Semantic Web data modeled after other formalisms than RDF exist. The limitation to a few RDF query languages is motivated both by the objective of an in-depth comparison of the languages addressed and by space limitations. During the three years before the writing of this article, more than three dozen proposals for RDF query languages have been published! Not only such a large number, but also the often immature nature of the proposals makes the focus on few, but representative languages a necessary condition for a non-trivial comparison. For this article, the following RDF query languages have been, admittedly subjectively, selected: Firstly, the "relational" or "pattern-based" query languages SPARQL, RQL, TRIPLE, and Xcerpt; secondly the reactive rule query language Algae; thirdly and last the "navigational access" query language Versa. Although subjective, this choice is arguably a good coverage of the diverse language paradigms considered for querying RDF data. It is the authors' hope and expectation, that this comparison will motivate and trigger further similar studies, thus completing the present article and overcoming its limitation. [ABSTRACT FROM AUTHOR]

The semantic web community and the business rules community have common roots. This article explores the differences and similarities between the two fields in order to encourage collaboration between the communities with respect to standardization efforts and research topics. Keywords: Business rules, Semantic Web, SBVR, Rule standards, Reasoning languages, Specification languages. [ABSTRACT FROM AUTHOR]

We present some recent results on the definition of logic-based systems integrating ontologies and rules. In particular, we take into account ontologies expressed in Description Logics and rules expressed in Datalog (and its nonmonotonic extensions). We first introduce the main issues that arise in the integration of ontologies and rules. In particular, we focus on the following aspects: (i) from the semantic viewpoint, ontologies are based on open-world semantics, while rules are typically interpreted under closed-world semantics. This semantic discrepancy constitutes an important obstacle for the definition of a meaningful combination of ontologies and rules; (ii) from the reasoning viewpoint, the interaction between an ontology and a rule component is very hard to handle, and does not preserve decidability and computational properties: e.g., starting from an ontology in which reasoning is decidable and a rule base in which reasoning is decidable, reasoning in the formal system obtained by integrating the two components may not be a decidable problem. Then, we briefly survey the main approaches for the integration of ontologies and rules, with special emphasis on how they deal with the above mentioned issues, and present in detail one of such approaches, i.e., ${{\cal DL}\textit{+log}}$. Finally, we illustrate the main open problems in this research area, pointing out what still prevents us from the development of both effective and expressive systems able to integrate ontologies and rules. [ABSTRACT FROM AUTHOR]

For realizing the Semantic Web vision, extensive work is underway for getting the layers of its conceived architecture ready. Given that the Ontology Layer has reached a certain level of maturity with W3C recommendations such as RDF and the OWL Web Ontology Language, current interest focuses on the Rules Layer and its integration with the Ontology Layer. Several proposals have been made for solving this problem, which does not have a straightforward solution due to various obstacles. One of them is the fact that evaluation principles like the closed-world assumption, which is common in rule languages, are usually not adopted in ontologies. Furthermore, naively adding rules to ontologies raises undecidability issues. In this paper, after giving a brief overview about the current state of the Semantic-Web stack and its components, we will discuss nonmonotonic logic programs under the answer-set semantics as a possible formalism of choice for realizing the Rules Layer. We will briefly discuss open issues in combining rules and ontologies, and survey some existing proposals to facilitate reasoning with rules and ontologies. We will then focus on description-logic programs (or dl-programs, for short), which realize a transparent integration of rules and ontologies supported by existing reasoning engines, based on the answer-set semantics. We will further discuss a generalization of dl-programs, viz.hex-programs, which offer access to different ontologies as well as higher-order language constructs. [ABSTRACT FROM AUTHOR]

Consider the following two conceptions of the Semantic Web: - A web of (logic based) knowledge representations. - A web of (semi-)structured data. In both conceptions, the common factor (the web) imposes certain requirements: extremely variable scalability (from a home page to community sites to sites that encompass a significant fraction of the web), rapid evolution, radical distribution, arbitrary interconnection and aggregation, and very little validation or other means of control. The demands of the web are forcing both the knowledge representation (KR) and the database communities to stretch their understanding and technology in different ways. [ABSTRACT FROM AUTHOR]