About the Reasoning Web 2006 Proceedings

To master large rule sets in ontologies and other logic-based specifications, the ability to divide them into components plays an im- portant 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 compo- sition. The paper presents several such ”gray-box composition” tech- niques, 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.

For realizing the Semantic Web vision, extensive work is underway for getting the layers of its conceived architecture ready. Given that the Ontol- ogy 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 prin- ciples like the closed-world assumption, which is common in rule languages, are usually not adopted in ontologies. Furthermore, naively adding rules to on- tologies 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 pos- sible formalism of choice for realizing the Rules Layer. We will briefly discuss open issues in combining rules and ontologies, and survey some existing pro- posals to facilitate reasoning with rules and ontologies. We will then focus on description-logic programs (or dl-programs, for short), which realize a transpar- ent 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.

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 ex- pressed in Datalog (and its nonmonotonic extensions). We first intro- duce 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 dis- crepancy 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 computa- tional 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., DL+log. Finally, we illustrate the main open prob- lems 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.

The semantic web community and the business rules community have common roots. This article explores the differences and similarities be- tween the two fields in order to encourage collaboration between the communi- ties with respect to standardization efforts and research topics.

The life sciences are a promising application area for seman- tic web technologies as there are large online structured and unstruc- tured 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. Anno- tating 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.

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.

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.