Ontologies are at the heart of the semantic web. They define the concepts and relationships that make global interoperability possible. However, as these ontologies grow in size they become more and more difficult to create, use, understand, maintain, transform and classify. We present and evaluate several algorithms for extracting relevant segments out of large description logic ontologies for the purposes of increasing tractability for both humans and computers. The segments are not mere fragments, but stand alone as ontologies in their own right. This technique takes advantage of the detailed semantics captured within an OWL ontology to produce highly relevant segments. The research was evaluated using the GALEN ontology of medical terms and procedures.

In this paper, we propose a set of tasks that are relevant for the modular reuse of ontologies. In order to formalize these tasks as reasoning problems, we introduce the notions of conservative extension, safety and module for a very general class of logic-based ontology languages. We investigate the general properties of and relationships between these notions and study the relationships between the relevant reasoning problems we have previously identified. To study the computability of these problems, we consider, in particular, Description Logics (DLs), which provide the formal underpinning of the W3C Web Ontology Language (OWL), and show that all the problems we consider are undecidable or algorithmically unsolvable for the description logic underlying OWL DL. In order to achieve a practical solution, we identify conditions sufficient for an ontology to reuse a set of symbols “safely”—that is, without changing their meaning. We provide the notion of a safety class, which characterizes any sufficient condition for safety, and identify a family of safety classes–called locality—which enjoys a collection of desirable properties. We use the notion of a safety class to extract modules from ontologies, and we provide various modularization algorithms that are appropriate to the properties of the particular safety class in use. Finally, we show practical benefits of our safety checking and module extraction algorithms. 1.

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task. We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in my Grid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.

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High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can reduced to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas in potential areas.

This paper presents a formalized ontological framework for the analysis of multiscale classifications of geographic objects. We propose a set of logical principles that guide such geographic classifications. Then we demonstrate application of these principles on a practical example of the "National Hierarchical Framework of Ecological Units". The framework has a potential to be used to facilitate interoperability between such geographic classifications

We give a brief introduction of Hume’s epistemology and his penetrating analysis of causality. It is pointed out that there are some flaws in his epistemology and his theory of causation. Alternative theories of causation are then briefly introduced. Partly inspired by Hume’s analysis of necessary connexion, we present a new argument of causation. We argue that Hume’s removing necessary connexion from causality can help to provide a promising way to unify the law of causality and indeterminism. We then propose a generalized principle of causality, according to which there are two kinds of causes: concrete causes and universal causes, and correspondingly there are two kinds of effects: lawful events and random events. Each actual effect is composed of both lawful element and random element. A detailed analysis of the motion of objects is also given to support the new principle. 1.

Abstract. A Wide-Area Sensor Network (WASN) is a collection of heterogeneous sensor networks and data repositories spread over a wide geographic area. The diversity of sensor types and the regional differences over which WASNs operate result in semantic interoperability mismatches among sensor data, and a difficulty in agreeing on methods for sensor data access and exchange. We assume that sensors and their associated data have an explicit spatio-temporal basis (or tagging) in their representation. In this paper, we describe a spatio-temporal looselycoupled federated database model for the WASN data storage problem-that of unifying query and data representation given a heterogeneous WASN- and propose a conceptual schema to ease the problem of integration of sensor data representations. This is a continuing and critical challenge as sensor networks become more ubiquitous and data interoperation becomes increasing vital for a variety of applications (such as homeland security, transportation, environmental monitoring, etc.). We employ a top-down ontology-driven software development methodology. We use the SNAP/SPAN ontology as a sample framework for the conceptual schema. We compare our methodology of conceptual schema development with a bottom-up entity-oriented schema construction and discuss the differences in the two approaches. A unique contribution is the discussion of deployment experiences to evaluate proposed approaches in the context of a concrete WASN testbed. 1

We explore some recurring socio-technical problems encountered in the development of infrastructure for sharing and re-using data across sites and social scales for eHealth research. We link these problems to contradictions between underlying assumptions about data as a commodity whose reuse is not compromised when it is extracted from the context in which it has been captured, and the reality of data as entangled with, and constituted through, local practice. To illustrate these problems, we draw on the experiences of a number of HealthGrid projects developing infrastructures for data sharing and reuse, and trace the strategies that have evolved to address them. These experiences problematize the “one size fits all ” model initially adopted by HealthGrids, and highlight the need for design and development strategies that are able to engage with local needs and thereby ensure that the technical infrastructure is properly aligned with the human infrastructure it is supposed to support.