Ignazio Palmisano

Enriching the gene ontology via the dissection of labels using the ontology pre-processor language

In this paper we describe the process of taking an axiomatically lean ontology and enriching it through the automatic application of axioms using ontology design patterns (ODP). Our exemplar is the Gene Ontologys Molecular Function Ontology; this describes an important part of biology and is widely used to describe data. Yet much of the knowledge within the GOs MF is captured within the terms that label the concepts and within the natural language definitions for those concepts. Whilst both of these are absolutely necessary for an ontology, it is also useful to have the knowledge within the textual part of the ontology exposed for computational use. In this work we use an extension to the Ontology PreProcessor Language (OPPL) to dissect terms within the ontology and add axiomatisation, through OPPLs application of ODP, that make the knowledge explicit for computational use. We show the axiomatic enriching of the GO MF; that this can be accomplished both rapidly and consistently; that there is an audit trail for the transformation; and that the queries supported by the ontology are greatly increased in number and complexity.

Task Oriented Evaluation of Module Extraction Techniques

Ontology Modularization techniques identify coherent and often reusable regions within an ontology. The ability to identify such modules, thus potentially reducing the size or complexity of an ontology for a given task or set of concepts is increasingly important in the Semantic Web as domain ontologies increase in terms of size, complexity and expressivity. To date, many techniques have been developed, but evaluation of the results of these techniques is sketchy and somewhat ad hoc. Theoretical properties of modularization algorithms have only been studied in a small number of cases. This paper presents an empirical analysis of a number of modularization techniques, and the modules they identify over a number of diverse ontologies, by utilizing objective, task-oriented measures to evaluate the fitness of the modules for a number of statistical classification problems.

An entropy inspired measure for evaluating ontology modularization

Ontology modularization has received growing interest from the research community lately, since it supports tasks such as ontology design/reuse and knowledge selection and integration. Most of the research efforts have concentrated on approaches to extract modules, or generate partitions from an ontology. However these approaches are influenced by different definitions of ontology modularization and thus tend to vary w.r.t. the concepts and properties in the ontology that should define the module, and on the characteristics that modules should exhibit, which often depend on the task for which the modularization process is performed. This diversity of approaches makes the comparative evaluation of the output of different modularization processes hard to perform. In this paper, we propose an entropy inspired measure for modularization, Integrated Ontology Entropy, that approximates the information content of modules, and hence provides a profile for the module generated. This measure is independent of the modularization technique used, and is calculated as a function of the number of edges connecting the named concepts in the ontology, when a graph representation of the ontology is utilized. In the paper we apply this measure to different modularization techniques and we empirically show how the measure captures different characteristics of modules, such as the degree of redundancy and the level of connectedness.

A context-based architecture for RDF knowledge bases: approach, implementation and preliminary results

In this paper we present a context-based architecture and implementation for supporting the construction and management of contextualized RDF knowledge bases. The goal of this work is to take explicitly into account any possible contextual dependency of a collection of RDF models, without losing sight of performance and scalability issues. We are illustrating motivations, as well as theoretical background, implementation details and test-results of our latest works.

Assessing the safety of knowledge patterns in OWL ontologies

The availability of a concrete language for embedding knowledge patterns inside OWL ontologies makes it possible to analyze their impact on the semantics when applied to the ontologies themselves. Starting from recent results available in the literature, this work proposes a sufficient condition for identifying safe patterns encoded in OPPL. The resulting framework can be used to implement OWL ontology engineering tools that help knowledge engineers to understand the level of extensibility of their models as well as pattern users to determine what are the safe ways of utilizing a pattern in their ontologies.

Evaluating Ontology Modules Using an Entropy Inspired Metric

In this paper we therefore propose a reformulation of the entropy metric to evaluate the amount of information carried by both the ontology structure, and also by the language elements (i.e. the semantics associated with the edges in the ontological graph). To evaluate this approach, the reformulated metric is empirically compared to Calemt & Daemis original entropy metric, for a variety of different sized modules. The results suggest that not only can entropy differentiate between structurally different modules of the same size, but that our improved entropy metric provides a finer grain differentiation than the original entropy metric.

Dynamic Change Evaluation for Ontology Evolution in the Semantic Web

Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as introduction or removal of concept definitions, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase, and given the open nature of the semantic Web, introduction of new ontologies and modifications to existing ones are to be expected. Dynamically handling such changes, without requiring human intervention, becomes crucial. This paper presents a framework that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology.

A Quality Assurance Workflow for Ontologies Based on Semantic Regularities

Syntactic regularities or syntactic patterns are sets of axioms in an OWL ontology with a regular structure. Detecting these patterns and reporting them in human readable form should help the understanding the authoring style of an ontology and is therefore useful in itself. However, pattern detection is sensitive to syntactic variations in the assertions; axioms that are semantically equivalent but syntactically different can reduce the effectiveness of the technique. Semantic regularity analysis focuses on the knowledge encoded in the ontology, rather than how it is spelled out, which is the focus of syntactic regularity analysis. Cluster analysis of the information provided by an OWL DL reasoner mitigates this sensitivity, providing measurable benefits over purely syntactic patterns - an example being patterns that are instantiated only in the entailments of an ontology. In this paper, we demonstrate, using SNOMED-CT, how the detection of semantic regularities in entailed axioms can be used in ontology quality assurance, in combination with lexical techniques. We also show how the detection of irregularities, i.e., deviations from a pattern, are useful for the same purpose. We evaluate and discuss the results of performing a semantic pattern inspection and we compare them against existing work on syntactic regularity detection. Systematic extraction of lexical, syntactic and semantic patterns is used and a quality assurance workflow that combines these patterns is presented.

Using ontology modularization for efficient negotiation over ontology correspondences in MAS

Efficient agent communication in open and dynamic environments relies on the agents ability to reach a mutual understanding over message exchanges. Such environments are characterized by the existence of heterogeneous agents that commit to different ontologies, with no prior assumptions regarding the use of shared vocabularies. Various approaches have therefore considered how mutually acceptable mappings may be determined dynamically between agents through negotiation. In particular, this paper focusses on the meaning based negotiation approach, proposed by Laera et al [1], that makes use of argumentation in order to select a set of mappings that is deemed acceptable by both agents. However, this process can be highly complex, reaching

Chainsaw: A metareasoner for large ontologies

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