Algebraic graph transformations for merging ontologies

The conception of an ontology is a complex task influenced by numerous factors like the point of view of the authors or the level of details. Consequently, several ontologies have been developed to model identical or related domains leading to partially overlapping representations. This divergence of conceptualization requires the study of ontologies merging in order to create a common repository of knowledge and integrate various sources of information. In this paper, we propose a formal approach for merging ontologies using typed graph grammars. This method relies on the algebraic approach to graph transformations, SPO (Simple PushOut) which allows a formal representation and ensures the consistence of the results. Furthermore, a new ontologies merging algorithm called GROM (Graph Rewriting for Ontology Merging) is presented

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

Identification of Design Principles

This report identifies those design principles for a (possibly new) query and transformation language for the Web supporting inference that are considered essential. Based upon these design principles an initial strawman is selected. Scenarios for querying the Semantic Web illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of the query language to be designed and implemented by the REWERSE working group I4

Ontology Mapping: The State of the Art

Ontology mapping is seen as a solution provider in today\u27s landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

A new method for interoperability between lexical resources using MDA approach

International audienceLexical resources are increasingly multiplatform due to the diverse needs of linguists. Merging, comparing, finding correspondences and deducing differences between these lexical resources remain difficult tasks. Thus, inte-roperability between these resources is hard even impossible to achieve. In this context, we establish a new method based on MDA approach to resolve interoperability between lexical resources. The proposed method consists of building common structure (OWL-DL ontology) for involved resources. This common structure has the ability to communicate involved resources. Hence, we may create a complex grid between involved resources allowing transformation from one format to another. We experiment our new built method on an LMF lexicon

Ontology translation approaches for interoperability: A case study with Protege-2000 and WebODE

We describe four ontology translation approaches that can be used to exchange ontologies between ontology tools and/or ontology languages. These approaches are analysed with regard to two main features: how they preserve the ontology semantics after the translation process (aka semantic or consequence preservation) and how they allow final users and ontology-based applications to understand the resulting ontology in the target format (aka pragmatic preservation). These approaches are illustrated with practical examples that show how they can be applied to achieve interoperability between the ontology tools Protege-2000 and WebODE

Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools