Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Methodologies, tools and languages for building ontologies. Where is their meeting point?

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available

WebODE: A Scalable Workbench for Ontological Engineering

This paper presents WebODE as a workbench for ontological engineering that not only allows the collaborative edition of ontologies at the knowledge level, but also provides a scalable architecture for the development of other ontology development tools and ontology-based applications. First, we will describe the knowledge model of WebODE, which has been mainly extracted and improved from the reference model of METHONTOLOGY’s intermediate representations. Later, we will present its architecture, together with the main functionalities of the WebODE ontology editor, such as its import/export service, translation services, ontology browser, inference engine and axiom generator, and some services that have been integrated in the workbench: WebPicker, OntoMerge and the OntoCatalogue

Results of Taxonomic Evaluation of RDF(S) and DAML+OIL Ontologies using RDF(S) and DAML+OIL Validation Tools and Ontology Platforms Import Services

Before using RDF(S) and DAML+OIL ontologies in Semantic Web applications, its content should be evaluated from a knowledge representation point of view. In recent years, some RDF(S) and DAML+OIL ‘checkers’, ‘validators’, and ‘parsers’ have been created and several ontology platforms are able to import RDF(S) and DAML+OIL ontologies. Two are the experiments presented in this paper. The first one reveals that the majority of RDF(S) and DAML+OIL parsers (Validating RDF Parser, RDF Validation Service, DAML Validator, and DAML+OIL Ontology Checker) do not detect taxonomic mistakes in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, are they able to detect such problems? The second experiment presented in this paper reveals that the majority of the ontology platforms (OilEd, OntoEdit, Protégé-2000, and WebODE) only detect a few of mistakes in concept taxonomies before importing them