Integration with Ontologies

One of today’s hottest IT topics is integration, as bringing together information from different sources and structures is not completely solved. The approach outlined here wants to illustrate how ontologies [Gr93] could help to support the integration process

Increasing productivity of knowledge workers by ontological training

Proceedings of the International Conference «Business Sustainability BS 2008», Minho, Portugal, 2008. pp. 158-163knowledge-driven organizations, productivity of knowledge workers, learning, thinking, analyst training,

Ontology Evaluation Functionalities of RDF(S), DAML+OIL, and OWL Parsers and Ontology Platforms

Before using ontologies in Semantic Web applications, ontology content and ontology tools (parsers, platforms, etc.) should be evaluated. In this paper we evaluate ontology evaluation functionalities of RDF(S), DAML+OIL, and OWL parsers and import services for such languages within ontology platforms. In recent years, some RDF(S), DAML+OIL, and OWL parsers have been created and several ontology platforms are able to import ontologies implemented in such languages. In this paper we present two experiments. The first one reveals that most RDF(S), DAML+OIL, and OWL parsers studied do not detect taxonomic problems, from a knowledge representation point of view, in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, the question is: are they able to detect such problems? The second experiment presented in this paper reveals that most ontology platforms analyzed only detect a few of problems in concept taxonomies during ontology import

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

Visual support for ontology learning: an experience report

Ontology learning methods aim to automate ontology construction. They are complex methods involving several elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them

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