PubMed Health⌕ Search

Biomedical subjects

G Steve

Publications and source records attributed to G Steve.

6 recordsLinked to original sources
PubMed2000

Understanding systematic conceptual structures in polysemous medical terms.

Polysemy is a bottleneck for the demanding needs of semantic data management. We suggest the importance of a well-founded conceptual analysis for understanding some systematic structures underlying polysemy in the medical lexicon. We present some cases studies, which exploit the methods (ontological integration and general theories) and tools (description logics and ontology libraries) of the ONIONS methodology defined elsewhere by the authors. This paper addresses an aspect (systematic metomymies) of the project we are involved in, which investigates the feasibility of building a large-scale ontology library of medicine that integrates the most important medical terminology banks.

Linguistics↗
PubMed1999

Toward a standard for guideline representation: an ontological approach.

Guidelines for clinical practice are being introduced in an extensive way in more and more different fields of medicine They have the potentialities of improving the quality and cost-efficiency of care in an increasingly complex health care delivery environment. Computerization may increase the effectiveness of both the information retrieval of guidelines and the management of guideline-based care. The scenario is evolving from stand-alone workstations to telematics applications that enable guidelines development and dissemination. However, such a knowledge sharing requires the definition of formal models for guidelines representation. The models should have a clear semantics in order to avoid ambiguities. The role of ontologies is that of making explicit the conceptualizations behind a model. In this paper we present our library of ontologies and point out its role for integrating existing guideline models and defining standard representations.

Humans↗
PubMed1998

An ontological analysis of the UMLS Metathesaurus.

Paper-based terminology systems cannot satisfy anymore the new desiderata of healthcare information systems: the demand for re-use and sharing of patient data, their transmission and the need of semantic-based criteria for purposive statistical aggregation. The unambiguous communication of complex and detailed medical concepts is now a crucial feature of medical information systems. Ontologies can support a more effective data and knowledge sharing in medicine. In this paper we briefly survey our ontological analysis and integration of various top-levels of terminologies and we report the main results of the ontological analysis of the UMLS Metathesaurus.

Semantics↗
PubMed1997

WWW-available conceptual integration of medical terminologies: the ONIONS experience.

We present the most applicable aspects of our research in the conceptual integration of terminologies. From past experience, we claim that the conceptualizations provided for terminological ontologies need to be philosophically and linguistically grounded. We developed ONIONS, a methodology for integrating domain terminologies by exploiting a library of generic ontologies. Our current focus is on flexible and cooperative modelling of terminological ontologies. We adopt modular and negotiable architectures of ontologies and some WWW-oriented tools, such as Ontolingua and Ontosaurus.

Computer Communication Networks↗
PubMed1995

Cognitive design for sharing medical knowledge models.

By building a medical conceptual model inside the galen project, a methodology has been defined to deal with both operative goals and deep and unsolved problems about knowledge. This knowledge sharing oriented approach exploits existing medical coding systems such as knowledge sources and enables us to represent their concepts in a wider cognitive context referring to ontological theories. The tasks of cognitive ergonomy, quasi-naturalness of language, versatility, and flexibility seem to be supported through some functions, such as viewpoint, context, and sign, which act as spin-off points to knowledge or information which is not modeled.

Artificial Intelligence↗