The elicitation of the ontology -- i.e. the objects of a domain -- is a key issue of conceptual modelling and therefore of knowledge acquisition. The Conceptual Graph Theory provides a knowledge representation formalism to be used in knowledgebased systems with an explicit "type lattice" to account for the ontology. Since knowledge is in most AI applications non formal, it has to be normalized to ensure that the formal exploitation of its representation conforms to its meaning in the domain. Noting the intensional nature of types, which reflect the essences of the objects they denote, this normalization relies on a commitment on type definitions by necessary and sufficient conditions at the knowledge level. Our claim is that the taxonomic structure that accounts for the intensional nature of the ontology can be nothing but a tree, precluding tangled taxonomies. From this starting point, we derive methodological principles to constrain the acquisition of the "type tree", thus...

This paper is organized as follows. First, we start with a brief recall of basic notions and related literature. Second, we describe the structuration of knowledge in Menelas, and the way it rests on a CTL. Third, we show how UMLS has been used to initiate CTL building, then illustrate how the CTL structures the knowledge acquisition process. Finally, we discuss the advantages and further issues associated with this approach.

Background: The UMLS Metathesaurus (UMLS-Meta) is currently the most comprehensive effort for integrating independently-developed medical thesauri and ontologies. UMLS-Meta is being used in many applications, including PubMed and ClinicalTrials.gov. The integration of new sources combines automatic techniques, expert assessment, and auditing protocols. The automatic techniques currently in use, however, are mostly based on lexical algorithms and often disregard the semantics of the sources being integrated. Results: In this paper, we argue that UMLS-Meta’s current design and auditing methodologies could be significantly enhanced by taking into account the logic-based semantics of the ontology sources. We provide empirical evidence suggesting that UMLS-Meta in its 2009AA version contains a significant number of errors; these errors become immediately apparent if the rich semantics of the ontology sources is taken into account, manifesting themselves as unintended logical consequences that follow from the ontology sources together with the information in UMLS-Meta. We then propose general principles and specific logic-based techniques to effectively detect and repair such errors. Conclusions: Our results suggest that the methodologies employed in the design of UMLS-Meta are not only very costly in terms of human effort, but also error-prone. The techniques presented here can be useful for both reducing human effort in the design and maintenance of UMLS-Meta and improving the quality of its contents. 1

Information Information

Abstract. The application of upper ontologies has been repeatedly advocated for supporting interoperability between domain ontologies in order to facilitate shared data use both within and across disciplines. We have developed BioTop as a topdomain ontology to integrate more specialized ontologies in the biomolecular and biomedical domain. In this paper, we report on concrete integration problems of this ontology with the domain-independent Basic Formal Ontology (BFO) concerning the issue of fiat and aggregated objects in the context of different granularity levels. We conclude that the third BFO level must be ignored in order not to obviate cross-granularity integration.

biomedical resources

THENATIONAL LIBRARY OF MEDICINE (NLM) is engaged in a longterm project to develop a Unified Medical Language System (UMLS) that will retrieve and integrate information from a variety of information resources. Two UMLS components use fundamental aspects of controlled vocabulary structure and management and their relationship to information retrieval that have general interest for librarianship. The UMLS project is described along with its initial deployment in retrieval environments.