vocabulary that provides consistent, unambiguous codes and definitions for concepts used in cancer research ” and which “exhibits ontology-like properties in its construction and use”. We performed a qualitative analysis of the Thesaurus in order to assess its conformity with principles of good practice in terminology and ontology design. Materials and methods: We used both the on-line browsable version of the Thesaurus and its OWL-representation (version 04.08b, released on August 2, 2004), measuring each in light of the requirements put forward in relevant ISO terminology standards and in light of ontological principles advanced in the recent literature. Results: We found many mistakes and inconsistencies with respect to the term-formation principles used, the underlying knowledge representation system, and missing or inappropriately assigned verbal and formal definitions. Conclusion: Version 04.08b of the NCI Thesaurus suffers from the same broad range of problems that have been observed in other biomedical terminologies. For its further development, we recommend the use of a more principled approach that allows the Thesaurus to be tested not just for internal consistency but also for its degree of correspondence to that part of reality which it is designed to

Formalisms such as description logics (DL) are sometimes expected to help terminologies ensure compliance with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) complies with such principles. We defined seven ontological principles (for example: each class must have at least one parent, each class must differ from its parent) and examined the properties of SNOMED CT classes with respect to these principles. Our major results are: 31 % of the classes have a single child; 27 % have multiple parents; 51 % do not exhibit any differentiae between the description of the parent and that of the child. The applications of this study to quality assurance for ontologies are discussed and suggestions are made for dealing with multiple inheritance.

Biomedical terminologies and ontologies are frequently described as enabling resources in text mining systems [e.g., 1, 2, 3]. These resources are used to supports tasks such as entity recognition (i.e., the identification of biomedical

from medical and scientific literature, in particular, from Medline abstracts and from full-text articles. Such information is being used, for example, to reconstruct biological pathways, identify pathogenic mechanisms and, importantly, to identify functional relationships that can be used to predict disease onset and its course thereafter. Our interest is in exploring the role of knowledge representation and reasoning (KR&R) as it relates to the problem of understanding biomedical text. The role we envision for a KR&R system in this context is as a knowledge store for a small, focused subset of abstracts gleaned from the Medline corpus that is relevant to a problem of interest. The system will infer new knowledge from the represented abstracts which can then be stored in a larger data repository and reported to a biologist. We are specifically interested in designing a system that, given a set of abstracts, can perform many of the same inferences that a biology expert would make if given the same set of abstracts. Inferences that go beyond the predictions of the biologist are particularly interesting, but our initial goal is to emulate the biologist. We have selected the disease neurofibromatosis type 1 (NF1) for our study with the goal of developing a model that can be applied to reasoning about other diseases and problems of interest. This approach is focused narrowly on a particular problem and as such may not lead to solutions relevant for general problem solving. However, we believe there is an important role for specialized problem solvers in the larger context of biomedical text understanding. Working closely with a domain expert in biology is providing valuable insights into how the computational synthesis of information might best serve the needs of a biologis...

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We describe the ontological modeling issues encountered in the EU-funded NEU-ROWEB project. The aim of the project is to support association studies in the field of neurovascular medicine, integrating the different local repositories maintained by the clinical partners. Specifically association studies are carried out by

Lexical relations organise and structure vocabulary. They reflect relations in the real world. Meronymy is the lexical relation between a word representing a part, and a word representing a whole, such as (finger partOf hand). Meronymy is of special importance in biomedicine: structure and process are organised along partitive axes. Anatomy, for example, is rich in part-whole relations. This importance is reflected in the modelling of meronymy in several biomedical knowledge resource. Much research has been carried out on the extraction of taxonomies (isA or kindOf relations) from text. Very little research has applied the same techniques to meronymy, and where it has been carried out, it has been limited in one way or another. This report examines meronymy and its place in biomedicine, reviews earlier work on part-whole extraction, and proposes extensions to it.

Conference Paper Turning informal thesauri into formal ontologies: a feasibility study on biomedical knowledge re-use