Ontologies are at the heart of the semantic web. They define the concepts and relationships that make global interoperability possible. However, as these ontologies grow in size they become more and more difficult to create, use, understand, maintain, transform and classify. We present and evaluate several algorithms for extracting relevant segments out of large description logic ontologies for the purposes of increasing tractability for both humans and computers. The segments are not mere fragments, but stand alone as ontologies in their own right. This technique takes advantage of the detailed semantics captured within an OWL ontology to produce highly relevant segments. The research was evaluated using the GALEN ontology of medical terms and procedures.

In recent years, as a knowledge-based discipline, bioinformatics has moved to make its knowledge more computationally amenable. After its beginnings in the disciplines as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by the biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine. Author biographies:

Abstract. This paper reports our experience with OWL for the Foundational Model of Anatomy (FMA). We show that converting the FMA from Protégé into OWL DL was possible, with most features of the original FMA captured. The conversion relies on translation and enrichment rules, implemented with flexible options. Unsurprisingly, reasoning with OWL proved to be a real challenge, due to the sheer size and complexity of the FMA. As the entire FMA in OWL DL raised inference problems hard to solve in terms of time and memory, an incremental approach was adopted. A number of various smaller versions that Racer could handle were successfully tested. Some inconsistencies were identified and some classes reclassified. The analysis of the results obtained so far shows the benefits of representing the FMA in OWL and, more generally, the usefulness of DLs reasoning techniques for large-scale biomedical ontologies shared on the Web. 1

The difficulty inherent in schema matching has led to the development of several generic match algorithms. This paper describes how we adapted general approaches to the specific task of aligning two ontologies of human anatomy, the Foundational Model of Anatomy and the GALEN Common Reference Model. Our approach consists of three phases: lexical, structural and hierarchical, which leverage different aspects of the ontologies as they are represented in a generic meta-model. Lexical matching identifies concepts with similar names. Structural matching identifies concepts whose neighbors are similar. Finally, hierarchical matching identifies concepts with similar descendants. We conclude by reporting on the lessons we learned.

Ontology is a burgeoning field, involving researchers

Integration of ontologies begins with establishing mappings between their concept entries. We map categories from the largest manually-built ontology, Cyc, onto Wikipedia articles describing corresponding concepts. Our method draws both on Wikipedia’s rich but chaotic hyperlink structure and Cyc’s carefully defined taxonomic and common-sense knowledge. On 9,333 manual alignments by one person, we achieve an F-measure of 90%; on 100 alignments by six human subjects the average agreement of the method with the subject is close to their agreement with each other. We cover 62.8 % of Cyc categories relating to common-sense knowledge and discuss what further information might be added to Cyc given this substantial new alignment. 1.

Abstract. The proposed use case focuses on interoperating between a rule base and a brain cortex anatomy ontology, in order to assist the labeling of the brain cortex structures- sulci and gyri- involved in MRI images. The use case documents the ontology and the rules so as to clarify the added value and needs of rules, and the language expressiveness required. The expected result is to get candidate languages extending OWL DL with rules that allow representing all the knowledge required (ontology and rules), joint with the properties of reasoning that are guaranteed. 1. Presentation of the application Our group is interested in image processing in the context of neuroimaging, applied both to medical practice, i.e. decision support in neurology and neurosurgery, and to research about neurological pathology such as epilepsy, dementia, etc. The considered application aims at developing new methods for assisting the labeling of the brain cortex structures- sulci and gyri- in MRI images. Indeed, nowadays the brain cortex can be automatically segmented but the problem remains to identify its various parts. Numerical tools previously developed at IDM provide a list of items corresponding to the gyrus parts and sulcus segments

Objective: To investigate the indirect alignment of two anatomical ontologies through a reference ontology and to compare it to direct alignment between these two ontologies. The ontologies under investigation are the Adult Mouse Anatomical Dictionary (MA) and the NCI Thesaurus (NCI). The Foundational Model of Anatomy serves as reference ontology. Methods: The direct alignment employs a combination of lexical and structural similarity. The indirect alignment simply derives mappings from direct alignments to the reference ontology. Results: The indirect MA-NCI alignment yielded 703 mappings and the direct alignment 715, 654 of which are common to both. The mappings specific to one approach were analyzed. Conclusions: When a reference ontology exists, indirect alignment of multiple ontologies through a reference represents a valid, cost-effective alternative to pairwise alignment.

Objectives: To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods: Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results: The ontologies under investigation in this survey include

Applications of semantic technologies often require the representation of and reasoning with structured objects—that is, objects composed of parts connected in complex ways. Although OWL is a general and powerful language, its class descriptions and axioms cannot be used to describe arbitrarily connected structures. OWL representation of structured objects can thus be underconstrained, which reduces the inferences that can be drawn and causes performance problems in reasoning. To address these problems, we extend OWL with description graphs, which provide for the description of structured objects in a simple and precise way. To represent conditional aspects of the domain, we also allow for SWRL-like rules over description graphs. Based on a novel observation about the nature of structured objects, we ensure decidability of our formalism. We also present a hypertableau-based decision procedure, which we implemented in the HermiT reasoner. To evaluate its performance, we extracted description graphs from the GALEN and FMA ontologies, classified them successfully, and even detected a modeling error in GALEN. 1