To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

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Relations in biomedical ontologies

The availability of whole genome sequences from various model organisms and increasing experimental data and literatures stimulated the evolution of a systems approach for biological research. The development of computational tools and algorithms to study biological pathway networks has made great progress in helping analyze research data. Pathway databases become an integral part of such an approach. This chapter first discusses how biological knowledge is represented, particularly the importance of ontologies or standards in systems biology research. Next, we use PANTHER Pathway as an example to illustrate how ontologies and standards play a role in data modeling, data entry, and data display. Last, we describe the usage of such systems. We also describe the computational tools that utilize PANTHER pathway information to analyze gene expression experimental data.

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PANTHER Pathway: An Ontology-Based Pathway Database Coupled with Data Analysis Tools

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.

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Mining meaning from Wikipedia

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.

http://www.ra.ethz.ch/cdstore/www2006/devel-www2006.ecs.soton.ac.uk/programme/files/pdf/4026.pdf

Web ontology segmentation: analysis, classification and use

This book covers two applications of ontologies in software engineering and software technology: sharing knowledge of the problem domain and using a common terminology among all stakeholders; and filtering the knowledge when defining models and metamodels. By presenting the advanced use of ontologies in software research and software projects, this book is of benefit to software engineering researchers in both academia and industry.

Ontologies for Software Engineering and Software Technology

The rapidly increasing wealth of genomic data has driven the development of tools to assist in the task of representing and processing information about genes, their products and their functions. One of the most important of these tools is the Gene Ontology (GO), which is being developed in tandem with work on a variety of bioinformatics databases. An examination of the structure of GO, however, reveals a number of problems, which we believe can be resolved by taking account of certain organizing principles drawn from philosophical ontology. We shall explore the results of applying such principles to GO with a view to improving GO's consistency and coherence and thus its future applicability in the automated processing of biological data.

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The ontology of the gene ontology.

The integration of standardized biomedical terminologies into a single, unified knowledge representation system has formed a key area of applied informatics research in recent years. The Unified Medical Language System (UMLS) is the most advanced and most prominent effort in this direction, bringing together within its Metathesaurus a large number of distinct source-terminologies. The UMLS Semantic Network, which is designed to support the integration of these source-terminologies, has proved to be a highly successful combination of formal coherence and broad scope. We argue here, however, that its organization manifests certain structural problems, and we describe revisions which we believe are needed if the network is to be maximally successful in realizing its goals of supporting terminology integration.

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Revising the UMLS Semantic Network

Where humans can manipulate and integrate the information they receive in subtle and ever-changing ways from context to context, computers need structured and context-free background information of a sort that ontologies can help to provide. A domain ontology captures the stable, highly general, and commonly accepted core knowledge for an application domain. The domain at issue here is that of the life sciences, in particular, molecular biology and bioinformatics. Contemporary life science research includes components drawn from physics, chemistry, mathematics, medicine, and many other areas, and all of these dimensions, as well as fundamental philosophical issues, must be taken into account in the construction of a domain ontology. Here we describe the basic features of domain ontologies in the life sciences and show how they can be used.


Keywords:

domain ontology;
molecular biology;
bioinformatics;
philosophy

/pdf/ontologies-for-the-life-sciences-2w96cxdc84.pdf

Steffen Schulze-Kremer

Papers

The ontology of the gene ontology.

Revising the UMLS Semantic Network

Ontologies for the life sciences