Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and

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:

The Gene Ontology (GO) is a controlled vocabulary widely used for the annotation of gene products. GO is organized in three hierarchies for molecular functions, cellular components, and biological processes but no relations are provided among terms across hierarchies. The objective of this study is to investigate three non-lexical approaches to identifying such associative relations in GO and compare them among themselves and to lexical approaches. The three approaches are: computing similarity in a vector space model, statistical analysis of co-occurrence of GO terms in annotation databases, and association rule mining. Five annotation databases (FlyBase, the Human subset of GOA, MGI, SGD, and WormBase) are used in this study. A total of 7,665 associations were identified by at least one of the three non-lexical approaches. Of these, 12 % were identified by more than one approach. While there are almost 6,000 lexical relations among GO terms, only 203 associations were identified by both non-lexical and lexical approaches. The associations identified in this study could serve as the starting point for adding associative relations across hierarchies to GO, but would require manual curation. The application to quality assurance of annotation databases is also discussed. 1.

doi:10.1093/bioinformatics/bti749

This paper provides an axiomatic formalization of a theory of foundational relations between three categories of entities: individuals, universals, and collections.

A number of sophisticated medical ontologies have been created over the past years. With their development the need for supporting the alignment of di#erent ontologies is gaining importance. We proposed C-OWL, an extension of the Web Ontology Language OWL that supports alignment mappings between di#erent, possibly incompatible ontologies on a semantic level. In this paper we report experiences from using C-OWL for the alignment of medical ontologies. We briefly review key concepts of the C-OWL semantics, explain the setting of the case study including some examples from the alignment and discuss the possibility of reasoning about the mapping based on the C-OWL semantics We conclude by arguing that C-OWL provides an adequate framework for aligning complex ontologies in the medical domain.

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

We describe a prototype Context Aware Perioperative Information System to capture and interpret data in an operating room of the future. The captured data is used to construct the context of the surgical procedure and detect medically significant events. Such events, and other state information, are used to automatically construct an Electronic Medical Encounter Record (EMR). The EMR records and correlates significant medical data and video streams with an inferred higher-level event model of the surgery. Information from sensors such as Radio Frequency Identification (RFID) tags provides basic context information including the presence of medical staff, devices, instruments and medication in the operating room (OR). Patient monitoring systems and sensors such as pulse oximeters and anesthesia machines provide continuous streams of physiological data. These low level data streams are processed to generate higher-level primitive events, such as a nurse entering the OR. A hierarchical knowledge-based event detection system correlates primitive events, patient data and workflow data to infer high-level events, such as the onset of anesthesia. The resulting EMR provides medical staff with a permanent record of the surgery that can be used for subsequent evaluation and training. The system can also be used to detect potentially significant errors. It seeks to automate some of the tasks done by nursing staff today that detracts from their ability to attend to the patient.

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