[Ontic, ontologic and ontology in psychiatry].
Explore the source record for details and available documents.
Humans↗
SEARCH · Search PubMed
Results for “ontologies”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ontology metadata to support the building of a library of biomedical ontologies.
Explore the source record for details and available documents.
Databases as Topic↗
GOLEM: an interactive graph-based gene-ontology navigation and analysis tool.
BACKGROUND: The Gene Ontology has become an extremely useful tool for the analysis of genomic data and structuring of biological knowledge. Several excellent software tools for navigating the gene ontology have been developed. However, no existing system provides an interactively expandable graph-based view of the gene ontology hierarchy. Furthermore, most existing tools are web-based or require an Internet connection, will not load local annotations files, and provide either analysis or visualization functionality, but not both. RESULTS: To address the above limitations, we have developed GOLEM (Gene Ontology Local Exploration Map), a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet at http://function.princeton.edu/GOLEM.
Computer Graphics↗
Formal ontology for natural language processing and the integration of biomedical databases.
The central hypothesis underlying this communication is that the methodology and conceptual rigor of a philosophically inspired formal ontology can bring significant benefits in the development and maintenance of application ontologies [A. Flett, M. Dos Santos, W. Ceusters, Some Ontology Engineering Procedures and their Supporting Technologies, EKAW2002, 2003]. This hypothesis has been tested in the collaboration between Language and Computing (L&C), a company specializing in software for supporting natural language processing especially in the medical field, and the Institute for Formal Ontology and Medical Information Science (IFOMIS), an academic research institution concerned with the theoretical foundations of ontology. In the course of this collaboration L&C's ontology, LinKBase, which is designed to integrate and support reasoning across a plurality of external databases, has been subjected to a thorough auditing on the basis of the principles underlying IFOMIS's Basic Formal Ontology (BFO) [B. Smith, Basic Formal Ontology, 2002. http://ontology.buffalo.edu/bfo]. The goal is to transform a large terminology-based ontology into one with the ability to support reasoning applications. Our general procedure has been the implementation of a meta-ontological definition space in which the definitions of all the concepts and relations in LinKBase are standardized in the framework of first-order logic. In this paper we describe how this principles-based standardization has led to a greater degree of internal coherence of the LinKBase structure, and how it has facilitated the construction of mappings between external databases using LinKBase as translation hub. We argue that the collaboration here described represents a new phase in the quest to solve the so-called "Tower of Babel" problem of ontology integration [F. Montayne, J. Flanagan, Formal Ontology: The Foundation for Natural Language Processing, 2003. http://www.landcglobal.com/].
Artificial Intelligence↗
The Ontology Lookup Service, a lightweight cross-platform tool for controlled vocabulary queries.
BACKGROUND: With the vast amounts of biomedical data being generated by high-throughput analysis methods, controlled vocabularies and ontologies are becoming increasingly important to annotate units of information for ease of search and retrieval. Each scientific community tends to create its own locally available ontology. The interfaces to query these ontologies tend to vary from group to group. We saw the need for a centralized location to perform controlled vocabulary queries that would offer both a lightweight web-accessible user interface as well as a consistent, unified SOAP interface for automated queries. RESULTS: The Ontology Lookup Service (OLS) was created to integrate publicly available biomedical ontologies into a single database. All modified ontologies are updated daily. A list of currently loaded ontologies is available online. The database can be queried to obtain information on a single term or to browse a complete ontology using AJAX. Auto-completion provides a user-friendly search mechanism. An AJAX-based ontology viewer is available to browse a complete ontology or subsets of it. A programmatic interface is available to query the webservice using SOAP. The service is described by a WSDL descriptor file available online. A sample Java client to connect to the webservice using SOAP is available for download from SourceForge. All OLS source code is publicly available under the open source Apache Licence. CONCLUSION: The OLS provides a user-friendly single entry point for publicly available ontologies in the Open Biomedical Ontology (OBO) format. It can be accessed interactively or programmatically at http://www.ebi.ac.uk/ontology-lookup/.
Database Management Systems↗
Logical ontology for mediating between nursing intervention terminology systems.
OBJECTIVES: Several researchers have proposed the use of logical ontologies as 'reference terminologies'. However, there are a number of unresolved issues. This article describes the development of a logical ontology for nursing interventions and presents the results of evaluation. METHODS: Initially this study involved the development in GRAIL of two separate experimental ontologies: an ontology based on the textual content of informal definitions for nursing interventions drawn from the Nursing Interventions Classification; and an ontology based on labels for the same nursing interventions. Following initial bench-testing, the ontology based on labels was selected for extension (to accommodate also nursing intervention components of the Home Health Care Classification System and the Omaha System), for further testing and for external evaluation. RESULTS: A hierarchy of nursing interventions generated automatically from the experimental ontology based on informal definitions contained only 3 hierarchical relationships, compared to 214 for the initial ontology based on labels. For the final extended ontology based on labels, the generated hierarchy contained the three source terminology systems in entirety - there were a total of 2861 hierarchical relationships. While the results of comparative bench testing of the final ontology were favourable, the results of external evaluation were mixed and showed little agreement between reviewers. CONCLUSION: This study suggests that while a logical ontology based on labels might be a useful tool for mediating between nursing intervention terminology systems, a formative consensus type development methodology might improve the approach by helping to harmonise ideological differences that may exist across the nursing profession.
Humans↗
Quality assurance of medical ontologies.
OBJECTIVE: To review the literature concerning the quality assurance of medical ontologies. METHODS: scholar.google.com was searched using the search strings (+ontology +"quality assurance") and (+ontology +"evaluation/evaluating"). Relevant publications were selected by manual review. Other work already familiar to the author, or suggested by other researchers contacted by the author, were included. The papers were analysed for common themes. RESULTS: Four broad properties of an ontology were identified that may be quality-assured: philosophical validity, compliance with meta-ontological commitments, 'content correctness', and fitness for purpose. Each published methodology addressed only a subset of these properties. 'Content' may be divided into domain knowledge content, and metadata describing either the provenance of domain knowledge content, or relationships between it and lexical information (e.g. for display and retrieval). 'Correctness' (whether of domain knowledge content or metadata) may also be further subdivided into truth, completeness, parsimony and internal consistency. CONCLUSIONS: Understanding of how to assure the quality of ontologies, or evaluate their fitness for specific purposes, is improving but remains poor. A combination of methodologies is required, but tools to support a comprehensive quality assurance programme remain lacking. Perfect quality of an ontology is not provable and may not be desirable: an ontology compliant with all current philosophical theories, following necessary ontological commitments, and with entirely 'correct' content, may be too complex to be directly usable or useful. The extent to which an ontology's fitness for purpose is predicted or influenced by its other properties remains to be determined. Field studies of ontologies in use, including interrater effects, are required.
Medical Informatics↗
Ontology annotation: mapping genomic regions to biological function.
With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.
Animals↗
Desiderata for domain reference ontologies in biomedicine.
Domain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain. An example of reference ontology in biomedical informatics is the Foundational Model of Anatomy (FMA), an ontology of anatomy that covers the entire range of macroscopic, microscopic, and subcellular anatomy. The purpose of this paper is to explore how two domain reference ontologies--the FMA and the Chemical Entities of Biological Interest (ChEBI) ontology, can be used (i) to align existing terminologies, (ii) to infer new knowledge in ontologies of more complex entities, and (iii) to manage and help reasoning about individual data. We analyze those kinds of usages of these two domain reference ontologies and suggest desiderata for reference ontologies in biomedicine. While a number of groups and communities have investigated general requirements for ontology design and desiderata for controlled medical vocabularies, we are focusing on application purposes. We suggest five desirable characteristics for reference ontologies: good lexical coverage, good coverage in terms of relations, compatibility with standards, modularity, and ability to represent variation in reality.
Anatomy↗
Effects of an ontology display with history representation on organizational memory information systems.
Ontologies, as a possible element of organizational memory information systems, appear to support organizational learning. Ontology tools can be used to share knowledge among the members of an organization. However, current ontology-viewing user interfaces of ontology tools do not fully support organizational learning, because most of them lack proper history representation in their display. In this study, a conceptual model was developed that emphasized the role of ontology in the organizational learning cycle and explored the integration of history representation in the ontology display. Based on the experimental results from a split-plot design with 30 participants, two conclusions were derived: first, appropriately selected history representations in the ontology display help users to identify changes in the ontologies; and second, compatibility between types of ontology display and history representation is more important than ontology display and history representation in themselves.
Female↗
Quality control for terms and definitions in ontologies and taxonomies.
BACKGROUND: Ontologies and taxonomies are among the most important computational resources for molecular biology and bioinformatics. A series of recent papers has shown that the Gene Ontology (GO), the most prominent taxonomic resource in these fields, is marked by flaws of certain characteristic types, which flow from a failure to address basic ontological principles. As yet, no methods have been proposed which would allow ontology curators to pinpoint flawed terms or definitions in ontologies in a systematic way. RESULTS: We present computational methods that automatically identify terms and definitions which are defined in a circular or unintelligible way. We further demonstrate the potential of these methods by applying them to isolate a subset of 6001 problematic GO terms. By automatically aligning GO with other ontologies and taxonomies we were able to propose alternative synonyms and definitions for some of these problematic terms. This allows us to demonstrate that these other resources do not contain definitions superior to those supplied by GO. CONCLUSION: Our methods provide reliable indications of the quality of terms and definitions in ontologies and taxonomies. Further, they are well suited to assist ontology curators in drawing their attention to those terms that are ill-defined. We have further shown the limitations of ontology mapping and alignment in assisting ontology curators in rectifying problems, thus pointing to the need for manual curation.
Artificial Intelligence↗
Automating data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML.
Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.
Databases, Factual↗
Building an ontology of pulmonary diseases with natural language processing tools using textual corpora.
Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.
France↗
Ontology-based knowledge representation for bioinformatics.
Much of biology works by applying prior knowledge ('what is known') to an unknown entity, rather than the application of a set of axioms that will elicit knowledge. In addition, the complex biological data stored in bioinformatics databases often require the addition of knowledge to specify and constrain the values held in that database. One way of capturing knowledge within bioinformatics applications and databases is the use of ontologies. An ontology is the concrete form of a conceptualisation of a community's knowledge of a domain. This paper aims to introduce the reader to the use of ontologies within bioinformatics. A description of the type of knowledge held in an ontology will be given.The paper will be illustrated throughout with examples taken from bioinformatics and molecular biology, and a survey of current biological ontologies will be presented. From this it will be seen that the use to which the ontology is put largely determines the content of the ontology. Finally, the paper will describe the process of building an ontology, introducing the reader to the techniques and methods currently in use and the open research questions in ontology development.
Artificial Intelligence↗
A fuzzy ontology and its application to news summarization.
In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.
Abstracting and Indexing↗
The semantic metadatabase (SEMEDA): ontology based integration of federated molecular biological data sources.
A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" [2]. SEMEDA can be found at http://www-bm.cs.uni-magdeburg.de/semeda/. We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.
Databases, Genetic↗
The epistemological-ontological divide in clinical radiology.
Medical ontologies like GALEN, the FMA or SNOMED represent a kind of "100% certain" medical knowledge which is not inherent to all medical sub-domains. Clinical radiology uses computerized imaging techniques to make the human body visible and interprets the imaging findings in a clinical context delivering a textual report. For clinical radiology few standardized vocabularies are available. We examined the definitions given in the glossary of terms for thoracic radiology published by the Fleischner Society. We further classified these terms with regard to their definitions in terms of (a) describing visible structures on the image itself, (b) referring to ontological entities of the body (anatomical or pathological), and (c) terms imposing knowledge on structures visible on the image, epistemologically representing ontological entities of the body. Each ontological/epistemological definition was rated on a scale of vague/weak-sound/strong and put in context with the evaluation comments for the use of the terms given in the glossary itself. The result of this distinction shows that clinical radiology uses many terms referring to ontological entities valid for representation in a medical ontology. However, many epistemological terms exist in the terminology which impose epistemological knowledge on ontological entities. The analysis of the evaluation comments reveals that terms classified as sound (ontologically) and strong (epistemologically) are evaluated higher than terms bearing vague or weak definitions. On the basis of this, we argue that the distinction between ontological and epistemological definitions is necessary in order to construct epistemologically-sensitive application ontologies for medical sub-domains, like clinical radiology, where knowledge is fragmented in terms of description, inferred from a description, concluded on the basis of imaging, or other additional information with varying degrees of certainty.
Electronic Data Processing↗
ONTOFUSION: ontology-based integration of genomic and clinical databases.
ONTOFUSION is an ontology-based system designed for biomedical database integration. It is based on two processes: mapping and unification. Mapping is a semi-automated process that uses ontologies to link a database schema with a conceptual framework-named virtual schema. There are three methodologies for creating virtual schemas, according to the origin of the domain ontology used: (1) top-down--e.g. using an existing ontology, such as the UMLS or Gene Ontology--, (2) bottom-up--building a new domain ontology-- and (3) a hybrid combination. Unification is an automated process for integrating ontologies and hence the database to which they are linked. Using these methods, we employed ONTOFUSION to integrate a large number of public genomic and clinical databases, as well as biomedical ontologies.
Data Collection↗