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Pharmaceutical ontologies glossary & taxonomy
Evolving Terminology for Emerging Technologies
Comments? Questions? Revisions?  Mary Chitty  mchitty@healthtech.com
Last revised October 22, 2007
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Finding guide to terms in these glossaries  Informatics map   Site Map  
Ontologies is a subset of Information management & interpretation 

biological databases: Biological databases have inherent complications stemming from the nature of the information they contain and the dependence of computational methods on these data. Most biological data are not digital, making machine- readability of the data (for automated data- mining) impossible. In addition, the lack of standardized nomenclature and ontology, the use of protein aliases (leading to ambiguity), the lack of interoperability across databases, and the presence of errors in database annotations have hindered and complicated the use of computational methods. Defining the Mandate of Proteomics in the Post- Genomics Era, Board on International Scientific Organizations, National Academy of Sciences, 2002 http://www.nap.edu/books/NI000479/html/R1.html

biomedical ontologies: Open Biomedical Ontologies is an umbrella web address for well-structured controlled vocabularies for shared use across different biological and medical domains.  http://obo.sourceforge.net/ 

Google = about 102, Jan. 8, 2003; about 294 Oct. 1, 2003; about 490 Oct 22, 2004; about 488 May 2, 2005

BioPax:  Biological Pathways Exchange.  A collaborative effort to create a data exchange format for biological pathway data. http://www.biopax.org

BioPortal: Provides access to open-source ontology management, query, and visualization tools - enabling users to access OBO for ontology development, peer review, and version management. We are creating data annotation tools that assist researchers in locating terms in OBO and other ontologies and in associating them with primary experimental data. http://www.bioontology.org/resources-bioportal.html

bottom-up ontologies: Are flexible through the use of implicit and, hence, parsimonious part- whole and subconcept- superconcept relations. The bottom- up method complements current practice, where, as a rule, ontologies are built top- down. The design method is illustrated by an example involving ontologies of pure substances at several levels of detail. It is not claimed that bottom- up construction is a generally valid recipe; indeed, such recipes are deemed uninformative or impossible. Rather, the approach is intended to enrich the ontology developer's toolkit. [Paul E. van der Vet, Nicolaas J.I. Mars, Bottom- Up Construction of Ontologies, IEEE Transactions on Knowledge Engineering, July- Aug, 1998 10(4): 513- 526] http://www.computer.org/tkde/tk1998/k0513abs.htm no longer linked to article

Google = "bottom-up ontologies" about 10 July 19, 2002; about 90 Feb. 20, 2006

clinical ontologies: Ontologies are correctly defined as hierarchies of concepts but are frequently applied to mean controlled syntax, database schema, semantic networks or thesaurus. In using an ontological approach to extract knowledge about disease progression and disease presentation, including co-morbidities, we have extended the approach of ontology construction to incorporate critical temporal domains. Towards this goal, we have applied LexiMine (SPSS) as a method for syntactical analysis of free text to establish the value in the analysis of full articles versus abstracts in knowledge extraction. Ontologies in Breast Cancer: Concepts vs. Words, Dr. Michael Liebman, Abramson Cancer Center of the University of Pennsylvania Data Integration for the Pharmaceutical Industry, Sept. 24-25, 2003, Baltimore MD

Google = about 50 May 29, 2003; about 74 June 10, 2004; about 291 Feb. 20, 2006

common ontology: Defines the vocabulary with which queries and assertions are exchanged among agents. ... The agents sharing a vocabulary need not share a knowledge base; each knows things the other does not, and an agent that commits to an ontology is not required to answer all queries that can be formulated in the shared vocabulary. In short, a commitment to a common ontology is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology. Tom Gruber, What is an ontology?"  Knowledge Systems Lab, Stanford Univ. 2001 http://www-ksl.stanford.edu/kst/what-is-an-ontology.html

Google = about 1,190 July 19, 2002, about 4,130 Oct. 22, 2004; about 45,100 Feb. 20, 2006

controlled vocabulary: Robin Cover's XML Cover Pages is described as "a collection of references on matters of Subject Classification, Taxonomies, Ontologies, Indexing, Metadata, Metadata Registries, Controlled Vocabularies, Terminology, Thesauri, Business Semantics", 2003 http://xml.coverpages.org/classification.html

A limited number of words or phrases used in an indexing system (subject headings) or database, to ensure reliable, consistent retrieval. Long used to enhance retrievability and consistency, ontologies and/ or taxonomies certainly sound sexier than "controlled vocabularies" but continue to have a good deal in common. Taxonomies add hierarchies, while ontologies make information "machine- understandable" as well as machine- readable. 

Google = about 39,700 July 19, 2002; about 85,300 Oct. 22, 2004 

core ontologies: http://www.loa-cnr.it/core_onto.html 

Google = about 880 Feb. 20, 2006

descriptive ontology: A descriptive ontology would try to explain how things are, whereas a normative ontology would try to tell us how things ought to be. Robert Kent "Ballot comment", Standard Upper Ontology [SUO] E-mail archive, IEEE, 2001 http://suo.ieee.org/email/msg05921.html

Google = about 121 July 19, 2002; about 343 Oct. 22, 2004; about 680 Feb. 20, 2006

Directed Acyclic Graph DAG: A directed graph where no path starts and ends at the same vertex. See also directed graph, acyclic graph, cycle. Note: Also called a DAG or acyclic digraph. Also called an oriented acyclic graph. Paul E. Black, NIST, Dictionary of Algorithms, Data Structures and Problems, 2001 http://www.nist.gov/dads/HTML/directAcycGraph.html

The difference between a DAG and a hierarchy is that in the latter each child can only have one parent; a DAG allows a child to have more than one parent. A child term may be an "instance" of its parent term (is a relationship) or a component of its parent term (part- of relationship). A child term may have more than one parent term and may have a different class of relationship with its different parents. [Gene Ontology Consortium, General Documentation" 2001] http://www.geneontology.org/doc/GO.doc.html

Google = about 18,300 July 19, 2002; about 35,000 Oct. 2, 2004; about 352,000 Feb. 20, 2006

How does this differ from faceted classification?

domain ontology: A formal specification of the concepts and of the relationships among concepts that characterize an application area. Mark Musen, Design and Use of Clinical Ontologies: Curricular Goals for the Education of Health Telematics Professionals, Stanford Medical Informatics, 1999  http://smi-web.stanford.edu/pubs/SMI_Reports/SMI-1999-0767.pdf

Google = about 3,070 July 19, 2002; about 9,500 Oct. 22, 2004; about 123,000 Feb. 20, 2006 

drug ontology: A three-year project to develop a highly structured drug knowledge base. Unlike existing reference sources, it is intended solely for use by software applications. Computer Science, Univ. of Manchester, UK 2002 http://www.cs.man.ac.uk/mig/projects/old/drugontology/index.html

Google = about 409 Feb. 20, 2006

dynamic ontology: A shared ontology that adapts to an application domain and evolves with time as the concepts in that domain change. A dynamic ontology experimental prototype system has been designed and implemented, and applied to the problem of concept mining in the USC Brain Project. "Federating Neuroscience DB, Univ. of Southern California Brain Project, 2001 http://www-hbp.usc.edu/Projects/FederatedDBs.htm 1999

Google = about 148 July 19, 2002; about 767 Oct. 22, 2004; about 12,600 Feb. 20, 2006

formal ontology: A terminological ontology whose categories are distinguished by axioms and definitions stated in logic or in some computer-oriented language that could be automatically translated to logic. There is no restriction on the complexity of the logic that may be used to state the axioms and definitions. The distinction between terminological and formal ontologies is one of degree rather than kind. Formal ontologies tend to be smaller than terminological ontologies, but their axioms and definitions can support more complex inferences and computations. The two major contributors to the development of formal ontology are the philosophers Charles Sanders Peirce and Edmund Husserl. Examples of formal ontologies include theories in science and mathematics, the collections of rules and frames in an expert system, and specification of a database schema in SQL. John F> Sowa, Terminology of methods and techniques for defining, sharing, and merging ontologies, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

Google = about 148,000 Feb. 20, 2006

functional bioinformatics:  The emerging field of functional bioinformatics focuses on the development of ontologies or concept classifications fed into algorithms used to perform computations of the functions of biomolecules . "About bioinformatics" George Washington Univ. Medical Center, 2002 http://www.gwumc.edu/bioinformatics/about/bioinfo.htm

An emerging subfield of bioinformatics that is concerned with ontologies and algorithms for computing with biological function. Functional bioinformatics is the computational counterpart of functional genomics ...  is concerned with managing and analyzing functional genomics data, such as gene expression experiments and large- scale knock- out experiments. .. emphasizes large- scale computational problems, such as problems involving complete metabolic networks and genetic networks.  Peter D. Karp "An ontology for biological function based on molecular interactions" Bioinformatics Ontology 16 (3): 269- 285, 2000

Gene Ontology^TM (GO):  The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism. … GO terms are organized in structures called directed acyclic graphs (DAGs), which differ from hierarchies in that a child, or more specialized, term can have many parent, or less specialized, terms. http://www.geneontology.org/

A collaborative effort to address the need for consistent descriptions of gene products in different databases. The project began as a collaboration between three model organism databases: FlyBase (Drosophila), the Saccharomyces Genome Database (SGD) and the Mouse Genome Database (MGD) in 1998. Since then, the GO Consortium has grown to include many databases, including several of the world's major repositories for plant, animal and microbial genomes. An introduction to the Gene Ontology http://www.geneontology.org/GO.doc.shtml

Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium (2000) Nature Genet. 25: 25-29 http://www.geneontology.org/GO_nature_genetics_2000.pdf
Gene Ontology Faq-O-Matic Gene Ontology Consortium
GO Term definitions, Gene Ontology Consortium ^TM http://www.geneontology.org/ontology/GO.defs

Gene Ontology Annotation Project: Gene Ontology controlled vocabulary will be applied to a non- redundant set of proteins described in the Swiss- Prot, TrEMBL and Ensembl databases that collectively provide complete proteomes for Homo sapiens and other organisms.  European Bioinformatics Institute, UK http://www.ebi.ac.uk/GOA/ add GOA to term

global ontologies: A major problem in existing organizations … the inconsistent usage of terminology due to the existence of different vocabularies that are independently created and used by different groups for different purposes. Such a lack of globally agreed terminology is the main source of difficulties for effective and efficient communication within the organization, affecting the communication both among persons and computers. ``In theory, a good solution to this problem would be to adopt a single global vocabulary that is widely accepted and embraced by everyone in the organization. However, for large organizations such as The Boeing Company, this remains a Holy Grail [...] because people will always disagree about what terms to use and how to define them [...] [d]ifferent communities of practice use the same terms with quite different meanings. Where disagreements arise, negotiating positions and coming up with agreement is notoriously difficult.'' Subsequently, the author explores the possibility of having either many local ontologies directly and point to point mapped one into another, or multiple local ontologies along with a global reference ontology which could be used for the mapping. Manuela Viezzer
Creating, integrating and maintaining global and local ontologies, in Ontologies and Problem-solving methods & Ontology learning, Univ of Birmingham, UK, Aug 31, 2000 http://www.cs.bham.ac.uk/~mxv/publications/onto_engineering/node4.html

Google = about 456 Feb. 20, 2006

GO slims: Cut-down versions of the Gene Ontology [GO] ontologies containing a subset of the terms in the whole GO. They give a broad overview of the ontology content without the detail of the specific fine-grained terms… Are particularly useful for giving a summary of the results of GO annotation of a genome, microarray, or cDNA collection when broad classification of gene product function is required without the detail of the specific fine grained terms.

heavyweight ontologies: Heavyweight ontologies, by contrast [to lightweight], contain class hierarchies, constraints, and inference rules. It takes a long time and many resources to develop and maintain them and it is uncertain if there will be a benefit from this extra effort. Resource Description Framework (RDF) and Web Ontology Language (OWL) of the World-Wide Web Consortium (W3C) are technologies designed to model heavyweight ontologies. … The terms ‘lightweight’ and ‘heavyweight’ ontologies were introduced by Rudi Studer, University of Karlsruhe, Germany. Topic Maps are Emerging: Why Should I Care?  H. Holger Rath, http://www.idealliance.org/papers/dx_xmle04/papers/03-01-03/03-01-03.html 

Google = about 21 July 19, 2002; about 60 Oct. 22, 2004; about 70 May 2, 2005; about 169 Feb. 20, 2006

hierarchy: A partial ordering of entities according to some relation. A type hierarchy is a partial ordering of concept types by the type-subtype relation. In lexicography, the type-subtype relation is sometimes called the hypernym-hyponym relation. A meronomy is a partial ordering of concept types by the part-whole relation. Classification systems sometimes use a broader-narrower hierarchy, which mixes the type and part hierarchies: a type A is considered narrower than B if A is subtype of B or any instance of A is a part of some instance of B. For example, Cat and Tail are both narrower than Animal, since Cat is a subtype of Animal and a tail is a part of an animal. A broader-narrower hierarchy may be useful for information retrieval, but the two kinds of relations should be distinguished in a knowledge base because they have different implications. John F. Sowa, Terminology of methods and techniques for defining, sharing, and merging ontologies, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

HL7: Health Level Seven is one of several American National Standards Institute (ANSI) -accredited Standards Developing Organizations (SDOs) operating in the healthcare arena. … Health Level Seven’s domain is clinical and administrative data. "Level Seven" refers to the highest level of the International Organization for Standardization (ISO) communications model for Open Systems Interconnection (OSI) - the application level. http://www.hl7.org/

Human Ontology Resources: SOFG Standards and Ontologies for Functional Genomics, http://www.sofg.org/resources/human.html#cbil 

lightweight ontologies: Topic maps are seen as lightweight ontologies because they are able to model knowledge in a very ‘shallow’ way (e.g. just topics, their classes, occurrences, and associations, but no class hierarchies, constraints, or inference rules). Even ‘shallow’ topic maps are already very useful without having put large investments in their creation. Topic Maps are Emerging: Why Should I Care?  H. Holger Rath, http://www.idealliance.org/papers/dx_xmle04/papers/03-01-03/03-01-03.html 

Google = about 154 July 19, 2002; about 287 Oct. 22, 2004; about 274 May 2, 2005; about 570 Feb. 20, 2006

Google = about 11,000 Feb. 20, 2006

logic based ontologies: Description logic ontologies differ in their approach to construction. Rather than manually create a hierarchy and then assign properties to concepts, the process is turned on its head. Each concept is assigned a logic definition which is then used to derive a classification. There is more than one way to classify a set of concepts. This approach allows different classifications to be produced for different purposes based on the same underlying terminological knowledge. Description logic based ontologies can be useful because they provide 1. scalability… 2. Extendability … 3. Explicitness… Building DAML + OIL Ontologies, OilEd, Univ of Manchester, UK, 2002 http://oiled.man.ac.uk/building/

Google = about 23 July 19, 2002; about 71 July 14, 2004; about 135 Feb. 20, 2006

LOINC Logical Observation Identifiers Names and Codes: The purpose of the LOINC database is to facilitate the exchange and pooling of results, such as blood hemoglobin, serum potassium, or vital signs, for clinical care, outcomes management, and research. Regenstrief Institute Inc. Germany http://www.loinc.org/

Google = "lower ontologies" about 62, Aug 8, 2002; about 182 Feb. 20, 2006
"lower level ontologies" about 134 Aug. 8, 2002; about 149 Feb. 2, 2006

MedDRA Medical Dictionary for Regulatory Activities: Developed by the International Conference on Harmonisation (ICH) and is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) acting as trustee for the ICH steering committee. http://www.meddramsso.com/NewWeb2003/index.htm 

metadata: The accepted definition of meta-data is "data about data" [5]. However, it still seems that most people use the word in different and incompatible meanings, causing many misunderstandings. In the course of implementing meta-data in e-learning applications, we have encountered objections of varying kinds to the concept of meta-data and its use. It seems to us that many of those objections stem from what we regard as misconceptions about the very nature of metadata. Mikael Nilsson, Matthias Palmér, Ambjörn Naeve, Semantic Web Metadata for e-Learning - Some Architectural Guidelines, Worldwide Web Conference, Hawaii, 2002 . http://www2002.org/CDROM/alternate/744/   more on ontologies: Information management & interpretation glossary

MGED Network, Ontology Working Group: The primary purpose of the MGED Ontology is to provide standard terms for the annotation of microarray experiments. These terms will enable structure queries of elements of the experiments. Furthermore, the terms will also enable unambiguous descriptions of how the experiment was performed. The terms will be provided in the form of an ontology which means that the terms will be organized into classes with properties and will be defined. A standard ontology format will be used. http://mged.sourceforge.net/ontologies/index.php

MIAME/MAGE-OM:  The boundaries between MIAME concepts, the MIAME- compliant MAGE-OM and the MGED ontology (that try to define and structure the MIAME concepts) is neither well defined nor easy to understand. In order to provide some help, this webpage contains explanatory documentation to understand the MIAME concepts, how its requirements map to the MAGE-OM and where the MGED ontology inclusion is required. [MGED, MIAME MAGE-OM, 2002] http://www.mged.org/Workgroups/MIAME/miame_mage-om.html

micro-theories: An ontology about a specific domain, that fits within, and for the most part is consistent with, an ontology with a broader scope. For example, structural biology fits within the larger context of biology. Structural biology will have its own terminology and specific algorithms that apply within the specific domain, but may not be useful or identical to, for example, the genome community. Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary >

Google = about 953 July 19, 2002; about 8,670 Oct. 22, 2004; about 18,600 Feb. 20, 2006

middle ontologies: Approach to design support as proposed in this paper, assumes that designers describe a problem rather in 'upper' and middle- level ontologies in the beginning. Later when the problem is better understood 'lower' ontologies are applied.  These may exist in a repository (built in the past), or may be created on top of existing ontologies. A lower ontology from one case can serve as an upper or middle- level one in the next one. [M. Czbor "Support for Problem Formalisation in Engineering Design" 10th International DAAAM Symposium, Vienna Univ. of Technology, Austria, 21- 23 Oct. 1999] http://kmi.open.ac.uk/people/dzbor/public/1999/DAAAM99.PDF

Google = "middle level ontologies" about 37; Aug. 8, 2002; about 29 Feb. 20, 2006
"middle ontologies" about 9 Aug. 8, 2002; about 85 Feb. 20, 2006

mixed ontologies: Generally of more practical use [than pure or orthogonal ontologies], but can easily overlap with each other. The overlaps can be managed through the elements that make up the mixed ontologies coming from pure ontologies. Matthew West, Integration of Industrial Data for Exchange, Access and Sharing, European PDT Days, 1997 http://www.matthew-west.org.uk/Documents/IntegrationAndSharingOfIndustrialData.PDF 

An ontology in which some subtypes are distinguished by axioms and definitions, but other subtypes are distinguished by prototypes. The top levels of a mixed ontology would normally be distinguished by formal definitions, but some of the lower branches might be distinguished by prototypes. Terminology of methods and techniques for defining, sharing, and merging ontologies, John F. Sowa, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

Google = about 13 July 19, 2002; about 55 Oct. 22, 2004; about 113 Feb. 20, 2006

National Center for Biomedical Ontology: http://www.bioontology.org/index.html 

natural language ontologies: Hand crafted, flexible but difficult to evolve, maintain and keep consistent, with weak semantics. Example Gene Ontology [Robert Stevens' slides, Univ. of Manchester, UK at Synopsis of the Bio-Ontologies Workshop at the EBI for MGED, Dec. 5, 2001] http://www.cbil.upenn.edu/Ontology/EBI_Bioontologies_Workshop.html

Google = about 69 July 19, 2002; about 96 Oct. 22, 2004; about 143 Feb. 20, 2006

natural language processing: The newly emergent interest in natural language processing for biology has been christened "Information Extraction". But work in this area has been going on for many decades under different names and this site includes a good deal of information about past and current work in NLP and in information extraction for biology in particular. The other major descriptor of the general field is "Computational Linguistics". BIONLP.org, Bob Futrelle, Computer Science, Northeastern Univ., US, updated 2005 http://www.ccs.neu.edu/home/futrelle/bionlp/

Google = about 166,000 July 19, 2002; about 471,000 Oct. 22, 2004; about 1,870, 000 March 6, 2006

navigational ontology: http://www.nlm.nih.gov/research/visible/vhpconf2000/AUTHORS/WACHOLDE/TEXTINDX.HTM 

In our navigational ontology, we take advantage of these associative relationships [meaningful relationships between anatomical concepts also hold for the links between images associated with these concepts] by linking 3D anatomical images with a piece of an ontology displayed in a graph which indicates the relationship of other anatomical concepts to the focus concept. Judith Venuti, INTRODUCTION TO THE VESALIUS PROJECT'S DEMONSTRATION NAVIGATIONAL ONTOLOGY, Anatomy Dept. Columbia Univ. US, 1999 http://cpmcnet.columbia.edu/vesalius/kb/demo/introduction.html

Google = about 26 July 19, 2002; about 15 Oct. 22, 2004; about 87 Feb. 20, 2006; about 83 June 14, 2007

NeuroCommons: The NeuroCommons project is creating an Open Source knowledge management platform for biological research. The first phase, a pilot project to organize and structure knowledge by applying text mining and natural language processing to open biomedical abstracts, was released to alpha testers in February 2007. The second phase is the development of a data analysis software system. http://sciencecommons.org/projects/data/ 

object based ontologies: Extensively used, good structuring, intuitive. Semantics defined by OKBC standard, Examples: EcoCyc (uses Ocelot) and RiboWeb (uses Ontolingua). [Robert Stevens' slides, Univ. of Manchester, UK at Synopsis of the Bio- Ontologies Workshop at the EBI for MGED, Dec. 5, 2001] http://www.cbil.upenn.edu/Ontology/EBI_Bioontologies_Workshop.html

Office of the National Coordinator for Health Information Technology (ONC): Provides leadership for the development and nationwide implementation of an interoperable health information technology infrastructure to improve the quality and efficiency of health care and the ability of consumers to manage their care and safety. http://www.hhs.gov/healthit/

OIL Ontology Inference Layer: A proposal for a web- based representation and inference layer for ontologies, which combines the widely used modelling primitives from frame- based languages with the formal semantics and reasoning services provided by description logics. It is compatible with RDF Schema (RDFS), and includes a precise semantics for describing term meanings (and thus also for describing implied information). http://www.ontoknowledge.org/oil/

ontological commitment: An agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology. We build agents that commit to ontologies. We design ontologies so we can share knowledge with and among these agents. [Tom Gruber, What is an ontology?" Knowledge Systems Lab, Stanford Univ. 2001] http://www-ksl.stanford.edu/kst/what-is-an-ontology.html

Google = about 2, 370 July 19, 2002; about 5,980 Oct. 22, 2004

ontology, ontologies:  A means of capturing knowledge about a domain, such that it can be used both by humans and computers. The most import aspect of ontology is that it creates a shared understanding of a domain; for both people and computers. The knowledge is captured in conceptual form; that is, concepts that represent classes or sets of instances in the world. Ontologies relate concepts to one another through relationships, which may have constraints placed upon them. Robert Stevens, Bio-Ontology Page, 2001 http://www.cs.man.ac.uk/~stevensr/ontology.html

A formal explicit specification of a shared conceptualization. In this context conceptualization refers to an abstract model of some phenomenon in the world that identifies that phenomenon's relevant concepts. Explicit means that the type of concepts used and the constraints on their use are explicitly defined, and formal means that the ontology should be machine understandable. ... Shared reflects the notion that an ontology captures consensual knowledge- that is, it is not restricted to some individual but is accepted by a group. Dieter Fensel et. al "OIL: An Ontology Infrastructure for the Semantic Web" IEEE Intelligent Systems, Mar/Apr. 2001 www.cs.vu.nl/~frankh/postscript/IEEE-IS01.pdf

A consensual, shared and formal description of the concepts that are important in a given domain and their properties (attributes) and relations between them, i.e., it is a conceptual knowledge model or a specification of a conceptualisation. Property constraints, facts, assertions, axioms and rules are also part of an ontology. Typically, an ontology identifies classes or categories of objects that are important in a domain, and organises these classes in a subclass- hierarchy. Each class is characterised by properties that are shared/ inherited by all elements in that class. This structure might look like a simple taxonomy, but the real power of ontologies depends on the presence of inference and deduction rules, and reasoning and classification services. Kamel Boulos et. al. 'Towards a Semantic Medical Web: HealthCyberMap's Dublin Core Ontology in Protege, 2000 http://protege.stanford.edu/ontologies/dublincore/hcm_dc_in_protege_newcastle.pdf

The word "ontology" seems to generate a lot of controversy in discussions about AI  [artificial intelligence]. It has a long history in philosophy, in which it refers to the subject of existence. ... In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set- of- concept- definitions, but more general. And it is certainly a different sense of the word than its use in philosophy. What is important is what an ontology is for. My colleagues and I have been designing ontologies for the purpose of enabling knowledge sharing and reuse. In that context, an ontology is a specification used for making ontological commitments. ... Notes: 1) Ontologies are often equated with taxonomic hierarchies of classes, but class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Tom Gruber, Stanford Univ. "What is an ontology?" 2001 http://www-ksl.stanford.edu/kst/what-is-an-ontology.html    

Related terms: interoperability, metadata, OIL Ontology Inference Layer, ontological commitment, ontology annotation tools, ontology editors, ontology evolution, ontology interoperability, RDF, semantic web, web ontology language; Microarrays glossary Ontology Working Group 

Ontology [computer science], Wikipedia http://en.wikipedia.org/wiki/Ontology_(computer_science)

ontology integration: http://www.meteck.org/sumOntDevBio.html

ontology specification languages: Traditional ontology specification languages are Ontolingua, CycL, languages based on description logics such as LOOM, and frame-based languages. More recently, languages have been developed that are based on Web standards such as XOL, SHOE or UPML. One of the most recent is OIL. Manuela Viezzer, Ontologies and Problem-solving methods & Ontology learning, Univ of Birmingham, UK, Aug 31, 2000 http://www.cs.bham.ac.uk/~mxv/publications/onto_engineering/node4.html

Ontology Working Group: Charged with developing an ontology for describing samples used in microarray experiments. MGED Network, Ontology Working Group   http://mged.sourceforge.net/ontologies/index.php

Open Biomedical Ontologies: An umbrella web address for well- structured controlled vocabularies for shared use across different biological and medical domains. http://www.bioontology.org/resources-obo.html

Protégé: A national resource for biomedical ontologies and knowledge bases. A core component of The National Center for Biomedical Ontology http://protege.stanford.edu/

Protege Ontologies Library http://protege.cim3.net/cgi-bin/wiki.pl?ProtegeOntologiesLibrary 

prototype-based ontology: A terminological ontology whose categories are distinguished by typical instances or prototypes rather than by axioms and definitions in logic. For every category c in a prototype-based ontology, there must be a prototype p and a measure of semantic distance d (x,y,c), which computes the dissimilarity between two entities x and y when they are considered instances of c. Terminology of methods and techniques for defining, sharing, and merging ontologies, John F. Sowa, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

pure ontologies: The basis for classification is the same throughout the classification hierarchy. Such ontologies can be expected to be orthogonal… Pure ontologies tend to be concise. … pure ontologies can be a useful tool for managing the mapping between data models. Matthew West, Shell Information Services Ltd, UK, Integration and Sharing of Industrial Data, European PDT Days 1997 http://www.matthew-west.org.uk/Documents/IntegrationAndSharingOfIndustrialData.PDF

Google = about 23 Feb. 20, 2006

RDF Resource Description Framework:  Integrates a variety of applications from library catalogs and world- wide directories to syndication and aggregation of news, software, and content to personal collections of music, photos, and events using XML as an interchange syntax. The RDF specifications provide a lightweight ontology system to support the exchange of knowledge on the Web.  W3C Semantic Web Activity, accessed May 5, 2005 http://www.w3.org/RDF/  

Science Commons: Science Commons serves the advancement of science by removing unnecessary legal and technical barriers to scientific collaboration and innovation. Built on the promise of Open Access to scholarly literature and data, Science Commons identifies and eases key barriers to the movement of information, tools and data through the scientific research cycle. http://sciencecommons.org/ 

Google = about 98,000 March 6, 2006; about 120,000 June 18, 2007

semantic heterogeneity: Different agents use the same word to mean different things, use different granularity to describe the same domain, describe a domain from a different perspective, and so on. All together, this is what researchers call semantic heterogeneity, namely a situation in which agents do not understand each others as they use languages with heterogeneous semantic. http://sra.itc.it/people/serafini/distribution/aaai-ws-ctxml.pdf

semantic mining: The aim of the Network of Excellence entitled Semantic Interoperability and Data Mining in Biomedicine (NoE 507505) is to establish Europe as the international scientific leader in medical and biomedical informatics. The long-term goal of the network will be the development of generic methods and tools supporting the critical tasks of the field; data mining, knowledge discovery, knowledge representation, abstraction and indexing of information, semantic-based information retrieval in a complex and high-dimensional information space, and knowledge-based adaptive systems for provision of decision support for dissemination of evidence based medicine. Semantic Mining, Linköpings universitet, Sweden http://www.semanticmining.org/

Sequence Ontology Project: http://www.bioontology.org/resources-obo.html

semantic web: The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications. In order to make this vision a reality for the Web, supporting standards, technologies and policies must be designed to enable machines to make more sense of the Web, with the result of making the Web more useful for humans.

Facilities and technologies to put machine- understandable data on the Web are rapidly becoming a high priority for many communities. For the Web to scale, programs must be able to share and process data even when these programs have been designed totally independently. The Web can reach its full potential only if it becomes a place where data can be shared and processed by automated tools as well as by people. W3C, Semantic Web Activity Statement, 2001 http://www.w3.org/2001/sw/Activity

The first layer of the semantic Web consists of ontologies and taxonomies, like "A machine bolt is a type of screw." "A huge amount of this is being done very desperately in the realm of biotech, for the human genome and new drug development. When you look at a Web services description, you realize that it's really just a very small ontology" Tim Berners Lee, August 30, 2001 keynote at Software Development East in Boston. [Alexandra Weber Morales "Web founder seeks simplicity" Show Daily Online, 2001] http://www.sdgnews.com/sd2001es_006/sd2001es_006.htm

Google = about 71,600 July 19, 2002; about 967,000 Oct. 22, 2004

Semantic Web Business Special Interest Group: http://business.semanticweb.org/
Semantic web Challenge: http://challenge.semanticweb.org/
Semantic Web Community Portal http://www.semanticweb.org/
Semantic web Healthcare and Life Sciences Interest Group http://www.w3.org/2001/sw/hcls/

SNOMED Systematized Nomenclature of Medicine: Terminology and implementation support products and services. College of American Pathologists http://www.snomed.org/

SOFG: Standards and ontologies for functional genomics, http://www.sofg.org

term extraction: Robert Futrelle, Northeastern Univ., 2001 http://www.ccs.neu.edu/home/futrelle/bionlp/psb2001/psb01-tutorial-bib1.htm

terminological ontology: An ontology whose categories need not be fully specified by axioms and definitions. An example of a terminological ontology is WordNet, whose categories are partially specified by relations such as subtype-supertype or part-whole, which determine the relative positions of the concepts with respect to one another but do not completely define them. Most fields of science, engineering, business, and law have evolved systems of terminology or nomenclature for naming, classifying, and standardizing their concepts. Axiomatizing all the concepts in any such field is a Herculean task, but subsets of the terminology can be used as starting points for formalization. Unfortunately, the axioms developed from different starting points are often incompatible with one another. Terminology of methods and techniques for defining, sharing, and merging ontologies, John F. Sowa, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

Google = about 257 Feb. 20, 2006

topic maps: The first category of topic maps applications brings the users in ‘direct contact’ with a topic map. Typically, such a topic map models a subject classification, taxonomy, thesaurus, or – most general – an ontology. The only or main purpose of these topic maps applications is the explicit creation, maintenance, and usage of the ontologies. They are not hidden by business logic from the users. The ontology and its components like topics, classes, associations, occurrences, and scope are the business objects. Topic Maps are Emerging: Why Should I Care?  H. Holger Rath, http://www.idealliance.org/papers/dx_xmle04/papers/03-01-03/03-01-03.html 

UMLS Unified Medical Language System, National Library of Medicine, US http://www.nlm.nih.gov/research/umls

upper ontology: An upper ontology is limited to concepts that are meta, generic, abstract and philosophical, and therefore are general enough to address (at a high level) a broad range of domain areas. IEEE, Standard Upper Ontology (SUO) Working Group, 2003 http://suo.ieee.org/

upper ontology [computer science], Wikipedia http://en.wikipedia.org/wiki/Upper_ontology_%28computer_science%29

Google = about 82,900 Feb. 20, 2006

web ontology language: What is an ontology?, W3C, Requirements for a web ontology language, [work in progress] http://www.w3.org/TR/webont-req/#onto-def

Bibliography
Glossary of Ontology Terminology, KSL Network Services, Stanford Univ., US, 2001, 20+ terms. http://www-ksl-svc.stanford.edu:5915/doc/frame-editor/glossary-of-terms.html

Terminology of methods and techniques for defining, sharing, and merging ontologies, John F. Sowa, 18 definitions, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm

Bibliography
Alpha glossary index
How to look for other unfamiliar  terms