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Pharmaceutical ontologies & taxonomies glossary & taxonomy
Evolving Terminologies for Emerging Technologies
Comments? Questions? Revisions?  Mary Chitty
Last revised December 17, 2014
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BioOntologies SIG

BioPax:  Biological Pathways Exchange.  A collaborative effort to create a data exchange format for biological pathway data.

BioPortal: Use BioPortal to access and share ontologies that are actively used in biomedical communities..

BioRoot Search A nifty ontologies search portal from NIST. 

bottom-up ontologies:
We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata, Paolo Ceravolo Ernesto Damiani, IEEE Transactions on Knowledge and Data Engineering  Marco Viviani DOI Bookmark:  

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   

bottom-up taxonomies:
Faceted classification is a hallmark of the bottom-up approach and suggests yet another reason why the phrase "build the taxonomy" is ill-conceived. ... The bottom-up approach suggests a very different way to classify content. When populating a top-down taxonomy, the central question is "where do I put this?" but at the heart of the bottom-up approach is the question "how do I describe this?" By asking this subtly different question, you’ll wind up in a dramatically different destination.  Peter Morville, "Bottoms up: Designing complex, adaptive systems, Faceted Classification, Dr. Dobbs, 2002

Can mean from specific to general, but it can also mean content- oriented. Jean Graef "Top down or bottom up" Montague Institute Review, 2001    

classification: Involves the development and use of a scheme for the systematic organization of knowledge. (Taylor p 576) Arlene Taylor identified three approaches to classification: enumerative, hierarchical, and analytico- synthetic. Enumerative classification attempts to assign headings for every subject and alphabetically enumerates them. Hierarchical classification uses a more philosophical approach based on the inherent organization of the subject being classified, and establishes logical rules for dividing topics into classes, divisions, and subdivisions. Analytico- synthetic classification assigns terms to individual concepts and provides rules for the local cataloger to use in constructing headings for composite subjects. Traditional classification systems in this country are basically enumerative, though many contain some elements of hierarchy and faceting. (Taylor pp 319- 321) Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE" Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995 

Indexing in the library and information management sense. See also classification, classifiers

classification: Can be done manually by human experts or automatically by software of many different types. However, the term as used in the microarray field has a more specific meaning: It always refers to automatic methods, and usually means automatic methods in which the classifier is built by adjusting parameters of a general model. These methods are sometimes called supervised computer- learning methods, in contrast to unsupervised methods, such as clustering. 

classifier:  A decision procedure that categorizes data into two or more predefined groups. Classifiers are also called predictors. Classifiers usually emit a score that can be interpreted as the likelihood that the data fall into a certain category, rather than just a binary yes/ no answer. In many applications it is necessary to convert this likelihood into a yes/ no answer, or perhaps a yes/ no/ maybe answer, typically through a simple thresholding scheme.  

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  
Related terms: ontological commitment, reusable ontologies, shared ontologies 

configurable: Information Management & Interpretation

controlled vocabularies: 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

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.     Broader terms: ontologytaxonomy Related terms: RDF, semantic web 
Controlled vocabularies Standards
, NISO ANSI/NISO Z39.19-2005

core ontologies:    Google = about 880 Feb. 20, 2006

data management vocabulary: A third type of taxonomy [in addition to search and navigational taxonomies] that is valuable in a business setting is the data management vocabulary. This taxonomy is a short list of authorized terms without any hierarchical structure that is used to support business transactions. For example, with a large sales force, it is most efficient if salespeople report their work using the same list of activities. They may count their contacts with companies according to a simple list of contact types (managers, decision-makers, and so on), and they may categorize the businesses they work with according to different controlled descriptors that have to do with the business's size or market. In this case, a shared taxonomy will help to support reporting needs of management and other salespeople trying to mine the information in the future. Without a shared taxonomy, a company risks developing islands of data that cannot be shared or easily utilized by the rest of the organization. Susan Conway and Char Sligar, "What is a taxonomy" Unlocking Knowledge Assets, Chapter 6, Building Taxonomies, Microsoft Press, 2002    Google = about 49 July 9, 2007; about 16,200 Nov 18, 2009 Related terms: descriptive taxonomies, navigational taxonomies

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

descriptive taxonomies: Supports information retrieval through searching. By developing and maintaining a core set of controlled vocabularies, a company can consistently label or tag its content with descriptive metadata selected from these authorized vocabularies. In addition, vocabularies can capture knowledge worker terminology and map it to a company’s preferred terms. ... Active mining of new terms and phrases from emerging content and from search query logs will help keep a descriptive taxonomy relevant to the users of that information. A taxonomy built on the thesaurus model (designating a preferred or authorized term with entry terms or variants) helps to link these different terms together. At search time, the term that the knowledge worker uses is associated with the preferred (or key) term for more precise searching, or the knowledge worker’s term is expanded to include the variant forms of the term as well as the authorized term for a broader search. Taxonomies built on the thesaurus model do not force all work groups to use a common set of terminology. Susan Conway and Char Sligar, "What is a taxonomy" Unlocking Knowledge Assets, Chapter 6, Building Taxonomies, Microsoft Press, 2002 Google = about 119 July 19, 2002; about 201 Oct. 22, 2004; about 456 July 9, 2007  Related terms: bottom-up taxonomies, data management vocabulary, navigational taxonomies, shared taxonomies

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

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 Annotations  
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 Domain ontology Google = about 409 Feb. 20, 2006, about 5,160 Oct 7, 2009; about 94,100 Nov 18, 2009  

domain taxonomies: The first step is to define the taxonomy of entities in the domain. This consists of firstly defining the basic classes, then defining the sub- types of these classes.  [Mick O'Donnell, Defining domain taxonomies" Domain Acquisition in Ilex 3.0, 1993-1996]

Google = about 166 July 19, 2002; about 276 Oct. 22, 2004 

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 1999

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

dynamic taxonomies: Developed as a way of sifting through large amounts of data. At its base it uses a domain specific taxonomic hierarchy, consisting of concepts connected by is- a relationships. Examples from the medical domain include UMLS and SNOMED. Concepts from the hierarchy are used to classify chunks of guidelines text. The hierarchy is then used as an augmented index for guidelines chunk retrieval. Navigation is done via the operations of browsing and zooming. [Dennis Wollersheim, Implementation of dynamic taxonomies for clinical guidelines retrieval, La Trobe Univ., Australia, c. 2001]

Google = about 119 July 19, 2002; about 369 Oct. 22, 2004  

facet: Ranganathan was the first to introduce the word "facet" into library and information science, and the first to consistently develop the theory of facet analysis. A facet is, simply put, a category. Taylor defines facets as "clearly defined, mutually exclusive, and collectively exhaustive aspects, properties, or characteristics of a class or specific subject." Ranganathan demonstrated that analysis, which is the process of breaking down subjects into their elemental concepts, and synthesis, the process of recombining those concepts into subject strings, could be applied to all subjects, and demonstrated that this process could be systematized. (Taylor pp 320- 321; Foskett p 390). The phrase "analytico- synthetic classification" derives from these two processes: analysis and synthesis.  Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE" Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995 

faceted classification: One of the most powerful, yet least understood methods of organizing information. Most folks, when thinking about organizing objects or information, immediately think of a hierarchical, or taxonomic, organization; a top- down structure, where you start with a number of broad categories that get ever more detailed, until you arrive at the object. In such structures, each object has a single home, and typically, one path to get there -- this is how things are organized in "the real world", where each item can only be in one place. Oftentimes, when thinking of organizing information, a hierarchy is where people begin (think Yahoo!).  Faceted classification, on the other hand, is a bottom- up scheme. Here, each object is tagged with a certain set of attributes and values (these are the facets), and the organization of these objects emerges from this classification, and how a user chooses to access them. ... Faceted classification allows for exploration directed by the user, where a large dataset is progressively filtered through the user's various choices, until arriving at a manageable set that meet the users' basic criteria. Instead of sifting through a pre- determined hierarchy, the items are organized on- the- fly, based on their inherent qualities. Peter Merholz "Innovation in classification" Sept. 23, 2001

The use of facets in information retrieval did not originate with Ranganathan. In the 18th century, a Frenchman named Condorcet devised what we would now call a faceted classification scheme for organizing information about objects or facts. (Whitrow) The Dewey Decimal Classification, first published in 1876, contained elements of facet analysis. Dewey recognized four facets common to all basic classes: bibliographic form, time, place, and general subjects (such as statistics or research) that at times are related to other subjects. (Foskett pp 176-7) Dewey provided for "number building" to combine two or more facets to express a complex subject. (Taylor p 320) The Universal Decimal Classification, based on the Dewey Decimal Classification and first published in 1905, was intended to be an international classification scheme. It also had elements of a faceted structure, and partly influenced Ranganathan's thinking. (Foskett p 349; Vickery pp 12- 14)  Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE" Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995 

faceted metadata: Composed of orthogonal [mutually independent] sets of categories. For example, in the domain of architectural images, some possible facets might be Materials (concrete, brick, wood, etc.), Styles (Baroque, Gothic, Ming, etc .... and so on. Jennifer English et. al "Flexible search and navigation using faceted metadata" 2002    Google = about 360 July 19, 2002; about 2,530 Oct. 22, 2004

folksonomy: An important aspect of a folksonomy is that is comprised of terms in a flat namespace: that is, there is no hierarchy, and no directly specified parent-‍child or sibling relationships between these terms. Folksonomies - Cooperative Classification and Communication Through Shared Metadata, Adam Mathes, Graduate School of Library  & Information Science, University of Illinois Urbana Champaign, 2004 

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  
Wikipedia     Google = about 148,000 Feb. 20, 2006

game ontologies: DrDC (Game) Ontologies for the Semantic Web, 2005 

game taxonomies: In the taxonomy system proposed here, some fundamental distinctions are drawn between game forms and functions based upon narrative, repetitive game play and simulation; computer games can be seen to manifest these three functional and formal aspects to differing degrees, depending upon the particular game or game genre. Beyond the boundaries of games played only via computers and consoles we identify further classification dimensions, from virtual to physical gaming, and from fictional to non-fictional gaming. This taxonomy has been developed within the Zero Game Studio of the Interactive Institute in Sweden. Game Taxonomies: A High Level Framework for Game Analysis and Design, Craig A. Lindley.  Gamasutra, October 3, 2003

Gene OntologyTM (GO):  Functional genomics

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   Google = about 456 Feb. 20, 2006, about 3,660 Oct 7, 2009  Related terms: local ontologies, semantic heterogeneity  
Merging global and specialized linguistic ontologies 2002 

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, 

Google = about 21 July 19, 2002; about 60 Oct. 22, 2004; about 70 May 2, 2005; about 169 Feb. 20, 2006  Related terms: lightweight ontologies, topic maps

heavyweight taxonomies, heavyweight taxonomy: 

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

integrated taxonomy: We developed a comprehensive help taxonomy by combining both user interface and help system attributes, ranging from help access interface, presentation, and supporting knowledge structure, to implementation. The taxonomy systematically identifies independent axes along which help can be categorized which in turn encloses a space of help categories in which to place currently existing help research, and identifies distinct help software architectural features which contrast pros and cons in different approaches to implement help systems. The taxonomy projects a vision of what help can be like if it is on a par with advances in user interface technology, and desirable design features of help system architectures which are in the progressive direction along with the user interface software tools.  [Piyawadee "Noi" Sukaviriya, An Integrated Taxonomy of Online Help Based on User Interface View, GVU, Georgia Institute of Technology, GIT-GVU-91-20] Google = about 85 July 19, 2002; about 353 Oct. 22, 2004

interoperability: Ability of a system or a product to work with other systems or products without special effort on the part of the customer. Interoperability is made possible by the implementation of standards. IEEE, Standards Glossary 2010

Enabling heterogeneous databases to function in an integrated way, sometimes refers to cross platform functionality and operability across relational, object- oriented, and non- standard types of databases. Google = about 1,080,000 July 19, 2002; about 2,380,000 Oct. 22, 2004; about 8,600,000 Nov 18, 2009 Related terms: metadata, ontology, taxonomies; Narrower terms: ontology interoperability, semantic interoperability, software interoperability

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, 

Google = about 154 July 19, 2002; about 287 Oct. 22, 2004; about 274 May 2, 2005; about 570 Feb. 20, 2006 Compare: heavyweight ontologies. Related term: RDF Resource Description Framework 

linked data: Linked Data is about using the Web to connect related data that wasn't previously linked, or using the Web to lower the barriers to linking data currently linked using other methods. More specifically, Wikipedia defines Linked Data as "a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF." 
Linked Data Glossary glossary/

logic based ontologies: Developing an error-free ontology is a difficult task. A common kind of error for an ontology is logical contradiction or incoherence. In this paper, we propose some approaches to measuring incoherence in DL-based ontologies. These measures give an ontology engineer important information for maintaining and evaluating ontologies.  Measuring Incoherence in Description Logic-based Ontologies,  Anthony Hunter and Guilin Qi. Paper  presented at  ISWC2007+ASWC200 

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   Google = about 23 July 19, 2002; about 71 July 14, 2004; about 135 Feb. 20, 2006, about 60,100 Oct 7, 2009

lower ontologies: See under middle ontologies  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

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 .   more on metadata

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]   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 
Related terms: lower ontologies, upper ontologies

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 

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  
Google = about 13 July 19, 2002; about 55 Oct. 22, 2004; about 113 Feb. 20, 2006
Related terms: local ontologies, pure ontologies

molecular taxonomy:  There has been a lack of uniform terminology for the precancerous and non- invasive lesions. Reasons for this lack relate in part to changing concepts about the biology of these lesions, subjective interpretation of criteria, heterogeneity of the neoplastic cell population, less than optimal interobserver reproducibility, and even changes in treatment. Very often descriptive terms applied to these lesions contain a mixture of diagnostic and prognostic meanings. Cancer Biomarkers Research Group, Meeting Summary Molecular Classifications for Precancerous Lesions, EDRN Working Group, Feb. 2001, Rockville MD Referenced in Classifying the precancers: A metadata approach BMC Medical Informatics and Decision Making Volume 3, Number 1, 1-9, DOI: 10.1186/1472-6947-3-8  "molecular taxonomy" Google = about 1,650 July 19, 2002; about 5,260 Oct. 22, 2004
"molecular taxonomies" Google = about 11 July 19, 2002; about 106, Oct. 22, 2004

National Center for Biomedical Ontology: The goal of the National Center for Biomedical Ontology is to support biomedical researchers in their knowledge-intensive work, by providing online tools and a Web portal enabling them to access, review, and integrate disparate ontological resources in all aspects of biomedical investigation and clinical practice. A major focus of our work involves the use of biomedical ontologies to aid in the management and analysis of data derived from complex experiments.

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]   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"., Bob Futrelle, Computer Science, Northeastern Univ., US, updated 2005  Google = about 166,000 July 19, 2002; about 471,000 Oct. 22, 2004; about 1,870, 000 March 6, 2006

navigational ontology: Designing a navigational ontology for browsing and accessing anatomical images, AMIA 2000   
Google = about 26 July 19, 2002; about 15 Oct. 22, 2004; about 87 Feb. 20, 2006; about 83 June 14, 2007, about 269 OCt 7, 2009

navigational taxonomies: Aimed at discovering information through browsing. Once again the taxonomy provides a controlled vocabulary, but rather than using it in the background for manipulating queries, you can display this taxonomy to knowledge workers to help them find the information they need. The navigational taxonomy consists of labels applied to categories of content based on knowledge workers’ mental models of how the information is organized. ... A navigational taxonomy is based on user behavior and not on content. As a result, the category labels may be organized differently from the concept- based descriptive taxonomy, and they also may contain words or phrases that would not meet the standards of a descriptive taxonomy. ...  navigational taxonomies are often specialized and unique to an instance of information presentation (a portal, a site, an intranet), and multiple content management systems do not typically reuse them as they would a descriptive taxonomy. Navigational taxonomies are therefore not governed by the same rules about which taxonomy terms can be changed.  Susan Conway and Char Sligar, "What is a taxonomy" Unlocking Knowledge Assets, Chapter 6, Building Taxonomies, Microsoft Press, 2002  
Google = about 21 July 19, 2002; about 27 Oct. 22, 2004; about 83 July 9, 2007; about 730 Nov 18, 2009

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  
Google = about 17,500 July 19, 2002

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

ontologies proteomics: Protein informatics

ontology engineering: Wikipedia 

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, 2007

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

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

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      Ontology Tom Gruber updated
Terminology of methods and techniques for defining, sharing, and merging ontologies,
John F. Sowa, 2001.18 definitions, including formal ontology, mixed ontology, prototype type ontology, terminological ontology.  
Human Ontology Resources
, SOFG Standards and Ontologies for Functional Genomics, 
Ontology (information science) Wikipedia  
What is an ontology? W3C, Requirements for a web ontology language, working in progress]

Google = ontology about 336,000 July 19, 2002; about 1,140,000 Oct. 1, 2003; about 1, 250,000 Oct. 22, 2004; about 8,750,000 Nov 18, 2009  Narrower terms: bottom- up ontologies, biomedical ontologies, common ontology, descriptive ontology, domain ontology, dynamic ontology, heavyweight ontologies, lightweight ontologies, logic based ontologies, micro- theories, middle ontologies, mixed ontologies, taxonomies, natural language ontologies, navigational ontology, object based ontologies, orthogonal ontologies, pure ontologies, reusable ontologies, shared ontologies, simple ontologies, structured ontology, top- down ontology, upper ontologies; 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  Ontology Working Group 

ontology alignment 

ontology annotation tools: Link unstructured and semistructured information sources with ontologies. [Dieter Fensel et. al "OIL: An Ontology Infrastructure for the Semantic Web" IEEE Intelligent Systems, Mar/Apr. 2001]

ontology chart 

ontology editors: Help human knowledge engineers build ontologies - they support the definition of concept hierarchies, the definition attributes for concepts, and the definition of axioms and constraints. They must provide graphical interfaces and conform to existing standards in Web- based software development. They enable the inspecting, browsing, codifying, and modifying of ontologies, and they support ontology development and maintenance tasks. [Dieter Fensel et. al "OIL: An Ontology Infrastructure for the Semantic Web" IEEE Intelligent Systems, Mar/Apr. 2001]    Google = about 314  July 19, 2002; about 873 Oct. 22, 2004 Related term: Computers & computing GUI Graphical User Interface  

ontology engineering 

ontology evolution:   3.2 Ontology evolution, W3C, Requirements for a web ontology language, work in progress] Google = about 234 July 19, 2002; about 886 Oct. 22, 2004

ontology integration: Marijke Keet, 2004

ontology interoperability: 3.3 Ontology interoperability,  W3C, Requirements for a web ontology language, work in progress  
Google = about 89 July 19, 2002; about 276 Oct. 1, 2003; about 284 Oct. 22, 2004 Broader term: interoperability

ontology language: An ontology must be encoded in some language. If one is using a simple ontology, few issues arise. However, if one is considering a more complex ontology, expressive power of a representation and reasoning language needs to be considered. As with any problem where a language is being chosen, it must be epistemologically adequate -- the language must be able to express the concepts in the domain. Deborah L. McGuinness, "Ontologies Come of Age". In Dieter Fensel, J im Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2002.

Open Biomedical Ontologies OBO: A collaborative experiment involving developers of science-based ontologies who are establishing a set of principles for ontology development with the goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain. 

ontology learning  Also ontology extraction, ontology acquisition

ontology specification languages:  The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web.  A roadmap to ontology specification languages, Oscar Corcho2 and Asunción Gómez-Pérez , Springer, 2000 

Ontology Working Group: Charged with developing an ontology for describing samples used in microarray experiments. MGED Network, Ontology Working Group

Open Biomedical Ontologies: The OBO Foundry is a collaborative experiment involving developers of science-based ontologies who are establishing a set of principles for ontology development with the goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain. 

orthogonal ontologies:  Disjoint, non-overlapping ontologies    Google = about 6 July 19, 2002; about 72 Oct. 22, 2004; about 836 Nov 18, 2009  Related term: pure ontologies. Compare mixed ontologies

orthogonal taxonomies: not everything falls into a simple hierarchical system of categories and subcategories. Orthogonal taxonomies allow design concerns to be separated.  Game Taxonomies: A High Level Framework for Game Analysis and Design, Craig A. Lindley.  Gamasutra, October 3, 2003 Google = about 24 July 19, 2002; about 45 Oct. 22, 2004; about 273 Nov 18, 2009

paraphrase problem: The situation that arises when the terminology used in the request is different from that used by the author. William A. Woods  Conceptual Indexing for Precision Content Retrieval  Google = about 153 July 19, 2002; about 211 Oct. 22, 2004

phylogenetic taxonomy: A system of naming only monophyletic groups of organisms. The hierarchical structure of the names devised by such a system, in principle, accurately reflects the evolutionary relationships of all the named groups of organisms. [Glossary, Natural History Museum, London, UK} Google = about 929 July 19, 2002 

physical ontology 

Protege Ontologies Library 

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

pure ontologies:  it therefore becomes straightforward to see “pure” ontologies rather as mathematical objects, namely as the domain of the semantic interpretation function under consideration. (Naturally, we shall ultimately have to devise a suitable and convenient computer representation for them, but this is an independent issue.) Dogma Project at VUB StarLab

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  Google = about 23 Feb. 20, 2006 Related term: orthogonal ontologies

RDF Resource Description Framework: The Resource Description Framework (RDF) is a language for representing information about resources in the World Wide Web. This Primer is designed to provide the reader with the basic knowledge required to effectively use RDF.   accessed Oct 7, 2009

reusable ontologies:  A key enabler for electronic Commerce, Richard Fikes, Knowledge Systems Lab, Stanford Univ.  Google = about 597 July 19, 2002; about 1,330 Oct. 1, 2003; about 778 Oct. 22, 2004 Related term: shared ontologies

reusable taxonomies: Metadata, Taxonomies and Content Reusabilities, Marcia Morante    
Google = about 5 July 19, 2002; about 8 Oct. 1, 2003; about 8 Oct. 22, 2004; about 8 June 22, 2007

Science Commons: Science Commons designs strategies and tools for faster, more efficient web-enabled scientific research. We identify unnecessary barriers to research, craft policy guidelines and legal agreements to lower those barriers, and develop technology to make research, data and materials easier to find and use.  Google = about 98,000 March 6, 2006; about 120,000 June 18, 2007, about 49,600,000 Oct 7, 2009

semantic data integration: Semantic data integration requires a shared understanding of the meaning of mathematical data. Until recently, math protocols provided no support for shared semantics beyond the meaning of the primitive data types and simply assumed that the communicating partners ``knew'' each other. An important task of the Computer Algebra community is to close this semantic gap. Several initiatives addressing this problem are underway (MP, OpenMath, MathBus) and we hope that more experience and a careful evaluation of the proposals will lead to a unifying solution. Olaf Bachmann, Hans Schönemann "A Proposal for Syntactic Data Integration for Math Protocols" Centre for Computer Algebra, Dept. of Mathematics, Univ. of Kaiserslautern, Germany  Google = about  214 July 19, 2002; about 1,530 Oct. 22, 2004; about 23,900 June 22, 2007

semantic grid:  As the Semantic Web is to the Web, so is the Semantic Grid to the Grid. Rather than orthogonal activities, we see the emerging semantic web infrastructure as an infrastructure for grid computing applications.  Google = about 190 July 19, 2002; about 5,470 Oct. 22, 2004; about 182,000 June 22, 2007 Related term: Computers & computing grid computing

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.

Semantic heterogeneity in document encoding systems is a serious obstacle to the interoperability required to create a critical mass of content for the electronic publishing industry. This is a problem which persists even after a common syntax (e.g. XML) has been adopted, and sometimes even when common vocabularies are used.  [Scholarly Technology Group, Brown Univ., US Jan 2002

Different databases use different controlled vocabularies, thesauri, taxonomies and/ or free text.  Google = about 2,820 July 19, 2002; about 6,080 Oct. 22, 2004; about 78,700 June 22, 2007  Contrast with: structural heterogeneity Related terms:  Bioinformatics databases, federated databases, integrated databases   Bioinformatics databases, federated databases, integrated databases 

semantic interoperability:   
Jeff Heflin, James Hendler, Semantic interoperability on the web, Extreme Markup Languages, 2000 Google = about 7,280 Apr. 24, 2003; about 18,300 Oct. 22, 2004; about 330,000 June 22, 2007  

semantic mining: The biomedical research community eagerly awaits the full integration of very large text collections, biological databases, ontologies and terminological resources. However, many challenges have yet to be met to achieve this ambitious goal. Significant advances have been made and many working systems for tasks ranging from entity recognition and simple relation extraction to structured event extraction have been deployed. International Symposium on Semantic Mining in Biomedicine Oct 2010 Hinxton UK  

semantic relationships: Denote concepts such as water, sea, and river, that are by definition permanent relationships; they arise from the definition of the subjects involved, and are not dependent on any particular document content. ... Foskett described three groups of semantic relationships: equivalence, hierarchical, and affinitive/associative. In equivalence relationships, more than one term denotes the same concept. These relationships are shown through cross- references in an alphabetical tool and through juxtaposition in a classified tool. Hierarchical relationships are of two kinds: genus/ species and whole/ part. These relationships are shown through hierarchies in classified tools and with Broader and Narrower Term codes in alphabetical tools. Foskett described several kinds of affinitive/ associative relationships; these relationships are denoted by Related Term codes. (Foskett pp 72- 78)  Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE" Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995   Related term: syntactic relationships

semantic transparency: Within the context of interoperable XML- based information processing, "semantic transparency" means that machines and humans are presented with information that is both unambiguous (having a precise, predictably interpreted meaning) and meaningfully correct (simultaneously satisfying a number of integrity constraints). Computer agents, in particular, must exchange well- defined data in order to calculate and pass along "the correct answer." Semantic transparency first requires that small information objects as well as large information objects built from smaller ones are formally specified at a detailed level in terms of their fundamental characteristics, relationships, and natural integrity constraints, such that validation tools can apply heuristics to test information correctness. Given unambiguous semantic specification, both computing agents and humans can verify that XML- encoded information is meaningful and trustworthy. Managing Names and Ontologies: An XML Registry and Repository, Robin Cover (OASIS)

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

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

Google = about 71,600 July 19, 2002; about 967,000 Oct. 22, 2004 Broader term: web Related terms: metadata, ontology, RDF, taxonomies, XML. Compare: syntax 

Semantic Web Business 
Semantic web Challenge: 
Semantic web Healthcare and Life Sciences Interest Group
Semantic web: Ontology 

semantics: How the information [in a data file] should be interpreted by others. "Challenges for Biomedical Informatics and Pharmacogenomics,  Altman RB, Klein TE, Annu Rev Pharmacol Toxicol. 2002; 42:113- 133.

shared ontologies: 3.1 Shared ontologies, W3C, Requirements for a web ontology language, work in progress
Google = about 1,090 July 19, 2002; about 2,450 Oct. 1, 2003; about 2,520 Oct. 22, 2004
Related term: reusable ontologies

shared taxonomies: Shared Taxonomies,, 2004    
Google = about 12 July 19, 2002; about 70 Oct. 22, 2004; about 86 May 2, 2005; about 217 June 22, 2007

soft ontology 

soft taxonomies: Fusion (or intelligent integration) of information takes place in an environment where the data may be of varying quality, and some may be incomplete or inconsistent. Combining metadata (and the associated data) is not possible without knowing (or learning) the mappings between their ontologies. Such mappings are likely to be soft, i.e. approximate — different sources arise from different designers with different world views. Acquisition of Soft Taxonomies for Intelligent Personal Hierarchies and the Soft Semantic Web T P Martin and B Azvine,  BT Technology Journal   Volume 21, Number 4 / October, 2003  113  DOI 10.1023/A:1027391706414

structural heterogeneity: Different databases use different fields, fieldnames and relationships between elements. This can also be a term in structural biology Google = about  2,210 July 19, 2002; about 9,340 Oct.. 22, 2004 Compare semantic heterogeneity Related term: metadata

syntactic heterogeneity: Semantic heterogeneity or semantic conflict is the main source of problems in multidatabase design.  Semantic heterogeneity in multidatabase systems: a review and a proposed meta-data structure, Wang, Te-Wei; Murphy, Kenneth E  Journal of Database Management, October 01, 2004 Google = about 114 July 19, 2002; about 243 Oct. 1, 2003; about 201 Oct. 22, 2004; about 227 May 9, 2005; about 5,080 Nov 18, 2009

syntactic relationships: Denote otherwise unrelated concepts that are brought together as composite subjects in the documents being indexed. These relationships are not permanent, but rather ad hoc. ...  Syntactic relationships are displayed according to the syntax of a normal sentence, either through the syntax of the subject string (in precoordinate indexing), or through devices such as facet indicators (in postcoordinate indexing). The result of not providing for the display of syntactic relationships in postcoordinate systems results in users not being able to distinguish between different contexts for the same term. ... recent research in information retrieval also supports the use of syntactic as well as semantic relationships.  Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE" Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995  Related term: semantic relationships

syntax: How information is structured in a data file. "Challenges for Biomedical Informatics and Pharmacogenomics,  Altman RB, Klein TE, Annu Rev Pharmacol Toxicol. 2002; 42:113- 133. Compare semantics   

tag cloud: Wikipedia

tags, tagging: Wikipedia

taxonomies, taxonomy:  Taxonomies define a world- view because they specify which characteristics that compose each item count as important and then they lay out the relationships that exist between those characteristics. Taxonomies are political, value- laden instruments of organization that have a wide- array of assumptions embedded within them. Along more formal lines, a taxonomy is a structured vocabulary that identifies a single key term to represent a concept that could be described using several words. [Katherine C. Adams "Immersed in Structure: The Meaning and Function of Taxonomies" Internetworking Aug. 2000]

Frustrations with search engines and information retrieval (and information overload) have led to increased interest in specialized taxonomies. A form of controlled vocabulary, with hierarchical relationships (broader terms, narrower terms) which provide additional suggestions for browsing, as do lateral relationships (related terms) and preferred terms. While there is theoretical interest in natural language processing, a very small percentage of web search engine queries actually use natural language processing successfully.

Directories such as Yahoo or the Open Directory Project are sometimes called taxonomies. In biology taxonomies are so associated with Linnaeus, and bioinformatics so dependent upon computers that ontology is almost always the preferred term in this context. See also FAQ question # 4 which has more about taxonomies.

Taxonomy Division, SLA  
Google taxonomy = about 617,000 July 19, 2002, about 3,270,000 Oct. 1, 2003, about 3,190,000 Oct. 22, 2004; about 28,300,000 Nov 18, 2009, about 38,000,000 Sept 10, 2010

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  
Google = about 257 Feb. 20, 2006  

thesaurus, thesauri: See under controlled vocabulary  Google = thesaurus about  2,760,000  thesauri  about 448,000 July 19, 2002; thesaurus about 6,270,000 Oct. 22, 2004 
NISO Z39.19 Standard for Structure and Organization of Information Retrieval Thesauri

top-down ontology: We spent the first six months attempting to design a top- down ontology of engineering. We accomplished very little until we selected a concrete system and example applications as contexts for our work. {Jay M. Tenenbaum Lessons from PACT and SHADE Enterprise Integration Technologies Corporation and Stanford University, 1995] Google = about 10 July 19, 2002; about 19 Oct. 22, 2004; about 7,630 Nov 18, 2009

top-down taxonomy: Goes from the general to the specific. Can also mean user oriented. Jean Graef "Top down or bottom up" Montague Institute Review, 2001 Google = about 16 July 19, 2002 about 90 June 17, 2003; about 79 Oct. 22, 2004 ; about 161,000 Nov 18, 2009

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, 

(XML) Topic Maps, XML Cover Pages , Robin Cover, 2002  Google = about 23,400 July 19, 2002

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

upper ontology [computer science], Wikipedia  Google = about 11,000 Oct. 22, 2004; = about 82,900 Feb. 20, 2006

web ontology language: What is an ontology?, W3C, Requirements for a web ontology language  accessed Oct 7, 2009
Requirements for a Web Ontology Language, working draft   
Google = about  736 July 19, 2002; about 19,600 Oct. 22, 2004; about 326,000 Nov 17, 2006

BioRoot Search A nifty ontologies search portal from NIST. 
Linked Data Glossary glossary/
Terminology of methods and techniques for defining, sharing, and merging ontologies, John F. Sowa, 18 definitions, 1997

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