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Glossaries & taxonomies Site Map BioOntologies SIG http://www.bio-ontologies.org.uk/ BioPax:
Biological Pathways Exchange. A
collaborative effort to create a data exchange format for biological
pathway data. http://www.biopax.org BioPortal: Use BioPortal to access and share ontologies that are actively used in biomedical communities.. http://bioportal.bioontology.org/ BioRoot
Search http://xpdb.nist.gov/bioroot/bioroot.pl
A nifty ontologies search portal from NIST. 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://www2.computer.org/portal/web/csdl/doi/10.1109/69.706054 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 http://www.ddj.com/architect/184411741 Can mean from specific to
general, but it can also mean content- oriented. Jean Graef "Top
down or bottom up" Montague Institute Review, 2001 http://www.montague.com/abstracts/topdown.html 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 http://library.music.indiana.edu/tech_s/mla/facacc.rev 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 http://www-ksl.stanford.edu/kst/what-is-an-ontology.html 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 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; about 496,000 Nov 18, 2009 Broader terms:
ontology, taxonomy Related terms: RDF, semantic web core ontologies:
http://www.loa-cnr.it/core_onto.html 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 http://www.microsoft.com/mspress/books/sampchap/5516a.aspx 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 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 http://www.microsoft.com/mspress/books/sampchap/5516a.aspx 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 Annotations http://www.arabidopsis.org/portals/genAnnotation/functional_annotation/go.jsp 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://med.stanford.edu/profiles/medicine/frdActionServlet?choiceId=showPublication&pubid=21408&fid=4164 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] http://www.hcrc.ed.ac.uk/ilex/Manual/extending/Domain-Acquisition/domacq/node4.html#S0....
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 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 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]
http://homepage.cs.latrobe.edu.au/lewisba/SPIRT/dw2001c.pdf 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 http://library.music.indiana.edu/tech_s/mla/facacc.rev 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 http://www.peterme.com/archives/00000063.html 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 http://library.music.indiana.edu/tech_s/mla/facacc.rev 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
http://bailando.sims.berkeley.edu/papers/chi02_short_paper.pdf 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 http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html 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 game ontologies: DrDC (Game) Ontologies for the Semantic Web,
2005 http://homepage.mac.com/micheal1/iblog/B1888672450/C1097851622/E20050813143420/index.html 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
http://www.gamasutra.com/features/20031003/lindley_01.shtml 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
http://www.cs.bham.ac.uk/~mxv/publications/onto_engineering/node4.html 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 heavyweight taxonomies,
heavyweight taxonomy: http://www2.computer.org/portal/web/csdl/doi/10.1109/ICCBSS.2006.32 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 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] http://www.cc.gatech.edu/gvu/reports/1991/abstracts/91-20.html interoperability:
The ability of two or
more systems or components to exchange information and to use the
information that has been exchanged. Institute of Electrical and
Electronics Engineers. IEEE Standard Computer Dictionary: A Compilation of
IEEE Standard Computer Glossaries. New York, NY: 1990 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.
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 lightweight
taxonomies: Existing ontologies vary in a continuum from
lightweight taxonomies (thesauri or conceptual vocabularies) to rigorous
formalizations. Manuela Viezzer, Ontologies and conceptual modeling,
2000-08-31]
http://www.cs.bham.ac.uk/~mxv/publications/onto_engineering/node1.html Google = about 5 July 19,
2002; about 4 Oct. 22, 2004
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."
http://linkeddata.org/ 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 http://oiled.semanticweb.org/building/ logic based taxonomies: http://www.ipacweb.org/conf/00/simpson.pdf lower ontologies: See
under middle ontologies 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 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] http://kmi.open.ac.uk/people/dzbor/public/1999/DAAAM99.PDF 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. 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
http://www.springerlink.com/content/2x6x4908206022vv/fulltext.pdf
"molecular
taxonomy" Google = about 1,650 July 19, 2002; about 5,260 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. http://www.bioontology.org/about-ncbo 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/ navigational ontology:
Designing a navigational ontology for browsing
and accessing anatomical images, AMIA 2000 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2243828 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
http://www.microsoft.com/mspress/books/sampchap/5516a.aspx 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 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 ontologies proteomics: Proteomics ontology
engineering: Wikipedia http://en.wikipedia.org/wiki/Ontology_(information_science)#Ontology_engineering 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 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 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 ontology
alignment http://en.wikipedia.org/wiki/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] www.cs.vu.nl/~frankh/postscript/IEEE-IS01.pdf ontology chart http://en.wikipedia.org/wiki/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] www.cs.vu.nl/~frankh/postscript/IEEE-IS01.pdf ontology engineering http://en.wikipedia.org/wiki/Ontology_engineering ontology
evolution: 3.2 Ontology evolution, W3C, Requirements
for a web ontology language, work in progress] http://www.w3.org/TR/webont-req/#goal-evolution ontology integration:
http://www.meteck.org/sumOntDevBio.html Marijke Keet, 2004 ontology
interoperability: 3.3 Ontology interoperability, W3C,
Requirements for a web ontology language, work in progress http://www.w3.org/TR/webont-req/#goal-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. http://www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-mit-press- 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. http://www.obofoundry.org/ ontology
learning http://en.wikipedia.org/wiki/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-Pérez2 , Springer,
2000 http://www.springerlink.com/content/qv8h33hqyb643y14/ 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: 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. http://www.obofoundry.org/ 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
http://www.gamasutra.com/features/20031003/lindley_01.shtml paraphrase problem:
The situation that arises when the terminology
used in the request is different from that used by the author. William A.
Woods, Sun Microsystems Research] http://research.sun.com/people/wwoods/ Conceptual Indexing for Precision Content
Retrieval http://research.sun.com/knowledge/ 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} http://www.nhm.ac.uk/hosted_sites/pe/2000_1/retinal/gloss.htm 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 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. http://www.w3.org/TR/rdf-primer/ accessed Oct 7, 2009 reusable ontologies: A key enabler for electronic Commerce, Richard
Fikes, Knowledge Systems Lab, Stanford Univ. http://ksl-web.stanford.edu/Reusable-ontol/index.html reusable taxonomies:
Metadata, Taxonomies and Content Reusabilities,
Marcia Morante http://adlcommunity.net//file.php/11/Documents/Eedo_Knowledgeware_Metadata_Taxonomies_and_Content_Reusability.pdf
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. http://sciencecommons.org/ 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 http://www.mathematik.uni-kl.de/~zca/Reports_on_ca/10/paper_html/node1.html 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. http://www.semanticgrid.org/ 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 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 http://www.stg.brown.edu/news/2002/nist_report.html
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 semantic
interoperability: http://en.wikipedia.org/wiki/Semantic_interoperability 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 http://www.smbm.eu/call-for-papers-2 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
http://library.music.indiana.edu/tech_s/mla/facacc.rev
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)
http://www.sun.com/981201/xml/ 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
http://www.w3.org/DesignIssues/Business 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. http://www.ncbi.nlm.nih.gov/pubmed/11807167 shared
ontologies: 3.1
Shared ontologies, W3C, Requirements for a web ontology language, work in
progress http://www.w3.org/TR/webont-req/#goal-shared-ontologies shared taxonomies:
Shared Taxonomies,
LouisRosenfeld.com, 2004 http://www.louisrosenfeld.com/home/bloug_archive/000276.html soft ontology http://en.wikipedia.org/wiki/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 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 http://www.accessmylibrary.com/article-1G1-122161922/semantic-heterogeneity-multidatabase-systems.html 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 http://library.music.indiana.edu/tech_s/mla/facacc.rev 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. http://www.ncbi.nlm.nih.gov/pubmed/11807167 tag cloud: Wikipedia http://en.wikipedia.org/wiki/Tag_cloud tags,
tagging: Wikipedia http://en.wikipedia.org/wiki/Tag_(metadata) 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] http://www.internettg.org/newsletter/aug00/article_structure.html 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. Wikipedia
http://en.wikipedia.org/wiki/Taxonomy 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 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 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] http://tools.org/EI/ICEIMT/archive/abstracts/PACT-SHADE.abstract 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 http://www.montague.com/abstracts/topdown.html topic maps: http://en.wikipedia.org/wiki/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 (XML)
Topic Maps, XML
Cover Pages , Robin Cover, 2002 http://xml.coverpages.org/topicMaps.html 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 web ontology language: What is an ontology?, W3C, Requirements for a
web ontology language http://www.w3.org/TR/webont-req/#onto-def accessed Oct 7, 2009 Bibliography |
