BioOntologies SIG https://www.biomedcentral.com/collections/sig
Ontologies: Necessary but not sufficient https://www.slideshare.net/robertstevens65/ontologies-necessary-but-not-sufficient
BioPax: Biological
Pathways Exchange. a
standard language that aims to enable integration, exchange,
visualization and analysis of biological pathway data.
Specifically, BioPAX supports data exchange between pathway data
groups and thus reduces the complexity of interchange between data
formats by providing an accepted standard format for pathway data.
It is an open and collaborative effort by the community of
researchers, software developers, and institutions. BioPAX is
defined in OWL
DL and
is represented in the RDF/XML format. BioPAX
Paper was
published in Nature Biotechnology in 2010 http://www.biopax.org
BioPortal: 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://bioportal.bioontology.org/
bottom-up ontologies: See
under top down and bottom up ontologies
bottom-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
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
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.
Indexing in the library and information management sense.
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, Towards Principles
for the Design of Ontologies http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.5775&rep=rep1&type=pdf
controlled vocabularies: ANSI/NISO
Z39.19-2005 (R2010) Guidelines for the Construction, Format, and
Management of Monolingual Controlled Vocabularies Presents
guidelines and conventions for the contents, display,
construction, testing, maintenance, and management of
monolingual controlled vocabularies. It focuses on controlled
vocabularies that are used for the representation of content
objects in knowledge organization systems including lists, synonym
rings, taxonomies, and thesauri. http://www.niso.org/publications/ansiniso-z3919-2005-r2010 .
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?: ontology, taxonomy
Related terms: RDF, semantic web
core ontology: https://en.wikipedia.org/wiki/Core_ontology
data integration, ontology based: Ontology-based
data integration involves
the use of ontology(s)
to effectively combine data or information from multiple
heterogeneous sources.[1] It
is one of the multiple data
integration approaches
and may be classified as Global-As-View (GAV).[2] The
effectiveness of ontology based data integration is closely tied
to the consistency and expressivity of the ontology used in the
integration process. Wikipedia https://en.wikipedia.org/wiki/Ontology-based_data_integration
Related term: semantic heterogeneity
data munging: is
basically the hip term for cleaning
up a messy data set. Origin:
munge /muhnj/
vt. 1. [derogatory] To imperfectly transform information. 2.
A comprehensive rewrite of a routine, data structure or the whole
program. 3. To modify data in some way the speaker doesn't need to
go into right now or cannot describe succinctly (compare mumble).
4. To add spamblock to
an email address. This term is often confused with mung,
which probably was derived from it. However, it also appears the
word `munge' was in common use in Scotland in the 1940s, and in
Yorkshire in the 1950s, as a verb, meaning to munch up into a
masticated mess, and as a noun, meaning the result of munging
something up (the parallel with the kluge/kludge pair
is amusing). The OED reports `munge' as an archaic verb meaning
"to wipe (a person's nose)". https://www.quora.com/What-is-data-munging-How-can-it-be-used-in-a-sentence-Are-there-any-synonyms-Where-does-the-phrase-come-from
data
wrangling: sometimes
referred to as data munging, is the process of
transforming and mapping
data from one "raw"
data form into another format with
the intent of making it more appropriate and valuable for a
variety of downstream purposes such as analytics. A data
wrangler is a person who performs these
transformation operations. This may include further munging, data
visualization, data aggregation, training a statistical
model, as well as many other potential uses. Data munging as a
process typically follows a set of general steps which begin with
extracting the data in a raw form from the data source, "munging"
the raw data using algorithms (e.g. sorting) or parsing the data
into predefined data structures, and finally depositing the
resulting content into a data sink for storage and future use.[1] ... The
"wrangler" non-technical term is often said to derive from work
done by the United
States Library of Congress's National
Digital Information Infrastructure and Preservation Program (NDIIPP)
and their program partner the Emory
University Libraries based MetaArchive
Partnership. The term "mung" has roots in munging as
described in the Jargon
File.[2] The
term "Data Wrangler" was also suggested as the best analogy to
coder for someone working with data.[3] The
terms data wrangling and data wrangler had sporadic use in the
1990s and early 2000s. One of the earliest business mentions of
data wrangling was in an article in Byte Magazine in 1997 (Volume
22 issue 4) referencing “Perl’s data wrangling services”. In 2001
it was reported that CNN hired[4] “a
dozen data wranglers” to help track down information for news
stories. One
of the first mentions of data wrangling in a scientific context
was by Donald Cline during the NASA/NOAA Cold Lands Processes
Experiment.[5] Cline
stated the data wranglers “coordinate the acquisition of the
entire collection of the experiment data. Wikipedia
accessed 2018 May 24 https://en.wikipedia.org/wiki/Data_wrangling
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: scalability, extendability,
explicitness. http://oiled.semanticweb.org/building/
descriptive and prescriptive ontologies: The
difference between Descriptive and Prescriptive is (as some have
pointed out) related to Intended Use and Design Methodology, but
in both cases is not the same thing. …. If the basis
for the decisions of the Ontology were based on a number of
existing documents and systems, and the implied ontology within
those artifacts, then it is a Descriptive Ontology…If the basis
for the decisions of the Ontology is instead predicated on the
best possible current understanding of the referent material that
the Ontology deals with, then it is Prescriptive in nature.
Note that a Prescriptive ontology would be formed with a
priori knowledge (as much as possible) of the
body of referents, and not based on whatever abstraction of that
knowledge is captured in existing systems, models, etchttp://ontolog.cim3.net/forum/ontology-summit/2007-04/msg00155.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
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 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
domain ontology (or domain-specific ontology) represents concepts
which belong to part of the world. Particular meanings of terms
applied to that domain are provided by domain ontology. Wikipedia https://en.wikipedia.org/wiki/Ontology_(information_science)#Domain_ontology
dynamic ontology: In
most cases, groups of domain expert design and standardize
ontology model. Unfortunately, in some cases, domain experts are
not yet available to develop an ontology. In this paper, we extend
the possibilities of creating a shareable knowledge
conceptualization terminology in uncommon domain knowledge where a
standardized ontology developed by groups of experts is not yet
available. Our aim is to capture knowledge and behaviour which is
represented by data. We propose a model of automatic data-driven
dynamic ontology creation. Fudholi
D.H., Rahayu W., Pardede E., Hendrik (2013) A Data-Driven Approach
toward Building Dynamic Ontology. In: Mustofa K., Neuhold E.J.,
Tjoa A.M., Weippl E., You I. (eds) Information and Communication
Technology. ICT-EurAsia 2013. Lecture Notes in Computer Science,
vol 7804. Springer, Berlin, Heidelberg https://link.springer.com/chapter/10.1007/978-3-642-36818-9_23
Dynamic Ontology video, Palantir https://www.youtube.com/watch?v=ts0JV4B36Xw&feature=youtu.be
dynamic taxonomies: A
new taxonomic model for structuring and accessing large
heterogeneous information bases is presented. The model is
designed to simplify both classification and access by
computer-illiterate people. It defines simple and intuitive
operations to access large information bases at the conceptual
level and at different levels of abstraction, in a totally
assisted way, through a simple, yet effective visual interface.
The model can also be used to summarize result sets computed by
other query methods, such as information retrieval, shape
retrieval, etc., and to provide user maps for complex hypermedia
networks. IEEE
Transactions on Knowledge and Data Engineering Vol 12(3): 468-479,
May 2000 ACM Digital
Library https://dl.acm.org/citation.cfm?id=628071
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, 1995 http://bcc.musiclibraryassoc.org/BCC-Historical/BCC95/95WGFAM2.html
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
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
FAIR data—Findable, Accessible, Interoperable, Reusable:
Meeting the fair principles
Principle F: findable The
principle of Findability focuses on the unique and unambiguous
identification of all relevant entities; the rich annotation and
description of these entities; the searchability of those
descriptive annotations; and the explicit connection between
metadata and data elements.
Principle A: accessible The principle of Accessibility speaks to
the ability to retrieve data or metadata based on its identifier,
using an open, free, and universally implementable standardized
protocol. The protocol must support authentication and
authorization if necessary, and the metadata should be accessible
“indefinitely,” and independently of the data, such that
identifiers can be interpreted/understood even if the data they
identify no longer exists.
Principle I: interoperable The Interoperability Principle states
that (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation; that
vocabularies themselves should follow FAIR principles; and that
the (meta)data should include qualified references to other
(meta)data.
Principle R: reusable The FAIR Reusability principle requires that
meta(data) have a plurality of accurate and relevant attributes;
provide a clear and accessible data usage license; associate data
and metadata with their provenance; and meet domain-relevant
community standards for data content. Publishing FAIR Data:
An Exemplar Methodology Utilizing PHI-Base Frontiers in Plant
Science, 2016 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4922217/
findability: the
ease with which information contained on a website can
be found, both from outside the website (using search
engines and
the like) and by users already on the website.[1] Although
findability has relevance outside the World
Wide Web,
the term is usually used in that context. Most relevant websites
do not come up in the top results because designers and engineers
do not cater to the way ranking algorithms work currently.[2] Its
importance can be determined from the first law of e-commerce,
which states "If the user can’t find the product, the user can’t
buy the product."[3] Wikipedia https://en.wikipedia.org/wiki/Findability accessed
2017 Oct 28
ACCESSIBILITY: https://en.wikipedia.org/wiki/Accessibility
INTEROPERABILITY: Wikipedia https://en.wikipedia.org/wiki/Interoperability
https://en.wikipedia.org/wiki/Interoperability#Syntactic_interoperability
https://en.wikipedia.org/wiki/Semantic_interoperability
https://en.wikipedia.org/wiki/Cross-domain_interoperability http://www.ncoic.org/cross-domain-interoperability/
https://en.wikipedia.org/wiki/Open_standard
REUSABILITY: In computer
science and software
engineering, reusability is
the use of existing assets in some form within the software
product development process. Assets are products and by-products
of the software development life cycle and include code, software
components, test suites, designs and documentation. Leverage is
modifying existing assets as needed to meet specific system
requirements. Wikipedia https://en.wikipedia.org/wiki/Reusability accessed
2017 Oct 28
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
Wikipedia http://en.wikipedia.org/wiki/Folksonomy
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, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm
Wikipedia http://en.wikipedia.org/wiki/Formal_ontology
Aligning terminologies and ontologies is not an easy task. The
divergence of the underlying meaning of word descriptions and
terms within different information sources is a well known
obstacle for direct approaches to data integration and mapping.
One single description may have a completely different meaning in
one data source when compared with another. This is because
different databases/terminologies often have a different viewpoint
on similar items. They are usually built with a specific
application-perspective in mind and their hierarchical structure
represents this.
A formal ontology, on the other hand, represents entities without
a particular application scope. Its hierarchy reflects ontological
principles and a basic class-subclass relation between its
concepts. A consistent framework like this is ideal for cross
mapping data sources. However, one cannot just integrate these
external data sources in the formal ontology. A direct
incorporation would lead to corruption of the framework and
principles of the formal ontology.
A formal ontology is a great cross mapping hub only if a complete
distinction between the content and structure of the external
information sources and the formal ontology itself is maintained.
This is possible by specifying a mapping relation between concepts
from a chaotic external information source and a concept in the
formal ontology that corresponds with the meaning of the former
concept. Formal ontology as
a cross mapping hub: cross mapping taxonomies, databases and
nonformal ontologies. Wikipedia accessed 2017 Oct 28 See related
ontology alignment
https://en.wikipedia.org/wiki/Formal_ontology#Applications_for_formal_.28upper-level.29_ontologies
game ontology: Game
Ontology Project http://www.gameontology.com/index.php/Main_Page
game taxonomy: See game classification Wikipedia https://en.wikipedia.org/wiki/Game_classification
Gene Ontology Consortium: The Gene Ontology project
provides controlled vocabularies of defined terms representing
gene product properties. These cover three domains: Cellular
Component, the parts of a cell or its extracellular
environment; Molecular Function, the elemental activities
of a gene product at the molecular level, such as binding or
catalysis; and Biological Process, operations or sets of
molecular events with a defined beginning and end, pertinent to
the functioning of integrated living units: cells, tissues,
organs, and organisms.
The GO ontology is structured as a directed acyclic graph where
each term has defined relationships to one or more other terms in
the same domain, and sometimes to other domains. The GO vocabulary
is designed to be species-agnostic, and includes terms applicable
to prokaryotes and eukaryotes, and single and multicellular
organisms. http://geneontology.org/ http://geneontology.org/page/ontology-documentation
global ontologies: There
is an increasing interest in linguistic ontologies (e.g. WordNet)
for a variety of content-based tasks, including conceptual
indexing, word sense disambiguation and cross-language information
retrieval. A relevant contribution in this direction is
represented by linguistic ontologies with domain specific
coverage, which are a crucial topic for the development of
concrete application systems. This paper tries to go a step
further in the direction of the interoperability of specialized
linguistic ontologies, by addressing the problem of their
integration with global ontologies. This scenario poses some
simplifications with respect to the general problem of merging
ontologies, since it enables to define a strong precedence
criterion so that terminological information overshadows generic
information whenever conflicts arise. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.7920
Related term: semantic heterogeneity
heavyweight ontologies: Ontologies
can be divided broadly into two main types; heavyweight and
lightweight5. Lightweight ontologies are mainly taxonomies and in
this type of ontology classes, subclasses, attributes and values
are represented as well as simple inheritance5. A database schema
which formally describes records in a database would be an example
of a “lightweight” ontology. Heavyweight ontologies model domains
in a deeper way and include multiple inheritance, axioms and
constraints5. A
comparative analysis of methodologies, tools ... - Semantic
Scholar https://pdfs.semanticscholar.org/07aa/b09e346f8732528889ed94fb283d0be65249.pdf
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, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm
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 http://www.ieee.org/education_careers/education/standards/standards_glossary.html
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. Related
terms: metadata, ontology, taxonomies; Narrower terms: ontology
interoperability, semantic interoperability, software
interoperability
lightweight ontology an ontology or knowledge
organization system in
which concepts are connected by rather general associations than
strict formal connections. Examples of lightweight ontologies
include associative network and multilingual classifications but
the term is not used consistently. Wikipedia https://en.wikipedia.org/wiki/Lightweight_ontology Accessed
2017 Oct 28
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." http://linkeddata.org/
Linked data glossary: This
document is a glossary of terms defined and used to describe
Linked Data, and its associated vocabularies and Best
Practices W3C https://www.w3.org/TR/ld-glossary/
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 http://wwwconference.org/proceedings/www2002/alternate/744/index.html more
on metadata
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
Related terms: lower ontologies, upper ontologies
mixed ontologies: 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, 1997 http://users.bestweb.net/~sowa/ontology/gloss.htm
Related terms: local ontologies, pure ontologies
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 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 2010 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
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, Towards
Principles for the Design of Ontologies http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.5775&rep=rep1&type=pdf Ontology
Tom Gruber updated http://tomgruber.org/writing/ontology-definition-2007.htm
ontologies - proteomics: A
principal aim of post- genomic biology is elucidating the
structures, functions and biochemical properties of all gene
products in a genome. However, to adequately comprehend such a
large amount of information we need new descriptions of proteins
that scale to the genomic level. In short, we need a unified
ontology for proteomics. Much progress has been made towards this
end, including a variety of approaches to systematic structural
and functional classification and initial work towards developing
standardized, unified descriptions for protein properties. In
relation to function, there is a particularly great diversity of
approaches, involving placing a protein in structured hierarchies
or more- generalized networks and a recent approach based on
circumscribing a protein's function through systematic enumeration
of molecular interactions. N Lan, GT Montelione, M. Gerstein,
Ontologies for proteomics: towards a systematic definition of
structure and function that scales to the genome level, Current
Opinion in Chemical Biology 7(1): 44- 54, Feb. 2003
ontology, ontologies: In
the context of computer and information sciences, an
ontology defines a set of representational primitives with
which to model a domain of knowledge or discourse. The
representational primitives are typically classes (or sets),
attributes (or properties), and relationships (or relations among
class members). The definitions of the representational
primitives include information about their meaning and constraints
on their logically consistent application. In the context of
database systems, ontology can be viewed as a level of abstraction
of data models, analogous to hierarchical and relational models,
but intended for modeling knowledge about individuals, their
attributes, and their relationships to other
individuals. Ontologies are typically specified in languages that
allow abstraction away from data structures and implementation
strategies; in practice, the languages of ontologies are closer in
expressive power to first-order logic than languages used to model
databases. For this reason, ontologies are said to be at the
"semantic" level, whereas database schema are models of data at
the "logical" or "physical" level. Due to their independence from
lower level data models, ontologies are used for integrating
heterogeneous databases, enabling interoperability among disparate
systems, and specifying interfaces to independent, knowledge-based
services. In the technology stack of the Semantic Web standards
[1], ontologies are called out as an explicit layer. There are
now standard languages and a variety of commercial and open source
tools for creating and working with ontologies. Ontology,
Tom Gruber in Encyclopedia of Database Systems, Springer Verlag,
2009
http://tomgruber.org/writing/ontology-definition-2007.htm
Terminology of methods and techniques for defining, sharing,
and merging ontologies, John
F. Sowa, 2001.definitions, include formal ontology, mixed
ontology, prototype type ontology, terminological ontology. http://users.bestweb.net/~sowa/ontology/gloss.htm
Ontology (information science) In computer
science and information
science,
an ontology is a formal naming and definition of the types,
properties, and interrelationships of the entities that
really exist in a particular domain
of discourse.
Thus, it is a practical application of philosophical ontology,
with a taxonomy.Wikipedia http://en.wikipedia.org/wiki/Ontology_(information_science
Ontology Development 101 https://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html
What is an ontology? W3C,
Requirements for a web ontology language, working in progress] http://www.w3.org/TR/webont-req/#onto-def
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
ontology alignment: Ontology
alignment, or ontology matching, is the process of
determining correspondences between concepts in ontologies.
A set of correspondences is also called an alignment. The phrase
takes on a slightly different meaning, in computer
science, cognitive
science or philosophy.Wikipedia https://en.wikipedia.org/wiki/Ontology_alignment Accessed
23017 Oct 27
ontology annotation tools: Link
unstructured and semi-structured 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 components: Most
ontologies describe individuals (instances), classes (concepts),
attributes, and relations. Wikipedia https://en.wikipedia.org/wiki/Ontology_components Accessed
2017 Oct 28
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 interoperability: 3.3
Ontology interoperability, W3C, Requirements for a web ontology
language, work in progress http://www.w3.org/TR/webont-req/#goal-interoperability
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, Jim Hendler, Henry Lieberman, and Wolfgang
Wahlster, editors. Spinning the Semantic Web: Bringing the World
Wide Web to Its Full Potential. MIT Press, 2002. https://www.researchgate.net/publication/221024668_Ontologies_Come_of_Age
ontology learning (ontology
extraction, ontology
generation, or ontology acquisition) is the automatic
or semi-automatic creation of ontologies,
including extracting the corresponding domain's terms
and the relationships between the concepts that
these terms represent from a corpus of
natural language text, and encoding them with an ontology
language for
easy retrieval. As building
ontologies manually
is extremely labor-intensive and time-consuming, there is great
motivation to automate the process. Wikipedia http://en.wikipedia.org/wiki/Ontology_learning Accessed
2017 Oct 28
Open Biomedical and Biomedical Ontology OBO
Foundry: The
Open Biological and Biomedical Ontology (OBO) Foundry is a
collective of ontology developers that are committed to
collaboration and adherence to shared principles. The mission of
the OBO Foundry is to develop a family of interoperable ontologies
that are both logically well-formed and scientifically accurate. . http://www.obofoundry.org/
orthogonal ontologies: One
of the key—and, arguably, more controversial—aims of the OBO
Foundry effort is to create a set of orthogonal ontologies,
which means that each term is defined in only one ontology. Other
ontologies that need to use the term refer to its definition in
the source ontology https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-2-S2-S2
Disjoint, non-overlapping. 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 Lndley. 2003 https://www.gamasutra.com/view/feature/131205/game_taxonomies_a_high_level_.php
OWL Web Ontology Language: W3C
Web Ontology Language (OWL) is a Semantic Web language designed to
represent rich and complex knowledge about things, groups of
things, and relations between things. OWL is a computational
logic-based language such that knowledge expressed in OWL can be
exploited by computer programs, e.g., to verify the consistency of
that knowledge or to make implicit knowledge explicit. https://www.w3.org/OWL/
OXO: EMBL-EBI
Ontology Xref Service (OxO). OxO
is a service for finding mappings (or cross-references) between
terms from ontologies, vocabularies and coding standards. OxO
imports mappings from a variety of sources including the Ontology
Lookup Service and a subset of mappings provided by the UMLS. https://www.ebi.ac.uk/spot/oxo/
paraphrase problem: The
situation that arises when the terminology used in the request is
different from that used by the author. William A.
Woods http://www.w3.org/Conferences/WWW4/Panels/krp/woods.html
Conceptual Indexing for Precision Content Retrieval http://dl.acm.org/citation.cfm?id=974965
Protege Ontology Library https://protegewiki.stanford.edu/wiki/Protege_Ontology_Library
pure ontologies:
Related term: orthogonal ontologies
RDF Resource Description Framework: a
standard model for data interchange on the Web. RDF has features
that facilitate data merging even if the underlying schemas
differ, and it specifically supports the evolution of schemas over
time without requiring all the data consumers to be changed
extends the linking structure of the Web to use URIs to name the
relationship between things as well as the two ends of the link
(this is usually referred to as a “triple”).
Using this simple model, it allows structured and semi-structured
data to be mixed, exposed, and shared across different
applications. https://www.w3.org/RDF/
An RDF statement expresses a relationship between two resources.
The subject and
the object represent
the two resources being related; the predicate represents
the nature of their relationship. The relationship is phrased in a
directional way (from subject to object) and is called in RDF a property.
Because RDF statements consist of three elements they are called triples. RDF
Primer 1.1 2014 https://www.w3.org/TR/rdf11-primer/
semantic heterogeneity: HETEROGENEITY is
when database
schema or datasets for
the same domain are developed by independent parties, resulting in
differences in meaning and interpretation of data values.[1] Beyond structured
data,
the problem of semantic heterogeneity is compounded due to the
flexibility of semi-structured
data and
various tagging methods applied to documents or unstructured
data.
Semantic heterogeneity is one of the more important sources of
differences in heterogeneous
datasets. Yet,
for multiple data sources to interoperate with
one another, it is essential to reconcile these semantic differences.
Decomposing the various sources of semantic heterogeneities
provides a basis for understanding how to map and transform data
to overcome these differences. Wikipedia Accessed 2017 Oct
28https://en.wikipedia.org/wiki/Semantic_heterogeneity
semantic interoperability: the
ability of computer systems
to exchange data with
unambiguous, shared meaning. Semantic interoperability
is a requirement to enable machine computable logic, inferencing,
knowledge discovery, and data federation between information
systems.[1] Semantic
interoperability is therefore concerned not just with the
packaging of data (syntax),
but the simultaneous transmission of the meaning with the data
(semantics). This is accomplished by adding data about the data
(metadata), linking each data element to a controlled, shared
vocabulary. The meaning of the data is transmitted with the data
itself, in one self-describing "information package" that is
independent of any information system. It is this shared
vocabulary, and its associated links to an ontology, which
provides the foundation and capability of machine interpretation,
inferencing, and logic. Wikipedia https://en.wikipedia.org/wiki/Semantic_interoperability Accessed
2017 Octr 28 Related
term: XML
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 Fen 1995
http://bcc.musiclibraryassoc.org/BCC-Historical/BCC95/95WGFAM2.html
Related term: syntactic relationships
semantic web: The
term “Semantic Web” refers to W3C’s vision of the Web of linked
data. Semantic Web technologies enable people to create data
stores on the Web, build vocabularies, and write rules for
handling data. Linked data are empowered by technologies such as RDF, SPARQL, JSON-LD, OWL,
and SKOS. The
goal of this wiki is to provide a “first stop” for more
information on Semantic Web technologies, in particular on Semantic
Web Standards published by the W3C. https://www.w3.org/2001/sw/wiki/Main_Page
Subsumed now by W3C Data activity https://www.w3.org/2013/data/
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
Semantic web Challenge: http://challenge.semanticweb.org/
Semantic web Healthcare and Life Sciences Interest Group http://www.w3.org/2001/sw/hcls/
Semantic web: Ontology http://semanticweb.org/wiki/Ontology
Related terms: metadata, ontology, RDF, taxonomies, XML.
Compare: syntax
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 Related
term: reusable ontologies
shared taxonomies: Shared
Taxonomies,
LouisRosenfeld.com, 2004 http://www.louisrosenfeld.com/home/bloug_archive/000276.html
Simple Knowledge Organization System (SKOS)
is a W3C
recommendation designed
for representation of thesauri, classification
schemes, taxonomies, subject-heading
systems,
or any other type of structured controlled
vocabulary.
SKOS is part of the Semantic
Web family
of standards built upon RDF and RDFS,
and its main objective is to enable easy publication and use of
such vocabularies as linked
data. Wikipedia,
accessed 2017 Oct 29 https://en.wikipedia.org/wiki/Simple_Knowledge_Organization_System
soft ontology http://en.wikipedia.org/wiki/Soft_ontology
structural heterogeneity: Different
databases use different fields, fieldnames and relationships
between elements. This can also be a term in structural biology. Compare
semantic heterogeneity Related term: metadata
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://bcc.musiclibraryassoc.org/BCC-Historical/BCC95/95WGFAM2.html
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. http://www.ncbi.nlm.nih.gov/pubmed/11807167 Compare
semantics
tag cloud: Wikipedia http://en.wikipedia.org/wiki/Tag_cloud
tags, tagging: Wikipedia http://en.wikipedia.org/wiki/Tag_(metadata)
taxonomies, taxonomy: the
practice and science of classification. The word is also used as
a count
noun: a
taxonomy, or taxonomic scheme, is a particular classification.
The word finds its roots in the Greek
language τάξις, taxis (meaning
'order', 'arrangement') and νόμος, nomos ('law' or
'science'). Originally, taxonomy referred only to the
classification of organisms or a particular classification of
organisms. In a wider, more general sense, it may refer to a
classification of things or concepts, as well as to the principles
underlying such a classification. Taxonomy is different from meronomy which
is dealing with the classification of parts of a whole. Many
taxonomies have a hierarchical structure,
but this is not a requirement. Wikipedia accessed
2017 Oct 28 https://en.wikipedia.org/wiki/Taxonomy_(general)
Taxonomy disambiguation https://en.wikipedia.org/wiki/Taxonomy Not
to be confused with taxidermy.
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 http://taxonomy.sla.org/
Taxonomy best practices
7 taxonomy best practices, David Hillis, CMSWire 2015 https://www.cmswire.com/information-management/7-taxonomy-best-practices/
Guidelines for taxonomy design, Drupal https://www.drupal.org/docs/7/organizing-content-with-taxonomies/guidelines-for-taxonomy-design
Taxonomy Design: Best Practices, Zach Wahl 2014 https://enterprise-knowledge.com/taxonomy-design-best-practices/
taxonomy mapping: Mapping
matches one taxonomy against another, so that terms in one
taxonomy may be used for terms in another, such as a user
interface taxonomy matching to another taxonomy that had been used
to index the content. The end result is that one taxonomy can now
retrieve more content…. Merging and mapping are not the same
thing. Merging brings together two taxonomies on the same subject,
eliminating duplicate terms, supplementing each other with terms
from one or the other taxonomy. The end result is a new and
improved taxonomy taking the best of both of the legacy
taxonomies. Heather Hedden, taxonomy merging or mapping?
2012 http://accidental-taxonomist.blogspot.com/2012/01/taxonomy-merging-or-mapping.html
Taxonomy, SharePoint
Managed Metadata 101 webinar 2017 Aug https://www.youtube.com/watch?v=VI-Z0JT8QoM
Two ways to design SharePoint taxonomy for an organization, Greg
Zelfond, 2017 April
http://sharepointmaven.com/2-ways-to-design-sharepoint-taxonomy-for-an-organization/
Introduction to Taxonomy for SharePoint, Charmaine Brooks http://documentmedia.com/article-2625-An-Introduction-to-Taxonomy-and-Classification-for-SharePoint.html ,
2017 April
Taxonomy SharePoint, Accidental Taxonomist http://accidental-taxonomist.blogspot.com/search/label/SharePoint
taxonomy standards: The
importance of the standards should not be overlooked. Taxonomies
are only useful if they are well constructed, and decades of
experience, practice, and use have indicated the conventions by
which the most usable and useful taxonomies should be built. In
addition to prescribing what works, the standards also encourage
consistency. Consistently designed taxonomies thus become familiar
to users, who then know how to use them with minimal training.
Users don’t have to be told what a narrower term is and where to
find it, or what a related term is and what its purpose is. Heather
Hedden, Accidental Taxonomist Blog, 2012 http://accidental-taxonomist.blogspot.com/2012/03/taxonomy-standards.html
Standard Taxonomies: This page contains an up-to-date listing of
XBRL schemas and linkbases of standard taxonomies that are
supported for the [Securities and Exchange] Commission's
Interactive Data programs. https://www.sec.gov/info/edgar/edgartaxonomies.shtml
Taxonomy
standards, Accidental Taxonomist http://accidental-taxonomist.blogspot.com/search/label/Taxonomy%20standards
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,
1997 http://users.bestweb.net/~sowa/ontology/gloss.htm
Terminology Forum,
Univ of Vaasa, Finland http://www.uva.fi/en/sites/terminology/ information
on terminological activities including terminology
work, research and education ,
on online
glossaries and termbanks from
different fields as well as on general
language dictionaries in
various languages. There are terminology
news and events,
a Twitter feed and links to terminology blogs.
thesaurus: In library
science and information
science,
thesauri have been widely used to specify domain models. Recently,
thesauri have been implemented with Simple
Knowledge Organization System (SKOS). Wikipedia https://en.wikipedia.org/wiki/Thesaurus Accessed
2017 Oct 28
top down
and bottom up ontologies: I
explore how a knowledge framework might be constructed, and also
how it can be represented in machine-understandable form. This, I
think, will be seen as one of the central challenges of the
current era. I have worked on bodies responsible for
the formalisation of information and have also for 15 years been
co-developing Chemical Markup Language (with Henry Rzepa). … My
first view - perhaps stemming from a background in physical
science - was that it should not be too difficult to create
machine-processable systems. We are using to manipulating
algorithms and transforming numeric quantities between different
representations. This process seemed to be universal and
independent of culture. … But my own experience has shown that the
creation of ontologies - or any classification - can be an emotive
area and lead to serious disagreements. It's easy for any
individual to imagine that their view of a problem is complete and
internally consistent and must therefore be identical to others in
the same domain. And so the concept of a localised "upper
ontology" creeps in - it works for a subset of human knowledge.
And the closer to physical science the easier to take this view.
But it doesn't work like that in practice. And there is another
problem. Whether or not upper ontologies are possible it is often
impossible to get enough minds together with a broad enough view
to make progress. So my pragmatic approach in
chemistry - and it is a pragmatic science - is that no overarching
ontology is worth pursuing. Even if we get one, people won't use
it. The
International Union of Pure and Applied Chemistry has
created hundreds of rules on how to name chemical compounds and
relatively few chemists use them unless they are forced to. We
have found considerable variance in the way authors report
experiments and often the "correct" form is hardly used. In many
cases it is "look at the current usage of other authors and do
something similar". Peter Murray Rust, Top down or
bottom up ontologies? 2007 https://blogs.ch.cam.ac.uk/pmr/2007/06/24/top-down-or-bottom-up-ontologies/
top down taxonomy: Bottom
up and top down are two opposite (but completely compatible)
approaches to developing hierarchical structure. The controlled
vocabulary standard ANSI/NISO
Z39.19 explains
the two approaches as follows:
a) Top Down – The broadest terms
are identified first and then narrower terms are selected to reach
the desired level of specificity. The necessary hierarchical
structures and relationships are created as the work proceeds
b) Bottom Up – This case frequently occurs when lists of terms
have been derived from a corpus of content objects and are then to
be incorporated in a controlled vocabulary. As in the case above,
the necessary hierarchical structures and relationships are
created as the work proceeds, but starting from the terms having
the narrowest scope and moving to the more generic ones. The
standard adds, “If a new controlled vocabulary is being created,
the “top down” approach is preferred. Once a controlled vocabulary
is in place, the “bottom up” approach is most often used to add
new terms to cover new concepts.” (ANSI/NISO Z39.19-2005, page 91)
In my own experience in creating and developing taxonomies, I have
also found that a combination of top down and bottom up approaches
works best. It also seems to be the most natural and organic
approach. Marjorie M.K. Hlava President, Access
Innovations
Melody Smith, Top Down? Taxo Diary 2013 http://taxodiary.com/2013/10/bottom-up-or-top-down/
topic map: a
standard for the representation and interchange of knowledge, with
an emphasis on the findability of
information. Topic maps were originally developed in the late
1990s as a way to represent back-of-the-book
index structures
so that multiple indexes from different sources could be merged.
However, the developers quickly realized that with a little
additional generalization, they could create a meta-model with
potentially far wider application. The ISO standard
is formally known as ISO/IEC 13250:2003. Wikipedia http://en.wikipedia.org/wiki/Topic_Maps
(XML) Topic Maps, XML
Cover Pages, Robin Cover, 2008 http://xml.coverpages.org/topicMaps.html
triple: See under RDF
upper ontology: In information
science,
an upper ontology (also known as a top-level ontology or foundation
ontology) is an ontology (in
the sense used in information science) which consists of very
general terms (such as "object", "property", "relation") that are
common across all domains. An important function of an upper
ontology is to support broad semantic
interoperability among
a large number of domain-specific ontologies by providing a common
starting point for the formulation of definitions. Terms in the
domain ontology are ranked "under" the terms in the upper
ontology, and the former stand to the latter in subclass
relations. https://en.wikipedia.org/wiki/Upper_ontology accessed
2017 Oct 28
Upper level ontologies are used to facilitate the semantic
integration of domain ontologies and guide the development of new
ontologies. For this purpose, they contain general categories that
are applicable across multiple domains
What is
an upper-level ontology? | [accessed Jan 31
2018].Robert Hoehndorf (2010) What is an upper level ontology? Ontogenesis. https://www.researchgate.net/publication/297667737_What_is_an_upper-level_ontology
upper ontology [computer science] : In information
science,
an upper ontology (also known as a top-level ontology or foundation
ontology) is an ontology (in
the sense used in information science) which consists of very
general terms (such as "object", "property", "relation") that are
common across all domains. An important function of an upper
ontology is to support broad semantic
interoperability among
a large number of domain-specific ontologies by providing a common
starting point for the formulation of definitions. Terms in the
domain ontology are ranked "under" the terms in the upper
ontology, and the former stand to the latter in subclass
relations.Wikipedia https://en.wikipedia.org/wiki/Upper_ontology accessed
2017 Oct 29
vaccine ontology: A
bottleneck in vaccine research and development is the lack of a
Vaccine Ontology for vaccine data standardization, integration,
and analysis. Dr.
Yongqun "Oliver" He and
the VIOLIN team
are working with Dr.
Barry Smith (University
at Buffalo and
the National
Center for Biomedical Ontology)
and Dr.
Lindsay Cowell (Duke
University)
to develop the Vaccine Ontology (VO). The Vaccine Ontology is
closely related to the Infectious
Disease Ontology (IDO),
initiated by Drs. Cowell and Smith http://www.violinet.org/vaccineontology/
vocabularies: On
the Semantic Web, vocabularies define the concepts and
relationships (also referred to as “terms”) used to describe and
represent an area of concern. Vocabularies are used to classify
the terms that can be used in a particular application,
characterize possible relationships, and define possible
constraints on using those terms. In practice, vocabularies can be
very complex (with several thousands of terms) or very simple
(describing one or two concepts only). There is no
clear division between what is referred to as “vocabularies” and
“ontologies”. The trend is to use the word “ontology” for more
complex, and possibly quite formal collection of terms, whereas
“vocabulary” is used when such strict formalism is not necessarily
used or only in a very loose sense. Vocabularies are the basic
building blocks for inference techniques
on the Semantic Web. W3C, Vocabularies, 2015 https://www.w3.org/standards/semanticweb/ontology
W3C Data activity: More
and more Web applications provide a means of accessing data. From
simple visualizations to sophisticated interactive tools, there is
a growing reliance on the availability of data which can be “big”
or “small”, of diverse origin, and in different formats; it is
usually published without prior coordination with other publishers
— let alone with precise modeling or common vocabularies. The Data
Activity recognizes and works to overcome this diversity to
facilitate potentially Web-scale data integration and processing.
It does this by providing standard data exchange formats, models,
tools, and guidance. The overall vision of the Data
Activity is that people and organizations should be able to share
data as far as possible using their existing tools and working
practices but in a way that enables others to derive and add
value, and to utilize it in ways that suit them. Achieving that
requires a focus not just on the interoperability of data but of
communities. https://www.w3.org/2013/data/
XML Extensible Marku0p Language:
a simple, very flexible text format derived from SGML (ISO 8879).
Originally designed to meet the challenges of large-scale
electronic publishing, XML is also playing an increasingly
important role in the exchange of a wide variety of data on the
Web and elsewhere. This page describes the work being done at W3C
within the XML Activity, and how it is structured. https://www.w3.org/XML/
Ontologies & taxonomies
resources
A DAM Glossary of Common Terms, John
Horodysk, 2018 https://www.cmswire.com/digital-asset-management/a-dam-glossary-of-common-terms/ Digital
Asset Management, with definitions of controlled vocabulary,
governance, metadata, taxonomy, workflow.
Accidental Taxonomist blog and book, Heather Hedden http://accidental-taxonomist.blogspot.com/
Linked Data Glossary, W3C, 2013 https://www.w3.org/TR/ld-glossary/
Ontology, John F Sowa, 2010 http://www.jfsowa.com/ontology/index.htm
Ontology
Lookup Service, Samples, Phenotypes & Ontologies Team, EMBL-EBI https://www.ebi.ac.uk/ols/index a
repository for biomedical ontologies that aims to provide a single
point of access to the latest ontology versions. You can browse
the ontologies through the website as well as programmatically via
the OLS API.
Carole Goble and Chris Wroe (2004). The Montagues and the
Capulets Comparative and Functional Genomics, 5 (8),
623-632 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447479/
Classic paper: Montagues and Capulets in Science on
the social problems of building biomedical ontologies. This paper
is worth reading (or re-reading) because it makes lots of relevant
points about the use and abuse of research and how people
misunderstand each other. It’s funny (and available Open Access
too… ABSTRACT: Two households, both alike in dignity, In fair
Genomics, where we lay our scene, (One, comforted by its logic’s
rigour, Claims ontology for the realm of pure, The other, with
blessed scientist’s vigour, Acts hastily on models that endure),
From ancient grudge break to new mutiny, When ‘being’ drives a fly-man to
blaspheme. From forth the fatal loins of these two foes,
Researchers to unlock the book of life; Whole misadventured
piteous overthrows, Can with their work bury their clans’ strife.
The fruitful passage of their GO-mark’d
love,
And the continuance of their studies sage, Which, united, yield
ontologies undreamed-of, Is now the hour’s traffic of our stage;
The which if you with patient ears attend, What here shall miss,
our toil shall strive to mend. https://duncan.hull.name/2010/02/05/shakespeare/
How to look for other unfamiliar terms
