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Ontologies & Taxonomies glossary & taxonomy

Evolving Terminology for Emerging Technologies
Comments? Questions? Revisions? 
Mary Chitty MSLS
Last revised January 10, 2020

SCOPE NOTE Ontologies vs. taxonomies Ontologies have been less successful than they could be in large-scale business applications due to a wide variety of interpretations. This leads to confusion, and consequently, people from various research communities use the term with different – sometimes incompatible – meanings. This research work analyzes and clarifies the term ontology and points out its difference from taxonomy. By way of two business case studies, both their potential in ontological engineering and the perceived requirements for ontologies are highlighted, and their misuse in research and business is discussed. In order to examine the case for applying ontologies in a specific domain or use case, the main benefits of using ontologies are defined and categorized as technical-centered or user-centered  An analysis of ontologies and their success factors for application to business, Christina Feilmayr Wolfram Wöß  Data & Knowledge Engineering Volume 101, January 2016, Pages 1-23 

Related glossaries include:  
Bioinformatics     Clinical informatics   Data science & Machine Learning    Drug discovery informatics      Genomic informatics      Protein informatics    Informatics term index

application ontologyan ontology engineered for a specific use or application focus and whose scope is specified through testable use cases. The application ontology will often use or reference canonical ontologies to construct ontological classes and relationships between classes. Application ontologies are used when modeling cross-domain experiments in biology, for data annotation or visualization and for producing data driven views across reference ontologies for specific user groups. … Application ontologies can also offer alternative ‘views’ on the reference ontologies by producing specific user or domain-oriented definitions for ontology classes. This may involve producing a definition that a particular community will relate to (given the application focus) (e.g. ‘normalization’ may have several meanings depending upon the context and application focus) or rendering class labels for a specific user community. James Malone, Helen Parkinson (2010) Reference and Application Ontologies.

application taxonomies: Within a given application’s information architecture, we use taxonomies to replace the general term group with the category or menu group and arrange all the “things” according to their actions, features or items.  David Rashty, Creating your application taxonomy, 2013

best practices taxonomies: My talk at Taxonomy Boot Camp 2017 Nov  Taxonomy for emerging technologies: Today's science fiction can be tomorrow's science

biomedical ontologies:  Ontologies are consensus-based controlled vocabularies of terms and relations, with associated definitions which are logically formulated in such a way as to promote automated reasoning. Ontologies are being used in the following ways: (1) Reference for naming things. The Gene Ontology (GO) is the canonical example of an ontology created for the primary purpose of providing controlled and standardized terms for naming things. Creating such ontology-based annotations is highly valuable for both querying databases and analyzing high throughput data. (2) Representation of encyclopedic knowledge. For example, the Foundational Model of Anatomy (FMA) is a comprehensive ontology of human anatomy. FMA contains more than 70,000 entitles that describes the elements of canonical human morphology, providing declarative descriptions of detailed anatomic structures. (3) Specification of an information model (database/knowledgebase schema). Ontologies provide an explicit specification of the terms used to express information in biomedical domain. They make relationships among data types in databases explicit and support automated reasoning such as deducing subsumption among classes. Representations of information models using ontologies can be published on the Semantic Web in the format of the Web Ontology Language (OWL). (4) Specification of a data exchange format, such as BioPax for pathway data exchange. (5) Representation of semantics of data for information integration. Ontologies can streamline the process of integrating and accessing data across diverse resources. (6) Computer reasoning with data.  Introduction to Vaccine Ontology, Biomedical Ontologies and their Applications, University of Michigan Medical School

Open Biomedical Ontologies is an umbrella web address for well-structured controlled vocabularies for shared use across different biological and medical domains.   

biomedical ontology recommender web services:  Mark A Musen, Clement Jonquet and Nigam H. Shah, Journal of Biomedical Semantics 2010, 1(Suppl 1):S1doi:10.1186/2041-1480-1-S1-S1

bio-ontologies: Biologists and bioinformaticians now look to ontologies or software that uses ontologies as a means of standardising the way data are described, queried, and interpreted. Ontologies can be used for the annotation and curation of experimental datasets and, in data sharing, both within and beyond the confines of individual labs, organizations, and communities. Bio-ontologies are also commonly used in methods of analysis, particularly in gene set enrichment analysis [1], using ontologies such as the Gene Ontology. With modern high-throughput data-generation technologies, there is now, more than ever, a need to integrate data from these and other sources, and there is a concomitant need for ontologies—raising the question of how to choose a bio-ontology. … Rule 10: Sometimes an Ontology Is Not Needed at All Ontologies provide a means of “knowing” what is being described in a data set. There is, however, more than one way to capture such knowledge. Before embarking on using or indeed making a bio-ontology, you need to decide whether an ontology is really what is needed. In the broadest terms, we are talking about knowledge organisation systems of which there are numerous types of useful resources: glossaries, taxonomies, thesauri, ontologies, and terminologies. As a growing discipline, there is a temptation to suggest that using biomedical ontologies will offer some advantage. Ontologies offer advantages over other knowledge systems—they enable both computational use and human understanding, they can contain multiple classification axes of classes as well as formal descriptions of how classes relate to one another, and can include rich vocabularies of labels, synonyms, and textual definitions. If these are desirable selection criteria, then an ontology should be considered. Ontologies do also come with computational overheads, however, and can be complex to understand. Languages such as the Web Ontology Language (OWL) [16] utilise description logics, which are technically challenging. Other resources such as a vocabulary do not offer the sorts of classification and rich computational descriptions of an ontology but are often much simpler to understand. Let your requirements guide you; ontologies are not a panacea—sometimes one isn’t needed at all. Malone J, Stevens R, Jupp S, Hancocks T, Parkinson H, Brooksbank C (2016) Ten Simple Rules for Selecting a Bio-ontology. PLoS Comput Biol 12(2): e1004743.

BioOntologies SIG   Ontologies: Necessary but not sufficient

BioPAX: Biological Pathway Exchange (BioPAX) is 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. 

BioPortal: repository of biomedical ontologies has almost 800 ontologies. 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. 
BioPortal mapping to I2B2 file mapping

bottom-up ontologies: See under top down and bottom up ontologies

bottom up taxonomiesFaceted 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

canonical ontology: See under reference ontology.  Is there more to understand about this concept?

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. 

clinical ontologies:  Open Clinical Ontologies  support from Cancer Research UK  

common ontologyDefines 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

consistency: The task of the taxonomist or information architect is not to provide absolute consistency and standardization, maximum tidiness and complete information efficiency.  Optimizing efficiency in a complex system…destroys the resilience of at that system and its capacity to adapt to new circumstances…Consistency and standardization must be sufficient for effectiveness…and no more than sufficient. Patrick Lambe, Organising Knowledge: Taxonomies, Knowledge and Organisational effectiveness, Chandos Publishing, 2007.

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.  .

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 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  Related term: semantic heterogeneity

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  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)".

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 mungingdata 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

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.

descriptive and prescriptive ontologiesThe 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, etc.

descriptive taxonomiesSupports 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    
Related terms: bottom-up taxonomies, data management vocabulary, navigational taxonomies, shared taxonomies

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

The difference between a DAG and a hierarchy is that in the latter each child can only have one parent; a DAG allows a child to have more than one parent. A child term may be an "instance" of its parent term (is a relationship) or a component of its parent term (part- of relationship). A child term may have more than one parent term and may have a different class of relationship with its different parents. Gene Ontology Annotations.  

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

domain taxonomy: Domain is the highest  taxonomic rank in the hierarchical biological classification system, above the kingdom level. There are three domains of life, the  Archaea, the  Bacteria, and the  Eucarya. Encyclopedia of Astrobiology

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 
Dynamic Ontology video, Palantir

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

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

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

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    

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

Estimated cost benefit analysis of not having FAIR research data: Minimum of 10.2 billion Euros per year.  PwC estimates time lost per year at 4.5 billion Euros, cost of storage 5.3 billion Euros [only data from academic research, private sector data not available]; license cost 360 million [private sector data not available]. Interdisciplinary and potential economic growth impacts cannot be estimated reliably.  Cost of not having FAIR research data, PwC EU Services, 2018, European Union Publications.   more on FAIR data

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  Wikipedia

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    Wikipedia    

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  Related term: ontology alignment

formal taxonomy:  The easy test for a formal taxonomy is to just ask “is a?” or more fully “is a instance of the lower level category necessarily a type of the higher-level category?” at each level and if the answer is yes, then it’s a formal taxonomy. But most taxonomies are not formal.  Taxonomies - formal and informal, Semantic Arts, 2013

game ontology: Game Ontology Project 

game taxonomy: See game classification Wikipedia 

Gene Ontology GO 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.

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. 
Related term: semantic heterogeneity   

heavyweight ontologies: Ontologies can be divided broadly into two main types; heavyweight and lightweight 5. 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 constraints 5.  A comparative analysis of methodologies, tools ... - Semantic Scholar

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

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 2016

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: FAIR Data, metadata, ontology, taxonomies; Narrower terms: ontology interoperability, semantic interoperability, software interoperability

knowledge graph: Since Google started an initiative called Knowledge Graph, a substantial amount of research has used the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is sometimes used as synonym for ontology.[2] One common interpretation is that a knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts.[3]Unlike ontologies, knowledge graphs, such as Google's Knowledge Graph, often contain large volumes of factual information with less formal semantics. In some contexts, the term knowledge graph is used to refer to any knowledge base that is represented as a graph.  Wikipedia accessed 2019 June 7

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 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 datainformation, and knowledge on the Semantic Web using URIs and RDF."  
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

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   

Could elevate the status of the web from machine- readable to something we might call machine- understandable. Metadata is "data about data" or specifically in our current context "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application ("one application's metadata is another application's data"). W3C, "Introduction to RDF Metadata" 1997

Metadata is machine understandable information for the web. The W3C Metadata Activity addressed the combined needs of several groups for a common framework to express assertions about information on the Web, and was superceded by the W3C Semantic Web Activity.  W3C, Metadata and Resource Description, W3C Technology and Society Domain, 2001 

Structured data elements used to describe other data.  Year introduced: MeSH  2017 

We don’t have to choose. .. Ontologies, taxonomies and folksonomies are not mutually exclusive. In many contexts … the formal structure of ontologies and taxonomies is worth the investment.  In others…the casual serendipity of folksonomies is certainly better than nothing. And in some contexts, such as intranets and knowledge networks, a hybrid metadata ecology that combines elements of each may be ideal. Peter Morville, Ambient Findability, 2005:139 
Narrower terms: Dublin Core Metadata Initiative, faceted metadata Related terms: interoperability, RDF, semantic web 

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. [. Czbor "Support for Problem Formalisation in Engineering Design" 10th International DAAAM Symposium, Vienna Univ. of Technology, Austria, 21- 23 Oct. 1999  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  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.

Natural Language Processing NLP: Computer processing of a language with rules that reflect and describe current usage rather than prescribed usage. MeSH Year introduced: 1991(1987)

The newly emergent interest in natural language processing for biology has been christened "Information Extraction". But work in this area has been going on for many decades under different names and this site includes a good deal of information about past and current work in NLP and in information extraction for biology in particular. The other major descriptor of the general field is "Computational Linguistics"., Bob Futrelle, Computer Science, Northeastern Univ., US, updated 2010 

navigational ontology: Designing a navigational ontology for browsing and accessing anatomical images, AMIA 2000   

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  

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   Ontology Tom Gruber updated

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 

The Artificial-Intelligence literature contains many definitions of an ontology; many of these contradict one another. For the purposes of this guide an ontology is a formal explicit description of concepts in a domain of discourse (classes (sometimes called concepts)), properties of each concept describing various features and attributes of the concept (slots (sometimes called roles or properties)), and restrictions on slots (facets (sometimes called role restrictions)). An ontology together with a set of individual instances of classes constitutes a knowledge base. In reality, there is a fine line where the ontology ends and the knowledge base begins.  McGuinness, Deborah, Ontology Development 101, A Guide to creating your first ontology

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.   

Ontology Development 101  
What is an ontology
? W3C, Requirements for a web ontology language, work in progress]

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: 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 sciencecognitive science or philosophy. Wikipedia  Accessed 2017 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 

ontology chart: 

ontology components: Most ontologies describe individuals (instances), classes (concepts), attributes, and relations. Wikipedia Accessed 2017 Oct 28

ontology engineering  

ontology evolution: 3.2  W3C, Requirements for a web ontology language, work in progress      

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

ontology interoperability: 3.3 Ontology interoperability, W3C, Requirements for a web ontology language, work in progress  Broader term: interoperability

ontology languageAn 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.

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 Accessed 2017 Oct 28

ontology mapping: may refer to: Semantic integration, the process of interrelating information from diverse sources or Ontology alignment, the process of determining correspondences between concepts in ontologies Wikipedia accessed 2019 June 5 
Ontologies mapping, Pistoia Alliance  Guidelines, tools and services, best practices and use cases.

Open Biological 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.

open standard

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

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  Game Taxonomies: A High Level Framework for ... - Studentportalen

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.

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.

pure ontology:  Most definitions on the web seem to be highly philosophical. See also formal 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.

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

reference ontology: There are many reference or ‘canonical’ ontologies in biomedicine. Organizations such as the OBO Foundry aim to organise these reference ontologies into a collection of non-overlapping or ‘orthogonal’ and interoperable resources. There are challenges in integrating, building and consuming reference ontologies. Current reference ontologies are not fully interoperable as they are constructed in different styles, using different tools and often do not share a common upper level ontology. Consequently, the import of all or part of most reference ontologies into a single resource is not practical or feasible.  James Malone, Helen Parkinson (2010) Reference and Application Ontologies.

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 Feb 1995 Related term: syntactic relationships

reproducibility: More than 70% of researchers have tried and failed to reproduce experiments. More than half have failed to reproduce their own experiments. Nature 2016 survey of researchers.  

replication alone will get us only so far …  routine replication might actually make matters worse… an essential protection against flawed ideas … is the strategic use  of multiple approaches to address one question. Each approach has its own unrelated assumptions, strengths and weaknesses. Results that agree across different methodologies are less likely to be artefacts”.   Munafo & Smith, 2018, Robust research needs many lines of evidence, Marcus R. Munafò and George Davey Smith, Nature 553, 399-401 (2018) doi: 10.1038/d41586-018-01023-3

I would argue that ontologies and taxonomies can play a crucial role in fostering reproducibility and interoperability.

reusable ontologies: An ontology should have two important properties in order to be a successful candidate for reuse: It should be usable be different software systems and agents, and it should be combinable with other ontologies for new applications and domains. ...I conclude that the study of software architecture is beneficial for ontology developers, and that similar concepts should be developed in the field of ontology engineering to facilitate the reuse of existing ontologies. How can we build reusable ontologies? Stefan Ukena

reusable taxonomies: Structuring related work is a daunting task encompassing literature review, classification, comparison (primarily in the form of concepts), and gap analysis. Building taxonomies is a compelling way to structure concepts in the literature yielding reusable and extensible models. However, constructing taxonomies as a product of literature reviews could become, to our experiences, immensely complex and error-prone. Including new literature or addressing errors may cause substantial changes (ripple effects) in taxonomies coping with which requires adequate tools. To this end, we propose a \emph {Taxonomy-as-a-Service (TaaS)} platform. TaaS combines the systematic paper review process with taxonomy development, visualization, and analysis capabilities. ...The screencast of our tool demonstration is available   Taxonomy-as-a-Service: How To Structure Your Related Work, Cornell University 2019

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 28 

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  Accessed 2017 Oct 28   
Related term: XML

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 RDFSPARQLJSON-LDOWL, 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.  
Subsumed now by W3C Data 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

Semantic web Challenge:  
Semantic web in healthcare and life sciences community group 

Semantic web 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.

shared ontologies: 3.1 Shared ontologies, W3C, Requirements for a web ontology language, work in progress  Related term: reusable ontologies

shared taxonomies: Shared Taxonomies,, 2004 

Simple Knowledge Organization System (SKOS): a 
W3C recommendation designed for representation of thesauriclassification schemestaxonomiessubject-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 

simple ontology: Specifications meeting these properties will be referred to as simple ontologies. We will require the following properties to hold in order to consider something an ontology: Finite controlled (extensive) vocabulary. Unambiguous interpretation of classes and term relationships. Strict hierarchical subclass relationships between classes.  We consider the following properties typical but not mandatory:  Property specification on a per-class basis,  Individual inclusion in the ontology, Value restriction specification on a per-class basis, Finally, the following properties may be desirable but not mandatory nor typical: Specification of disjoint classes, Specification of arbitrary logical relationships between terms, Distinguished relationships such as inverse and part-whole. The line in our chart is drawn such that everything to the right of it will be called an ontology and meet at least the first three conditions stated above. Additionally, everything to the right of it can be used as a basis for inference.  McGuinness, Deborah. (2003). Ontologies Come of Age. 171-194.

soft ontology: coined by Eli Hirsch in 1993, refers to the embracing or reconciling of apparent ontological differences, by means of relevant distinctions and contextual analyses.... as proposed in computer science circles by Aviles et al. (2003), is a definition of a domain in terms of a flexible set of ontological dimensions. It can be regarded as a subclass of ontologies as they are conceived of in computer science, in Gruber's terms (1993) as definitions of conceptualization. Unlike standard ontologies, the approach allows the number of its constitutive concepts to increase or decrease dynamically, any subsets of the ontology to be taken into account at a time, or the order their mutual weight or priority to vary in a graded manner so as to allow different ontological perspectives.  ...The approach is particularly applicable for expert practices that intend to present raw content or data without presenting any authoritative taxonomy or categorization. It also serves to support neutrality for domains such as ethics, politics, aesthetics or philosophy, in which there may not exist a single authorized conceptualization or truth, or it may be instrumental to present a range of perspectives to the domain.  Wikipedia accessed 2018 Oct 17 

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 Related term: semantic relationships

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

tag cloud: Wikipedia

tags, tagging: Wikipedia

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

Taxonomy disambiguation  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  
Taxonomy best practices
 7 taxonomy best practices, David Hillis, CMSWire 2015 
Guidelines for taxonomy design, Drupal 
Taxonomy Design: Best Practices, Zach Wahl 2014

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

Taxonomy, SharePoint 
Managed Metadata 101 webinar 2017 Aug 
Two ways to design SharePoint taxonomy for an organization, Greg Zelfond, 2017 April 
Introduction to Taxonomy for SharePoint, Charmaine Brooks , 2017 April 
Taxonomy SharePoint: Accidental Taxonomist

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

Standard Taxonomies: This page contains an up-to-date listing of XBRL schemas and link bases of standard taxonomies that are supported for the [Securities and Exchange] Commission's Interactive Data programs. 
Taxonomy standards
, Accidental Taxonomist

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  

Terminology Forum, Univ of Vaasa, Finland 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. 

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  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

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

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  
(XML) Topic Maps, XML Cover Pages, Robin Cover, 2008  

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.  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.  

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

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

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.

weak ontology: In computer science, a weak ontology is an ontology that is not sufficiently rigorous to allow software to infer new facts without intervention by humans (the end users of the software system). By this standard – which evolved as artificial intelligence methods became more sophisticated, and computers were used to model high human impact decisions – most databases use weak ontologies. Wikipedia accessed 2018 Oct 17

XML Extensible Markup Language: The universal format for structured documents and data on the Web. W3C, "Extensible Markup Language (XML)"

Ontologies & taxonomies resources 
A DAM Glossary of Common Terms, John Horodysk, 2018   Digital Asset Management, with definitions of controlled vocabulary, governance, metadata, taxonomy, workflow. 
Hedden, Heather, Accidental Taxonomist blog and book, 
Lambe, Patrick, Organising Knowledge: Taxonomies, Knowledge and Organisational effectiveness, Chandos Publishing, 2007.

Linked Data Glossary, W3C, 2013 
Ontology, John F Sowa, 2010 
Ontology Lookup Service, Samples, Phenotypes & Ontologies Team, EMBL-EBI   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  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.

How to look for other unfamiliar terms

IUPAC definitions are reprinted with the permission of the International Union of Pure and Applied Chemistry.

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