Ontology (computer science)

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In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.

Contents

Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge. Each uses ontological assumptions to frame explicit theories, research and applications. New ontologies may improve problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. [1] For instance, the definition and ontology of economics is a primary concern in Marxist economics, [2] but also in other subfields of economics. [3] An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management).

What ontologies in both information science and philosophy have in common is the attempt to represent entities, ideas and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in computer science), [4] and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence).

Applied ontology is considered a successor to prior work in philosophy, however many current efforts are more concerned with establishing controlled vocabularies of narrow domains than first principles, the existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI. [5]

Etymology

The compound word ontology combines onto-, from the Greek ὄν , on (gen. ὄντος, ontos), i.e. "being; that which is", which is the present participle of the verb εἰμί, eimí, i.e. "to be, I am", and -λογία, -logia , i.e. "logical discourse", see classical compounds for this type of word formation. [6] [7]

While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius).

The first occurrence in English of ontology as recorded by the OED ( Oxford English Dictionary , online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy by Gideon Harvey.

History

Ontologies arise out of the branch of philosophy known as metaphysics, which deals with questions like "what exists?" and "what is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Metaphysics has been an ongoing topic of discussion since recorded history.

Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems[ citation needed ]. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, [8] [9] [10] publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings. [11] Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. [12]

In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber [13] used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization:

An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy. [14]

Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance, Gruber stated (1993):

Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions  — that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world. [15] To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. [16]

As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity." [17]

Components

Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes and relations. In this section each of these components is discussed in turn.

Common components of ontologies include:

Individuals
Instances or objects (the basic or "ground level" objects)
Classes
Sets, collections, concepts, classes in programming, types of objects or kinds of things
Attributes
Aspects, properties, features, characteristics or parameters that objects (and classes) can have
Relations
Ways in which classes and individuals can be related to one another
Function terms
Complex structures formed from certain relations that can be used in place of an individual term in a statement
Restrictions
Formally stated descriptions of what must be true in order for some assertion to be accepted as input
Rules
Statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form
Axioms
Assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In those disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements
Events
The changing of attributes or relations

Ontologies are commonly encoded using ontology languages.

Types

Domain ontology

A domain ontology (or domain-specific ontology) represents concepts which belong to a realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings.

Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.)[ citation needed ].

At present, merging ontologies that are not developed from a common upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effort. There are studies on generalized techniques for merging ontologies, [18] but this area of research is still ongoing, and it is a recent event to see the issue sidestepped by having multiple domain ontologies using the same upper ontology like the OBO Foundry.

Upper ontology

An upper ontology (or foundation ontology) is a model of the commonly shared relations and objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that overarches the terms and associated object descriptions as they are used in various relevant domain ontologies.

Standardized upper ontologies available for use include BFO, BORO method, Dublin Core, GFO, Cyc, SUMO, UMBEL, the Unified Foundational Ontology (UFO), [19] and DOLCE. [20] [21] WordNet has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontologies. [22]

Hybrid ontology

The Gellish ontology is an example of a combination of an upper and a domain ontology.

Visualization

A survey of ontology visualization methods is presented by Katifori et al. [23] An updated survey of ontology visualization methods and tools was published by Dudás et al. [24] The most established ontology visualization methods, namely indented tree and graph visualization are evaluated by Fu et al. [25] A visual language for ontologies represented in OWL is specified by the Visual Notation for OWL Ontologies (VOWL). [26]

Engineering

Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain. [27] It is a subfield of knowledge engineering that studies the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tools and languages that support them. [28] [29]

Ontology engineering aims to make explicit the knowledge contained in software applications, and organizational procedures for a particular domain. Ontology engineering offers a direction for overcoming semantic obstacles, such as those related to the definitions of business terms and software classes. Known challenges with ontology engineering include:

  1. Ensuring the ontology is current with domain knowledge and term use
  2. Providing sufficient specificity and concept coverage for the domain of interest, thus minimizing the content completeness problem
  3. Ensuring the ontology can support its use cases

Editors

Ontology editors are applications designed to assist in the creation or manipulation of ontologies. It is common for ontology editors to use one or more ontology languages.

Aspects of ontology editors include: visual navigation possibilities within the knowledge model, inference engines and information extraction; support for modules; the import and export of foreign knowledge representation languages for ontology matching; and the support of meta-ontologies such as OWL-S, Dublin Core, etc. [30]

Learning

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. Information extraction and text mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges. [31]

Research

Epistemological assumptions, which in research asks "What do you know? or "How do you know it?", creates the foundation researchers use when approaching a certain topic or area for potential research. As epistemology is directly linked to knowledge and how we come about accepting certain truths, individuals conducting academic research must understand what allows them to begin theory building. Simply, epistemological assumptions force researchers to question how they arrive at the knowledge they have.[ citation needed ]

Languages

An ontology language is a formal language used to encode an ontology. There are a number of such languages for ontologies, both proprietary and standards-based:

Published examples

The W3C Linking Open Data community project coordinates attempts to converge different ontologies into worldwide Semantic Web.

Libraries

The development of ontologies has led to the emergence of services providing lists or directories of ontologies called ontology libraries.

The following are libraries of human-selected ontologies.

The following are both directories and search engines.

Examples of applications

In general, ontologies can be used beneficially in several fields.

See also

Related philosophical concepts

Related Research Articles

<span class="mw-page-title-main">Cyc</span> Artificial intelligence project

Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge that other AI platforms may take for granted. This is contrasted with facts one might find somewhere on the internet or retrieve via a search engine or Wikipedia. Cyc enables semantic reasoners to perform human-like reasoning and be less "brittle" when confronted with novel situations.

<span class="mw-page-title-main">Knowledge representation and reasoning</span> Field of artificial intelligence

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors.

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure Programing language.

In information science, an upper ontology is an ontology which consists of very general terms 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, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

In computer science and artificial intelligence, ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. Ontology languages are usually declarative languages, are almost always generalizations of frame languages, and are commonly based on either first-order logic or on description logic.

Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information, documents of all sorts, contacts, search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value.

<span class="mw-page-title-main">Semantic technology</span> Technology to help machines understand data

The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF and OWL. These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources.

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.

Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.

Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets.

The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. The Social Semantic Web can be seen as a Web of collective knowledge systems, which are able to provide useful information based on human contributions and which get better as more people participate. The Social Semantic Web combines technologies, strategies and methodologies from the Semantic Web, social software and the Web 2.0.

Machine interpretation of documents and services in Semantic Web environment is primarily enabled by (a) the capability to mark documents, document segments and services with semantic tags and (b) the ability to establish contextual relations between the tags with a domain model, which is formally represented as ontology. Human beings use natural languages to communicate an abstract view of the world. Natural language constructs are symbolic representations of human experience and are close to the conceptual model that Semantic Web technologies deal with. Thus, natural language constructs have been naturally used to represent the ontology elements. This makes it convenient to apply Semantic Web technologies in the domain of textual information. In contrast, multimedia documents are perceptual recording of human experience. An attempt to use a conceptual model to interpret the perceptual records gets severely impaired by the semantic gap that exists between the perceptual media features and the conceptual world. Notably, the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual world is rather artificial. The key to semantic processing of multimedia data lies in harmonizing the seemingly isolated conceptual and the perceptual worlds. Representation of the Domain knowledge needs to be extended to enable perceptual modeling, over and above conceptual modeling that is supported. The perceptual model of a domain primarily comprises observable media properties of the concepts. Such perceptual models are useful for semantic interpretation of media documents, just as the conceptual models help in the semantic interpretation of textual documents.

<span class="mw-page-title-main">Ontology engineering</span> Field which studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

<span class="mw-page-title-main">Conceptualization (information science)</span> Abstract simplified view of selected part(s) of the world

In information science a conceptualization is an abstract simplified view of some selected part of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them. An explicit specification of a conceptualization is an ontology, and it may occur that a conceptualization can be realized by several distinct ontologies. An ontological commitment in describing ontological comparisons is taken to refer to that subset of elements of an ontology shared with all the others. "An ontology is language-dependent", its objects and interrelations described within the language it uses, while a conceptualization is always the same, more general, its concepts existing "independently of the language used to describe it". The relation between these terms is shown in the figure to the right.

<span class="mw-page-title-main">UMBEL</span>

UMBEL is a logically organized knowledge graph of 34,000 concepts and entity types that can be used in information science for relating information from disparate sources to one another. It was retired at the end of 2019. UMBEL was first released in July 2008. Version 1.00 was released in February 2011. Its current release is version 1.50.

A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. As its name indicates, a triple is a set of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions.

References

  1. ↑ G Budin (2005), "Ontology-driven translation management", in Helle V. Dam (ed.), Knowledge Systems and Translation, Jan Engberg, Heidrun Gerzymisch-Arbogast, Walter de Gruyter, p. 113, ISBN   978-3-11-018297-2
  2. ↑ Palermo, Giulio (10 January 2007). "The ontology of economic power in capitalism: mainstream economics and Marx". Cambridge Journal of Economics. 31 (4): 539–561. doi:10.1093/cje/bel036 – via Oxford Journals.
  3. ↑ Zuniga, Gloria L. (1999-02-02). "An Ontology Of Economic Objects". Mpra Paper. Research Division of the Federal Reserve Bank of St. Louis. Retrieved 2013-06-16.
  4. ↑ Sowa, J. F. (1995). "Top-level ontological categories". International Journal of Human-Computer Studies. 43 (5–6 (November/December)): 669–85. doi:10.1006/ijhc.1995.1068.
  5. ↑ Musen, Mark (2015). "The Protégé Project: A Look Back and a Look Forward". AI Matters. 1 (4): 4–12. doi:10.1145/2757001.2757003. PMC   4883684 . PMID   27239556.
  6. ↑ "ontology". Online Etymology Dictionary .
  7. ↑ εἰμί . Liddell, Henry George ; Scott, Robert ; A Greek–English Lexicon at the Perseus Project
  8. ↑ Powers, David (1983). "Robot Intelligence". Electronics Today International.
  9. ↑ Powers, David (1984). "Natural Language the Natural Way". Computer Compacts. 2 (3–4): 100–109. doi:10.1016/0167-7136(84)90088-X.
  10. ↑ Powers, David; Turk, Chris (1989). Machine Learning of Natural Language. Springer-Verlag. ISBN   978-1-4471-1697-4.
  11. ↑ Powers, David (1991). Preface: Goals, Issues and Directions in Machine Learning of Natural Language and Ontology. AAAI Spring Symposium on Machine Learning of Natural Language and Ontology. DFKI.
  12. ↑ Gruber, T. (2008). "Ontology". In Liu, Ling; Özsu, M. Tamer (eds.). Encyclopedia of Database Systems. Springer-Verlag. ISBN   978-0-387-49616-0.
  13. ↑ Gruber, T. (1993). "Toward Principles for the Design of Ontologies Used for Knowledge Sharing". International Journal of Human-Computer Studies. 43 (5–6): 907–928. doi:10.1006/ijhc.1995.1081. S2CID   1652449.
  14. ↑ Gruber, T. (2001). "What is an Ontology?". Stanford University. Archived from the original on 2010-07-16. Retrieved 2009-11-09.
  15. ↑ Enderton, H. B. (1972-05-12). A Mathematical Introduction to Logic (1 ed.). San Diego, CA: Academic Press. p.  295. ISBN   978-0-12-238450-92nd edition; January 5, 2001{{cite book}}: CS1 maint: postscript (link)
  16. ↑ Gruber, Thomas R. (June 1993). "A translation approach to portable ontology specifications" (PDF). Knowledge Acquisition . 5 (2): 199–220. CiteSeerX   10.1.1.101.7493 . doi:10.1006/knac.1993.1008. S2CID   15709015.
  17. ↑ Feilmayr, Christina; Wöß, Wolfram (2016). "An analysis of ontologies and their success factors for application to business". Data & Knowledge Engineering. 101: 1–23. doi:10.1016/j.datak.2015.11.003.
  18. ↑ "Project: Dynamic Ontology Repair". University of Edinburgh Department of Informatics. Retrieved 2 January 2012.
  19. ↑ Giancarlo Guizzardi & Gerd Wagner. "A Unified Foundational Ontology and some Applications of it in Business Modeling" (PDF). Retrieved 31 March 2014.
  20. 1 2 "Laboratory for Applied Ontology - DOLCE". Laboratory for Applied Ontology (LOA). Retrieved 10 February 2011.
  21. 1 2 "OWL version of DOLCE+DnS". Semantic Technology Lab. Retrieved 21 February 2013.
  22. ↑ Navigli, Roberto; Velardi, Paola (2004). "Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites". Computational Linguistics . MIT Press. 30 (2): 151–179. CiteSeerX   10.1.1.329.6965 . doi:10.1162/089120104323093276. S2CID   2453822.
  23. ↑ Katifori, A.; Halatsis, C.; Lepouras, G.; Vassilakis, C.; Giannopoulou, E. (2007). "Ontology Visualization Methods - A Survey" (PDF). ACM Computing Surveys. 39 (4): 10. CiteSeerX   10.1.1.330.3281 . doi:10.1145/1287620.1287621. S2CID   14205872. Archived from the original (PDF) on 4 March 2016.
  24. ↑ Dudás, M.; Lohmann, S.; Svátek, V.; Pavlov, D. (2018). "Ontology Visualization Methods and Tools: a Survey of the State of the Art". Knowledge Engineering Review. 33 (e10). doi:10.1017/S0269888918000073. S2CID   52965103.
  25. ↑ Fu, Bo; Noy, Natalya F.; Storey, Margaret-Anne (2013). "Indented Tree or Graph? A Usability Study of Ontology Visualization Techniques in the Context of Class Mapping Evaluation". The Semantic Web – ISWC 2013: 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21–25, 2013, Proceedings, Part I. Lecture Notes in Computer Science. Vol. 8218. Berlin: Springer. pp. 117–134. doi: 10.1007/978-3-642-41335-3_8 . ISBN   978-3-642-41335-3.
  26. ↑ Negru, Stefan; Lohmann, Steffen; Haag, Florian (7 April 2014). "VOWL: Visual Notation for OWL Ontologies: Specification of Version 2.0". Visual Data Web.
  27. ↑ Pouchard, Line; Ivezic, Nenad; Schlenoff, Craig (March 2000). "Ontology Engineering for Distributed Collaboration in Manufacturing" (PDF). Proceedings of the AIS2000 Conference.
  28. ↑ Gómez-Pérez, Ascunion; Fernández-López, Mariano; Corcho, Oscar (2004). Ontological Engineering: With Examples from the Areas of Knowledge Management, E-commerce and the Semantic Web (1 ed.). Springer. p.  403. ISBN   978-1-85233-551-9.
  29. ↑ De Nicola, Antonio; Missikoff, Michele; Navigli, Roberto (2009). "A Software Engineering Approach to Ontology Building" (PDF). Information Systems . Elsevier. 34 (2): 258–275. doi:10.1016/j.is.2008.07.002.
  30. ↑ Alatrish, Emhimed (2013). "A comparison of some ontology editors" (PDF). Management Information Systems. 8 (2): 18–24.
  31. ↑ Krallinger, M; Leitner, F; Vazquez, M; Salgado, D; Marcelle, C; Tyers, M; Valencia, A; Chatr-Aryamontri, A (2012). "How to link ontologies and protein-protein interactions to literature: Text-mining approaches and the Bio Creative experience". Database. 2012: bas017. doi:10.1093/database/bas017. PMC   3309177 . PMID   22438567.
  32. ↑ "SADL". SourceForge . Retrieved 10 February 2011.
  33. ↑ "AURUM - Information Security Ontology" . Retrieved 29 January 2016.
  34. ↑ "Basic Formal Ontology (BFO)". Institute for Formal Ontology and Medical Information Science (IFOMIS).
  35. ↑ "BioPAX" . Retrieved 10 February 2011.
  36. ↑ Osterwalder, Alexander; Pigneur, Yves (June 17–19, 2002). "An e-Business Model Ontology for Modeling e-Business" (PDF). 15th Bled eConference, Slovenia. Archived from the original (PDF) on 2011-04-19.{{cite journal}}: Cite journal requires |journal= (help)CS1 maint: location (link)
  37. ↑ Upward, Antony; Jones, Peter (2016). "An Ontology for Strongly Sustainable Business Models: Defining an Enterprise Framework Compatible with Natural and Social Science". Organization & Environment. 29 (1): 97–123. doi:10.1177/1086026615592933. S2CID   145089240.
  38. ↑ "About CCO and GexKB". Semantic Systems Biology. Archived from the original on 2012-07-30.
  39. ↑ "CContology" . Retrieved 10 February 2011.
  40. ↑ "The CIDOC Conceptual Reference Model (CRM)" . Retrieved 10 February 2011.
  41. ↑ "COSMO". MICRA Inc. Retrieved 10 February 2011.
  42. ↑ Osborne, JD; Flatow, J; Holko, M; Lin, SM; Kibbe, WA; Zhu, LJ; Danila, MI; Feng, G; Chisholm, RL (2009). "Annotating the human genome with Disease Ontology". BMC Genomics. 10 Suppl 1 (Suppl 1): S6. doi:10.1186/1471-2164-10-S1-S6. PMC   2709267 . PMID   19594883.
  43. ↑ Damiano, Rossana; Lombardo, Vincenzo; Pizzo, Antonio (2005). Subsol, Gérard (ed.). "Formal Encoding of Drama Ontology" (PDF). Virtual Storytelling. Using Virtual Reality Technologies for Storytelling. Lecture Notes in Computer Science. Springer Berlin Heidelberg. 3805: 95–104. doi:10.1007/11590361_11. hdl: 2318/148510 . ISBN   9783540322856.
  44. ↑ "Financial Industry Business Ontology (FIBO)" . Retrieved 15 March 2017.
  45. ↑ "Foundational, Core and Linguistic Ontologies" . Retrieved 10 February 2011.
  46. ↑ "Foundational Model of Anatomy" . Retrieved 10 February 2011.
  47. ↑ "Car Extension" . Retrieved 15 June 2017.
  48. ↑ "GOLD" . Retrieved 10 February 2011.
  49. ↑ "Generalized Upper Model" . Retrieved 10 February 2011.
  50. ↑ "The IDEAS Group Website". Archived from the original on 16 December 2018. Retrieved 10 February 2011.
  51. ↑ "Linkbase". Archived from the original on 18 September 2008. Retrieved 10 February 2011.
  52. ↑ Configuration Console Reference Guide: Landmark Pattern Language (LPL). Retrieved 4 February 2020.
  53. ↑ "Bioportal". National Center for Biological Ontology (NCBO). Archived from the original on 2009-06-12. Retrieved 2013-10-24.
  54. ↑ "Ontology browser for most of the Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP).
  55. ↑ "The Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP).
  56. ↑ "OMNIBUS Ontology". Archived from the original on 19 July 2012. Retrieved 10 February 2011.
  57. ↑ "ONSTR". Archived from the original on 16 April 2014. Retrieved 16 April 2014.
  58. ↑ "Plant Ontology" . Retrieved 10 February 2011.
  59. ↑ "PRO". Archived from the original on 10 March 2011. Retrieved 10 February 2011.
  60. ↑ "ProbOnto" . Retrieved 1 July 2017.
  61. ↑ Swat, MJ; Grenon, P; Wimalaratne, S (2016). "ProbOnto: ontology and knowledge base of probability distributions". Bioinformatics. 32 (17): 2719–21. doi:10.1093/bioinformatics/btw170. PMC   5013898 . PMID   27153608.
  62. ↑ "Protein Ontology". Archived from the original on 10 March 2011. Retrieved 10 February 2011.
  63. ↑ Nembaware, Victoria; Mazandu, Gaston K.; Hotchkiss, Jade; Safari Serufuri, Jean-Michel; Kent, Jill; Kengne, Andre Pascal; Anie, Kofi; Munung, Nchangwi Syntia; Bukini, Daima; Bitoungui, Valentina Josiane Ngo; Munube, Deogratias (2020-10-01). "The Sickle Cell Disease Ontology: Enabling Collaborative Research and Co-Designing of New Planetary Health Applications". OMICS: A Journal of Integrative Biology. 24 (10): 559–567. doi:10.1089/omi.2020.0153. PMC   7549008 . PMID   33021900.
  64. ↑ Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M (2005). "The Sequence Ontology: a tool for the unification of genome annotations". Genome Biology. 6 (5): R44. doi:10.1186/gb-2005-6-5-r44. PMC   1175956 . PMID   15892872.
  65. ↑ Niles, I., & Pease, A., (2001), Toward a Standard Upper Ontology, in Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS-2001), Chris Welty and Barry Smith, eds, pp2-9.
  66. ↑ "SWEET" . Retrieved 11 March 2022.
  67. ↑ "SSN" . Retrieved 10 November 2021.
  68. ↑ Mungall, CJ; Torniai, C; Gkoutos, GV; Lewis, SE; Haendel, MA (2012). "Uberon, an integrative multi-species anatomy ontology". Genome Biol. 13 (1): R5. doi:10.1186/gb-2012-13-1-r5. PMC   3334586 . PMID   22293552.
  69. ↑ "YAMATO". Archived from the original on 3 March 2011. Retrieved 10 February 2011.
  70. ↑ "COLORE" . Retrieved 4 May 2011.
  71. ↑ "DAML Ontology Library" . Retrieved 10 February 2011.
  72. ↑ "ODP Library" . Retrieved 21 February 2013.
  73. ↑ "Protege Ontology Library" . Retrieved 10 February 2011.
  74. ↑ "SchemaWeb". Archived from the original on 10 August 2011. Retrieved 10 February 2011.
  75. ↑ "OBO Foundry" . Retrieved 10 February 2011.
  76. ↑ Smith, B.; Ashburner, M.; Rosse, C.; Bard, J.; Bug, W.; Ceusters, W.; Goldberg, L. J.; Eilbeck, K.; Ireland, A.; Mungall, C. J.; Leontis, N.; Rocca-Serra, P.; Ruttenberg, A.; Sansone, S. A.; Scheuermann, R. H.; Shah, N.; Whetzel, P. L.; Lewis, S. (2007). "The OBO Foundry: Coordinated evolution of ontologies to support biomedical data integration". Nature Biotechnology . 25 (11): 1251–1255. doi:10.1038/nbt1346. PMC   2814061 . PMID   17989687. Open Access logo PLoS transparent.svg
  77. ↑ "Welcome to the NCBO BioPortal | NCBO BioPortal". bioportal.bioontology.org. Retrieved 2019-03-28.
  78. ↑ "OntoSelect". Archived from the original on 11 November 2010. Retrieved 10 February 2011.
  79. ↑ "Ontaria" . Retrieved 10 February 2011.
  80. ↑ "OpenOntologyRepository - OntologPSMW". ontologforum.org. Retrieved 2019-03-28.
  81. ↑ Oberle, Daniel (2014). "How ontologies benefit enterprise applications" (PDF). Semantic Web. IOS Press. 5 (6): 473–491. doi:10.3233/SW-130114.
  82. ↑ Frank, Andrew U. (2001). "Tiers of ontology and consistency constraints in geographical information systems". International Journal of Geographical Information Science. 15 (7): 667–678. doi:10.1080/13658810110061144. S2CID   6616354.
  83. ↑ Stevenson, Mark; Guo, Yikun (2010). "Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus". Journal of Biomedical Informatics. 43 (5): 762–773. doi: 10.1016/j.jbi.2010.06.001 . PMID   20541624.
  84. ↑ BODENREIDER O; MITCHELL JA; MCCRAY AT (2005). "Biomedical Ontologies". Pac Symp Biocomput: 76–78. doi:10.1142/9789812704856_0016. ISBN   978-981-238-598-7. PMC   4300097 . PMID   15759615.

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