Ontology (computing)

In computer science and communication science, an ontology is a formal definition of types, properties, and relationships between entities that actually or fundamentally exist for a particular domain of discourse. It is a practical application of philosophical ontology, with a taxonomy.

An ontology catalogs the variables required for some set of computation and establishes the relationships between them. In the fields of artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, and information architecture, ontologies are created to limit complexity and to organize information. The ontology can then be applied to solve problems.

Etymology and definition

The term ontology has its origins in philosophy and has been applied in many different ways. It comes from onto- from Greek ὤν, ὄντος, (""what one is"), present participle of the verb εἰμί ("to be"). Meaning in computer science is a model for describing the world that consists of a set of types, properties, and relationships between types. It is also expected that what is represented by the model in an ontology is as similar as possible to the real world. (in relation to the object).

Introduction

What many ontologies have in common, both in Computer Science and in Philosophy, is the representation of entities, ideas and events, along with their properties and relationships, according to their categorization system. In both fields there is considerable work on issues relating to ontology (e.g., Quine and Kripke in Philosophy, Sowa and Guarino in Computer Science), and corresponding debates as to whether normative ontology is viable (e.g., debates on fundamentalism in philosophy, and about the Cyc project in artificial intelligence). The difference between the two is in the way they focus. Computer scientists are more concerned with a fixed establishment and controlled vocabularies, while philosophers are more concerned with principles, that is, whether there are such things as a fixed essence or whether entities should ontologically take precedence over processes.

History

Ontologies come from the branch of philosophy known as metaphysics, which is concerned with the nature of reality – of what exists. This branch is concerned with the analysis of various types or modes of existence, often with special attention to the relationships between the particular and the universal, between intrinsic and extrinsic properties, and between essence and existence. The traditional goal of ontological analysis is to divide the world "into sets" to discover those categories or fundamental types in which the objects of the world naturally fall.

During the second half of the 20th century, philosophers exhaustively debated possible methods or approaches for building ontologies without actually being built on any ontology they elaborated. In contrast, computer scientists were building some large and robust ontologies, such as WordNet and Cyc, with debates over how they should be built.

Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that capturing knowledge is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as computational models that allow some degree of automatic reasoning. In the 1980s, the AI community began to use the term ontology to refer to the theory of the modeled world and a component of knowledge systems. Some researchers have been inspired by some philosophical ontologies, seeing a computational ontology as a type of applied philosophy.

In the early 1990s, a highly cited web page and article "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber it was recognized as a deliberate definition of ontology as a technical term in computer science. Gruber introduced the term to refer to a specification of a conceptualization:

An ontology is a description (like a formal specification of a program) of concepts and relationships that can formally exist for an agent or community of agents. This definition is consistent with the use of ontology as a set of conceptual but more general definitions. And this is a different sense of the word ontology used in philosophy.

According to Gruber (1993):

Ontologies are often associated with taxonomic hierarchies of classes, class definition and relationships, but ontologies need not be limited to these forms. Ontologies are not limited to conservative definitions either—that is, definitions in the traditional logical sense that only introduces terminology and does not add any knowledge about the world. To specify a conceptualization, it is necessary to establish axioms that limit possible interpretations for defined terms.

In 1997, Borst made Gruber's definition more specific by stating that an ontology is "a formal specification of a shared conceptualization". Therefore, the ontological conceptualization must express a shared vision among several parties, a consensus rather than an individual point of view. Furthermore, such a conceptualization must be expressed in a formal language so that it can be processed by a computer.

Components

Contemporary ontologies share many structural similarities, regardless of the language in which they were expressed. As mentioned above, most ontologies describe individuals (instances), classes (concepts), attributes, and relationships. This section will discuss each of these components:

The most common components of an ontology are:

• Individuals: instances or objects (the basic or "low level" objects)
• Classes: sets, collections, concepts, classes in programming, types of objects, or types of things.
• Attributes: aspects, properties, traits, characteristics, or parameters that objects (and classes) may have.
• Relations: ways in which classes and individuals can relate to each other.
• Functions: Complex structures formed from a certain relationship that can be used instead of an individual term in a statement
• Restrictions: establish formal descriptions of what should be true with the objective that any assertion may be accepted as entry.
• Rules: Statements in the form of si-then (before-consequent) prayers that describe logical inferences that can be derived from an assertion in a particular form.
• Axioms: assertions (including rules) in a logical way that together include all the theory that ontology describes in its domain of application. This definition is different from the “axiomas” in generated grammars and logical form. In these disciplines, axioms only include specified statements as a knowledge a priori. In ontologies, "axiomas" also include theories derived from axiomatic statements.
• Events: changes in attributes or relationships.

Ontologies are often coded using ontology languages.

Types

Domain Ontologies

The domain ontology (or domain-specific ontology) represents concepts that belong to a part of the world. The particular meaning of a term applied to that domain is provided by the domain of the ontology. For example, the word card has many meanings. An ontology about the domain bank could model the meaning to "credit card", while an ontology about the domain of computer hardware could model the concepts to "network card" and "graphics card".

Since concept ontologies represent concepts in very specific ways, they are usually highly incompatible. As systems that depend on expanded domain ontologies, they usually need to blend domain ontologies into a more general representation. This represents a challenge for the design of an ontology. Different ontologies in the same domain are made in different languages, different intents to use the ontology, and different perceptions of the domain (based on cultural background, education, ideology, etc.).

Currently, merging ontologies that are not built from a common basic ontology is a very expensive and lengthy manual process. Domain ontologies using the same basic ontology which provides a set of basic elements with which to specify the meaning of domain ontology elements can be automatically merged. There are studies in generalized techniques for mixing ontologies, but this area remains highly theoretical.

General Ontologies

Represent general concepts that are not specific to a domain. For example, ontologies about time, ontologies of behavior, of causality, etc. They can be reused across different domains.

Task ontology

They provide the vocabulary to describe terms involved in problem solving processes which may be related to similar tasks in the same or different domains. They include nouns, verbs, phrases, and adjectives related to the task (“goal,” “plan,” “assign,” “classify,” etc.).

Terminology Ontology

They specify the terms that are used to represent knowledge in the universe of discourse. They are usually used to unify vocabulary in a certain domain (lexical and non-semantic content). Also known as linguistic ontologies.

Information ontology

Specify the storage structure of databases. They offer a framework for the standardized storage of information (structure of the records of a DB).

Knowledge modeling ontology

They specify conceptualizations of knowledge. They have a rich internal structure and are often tailored to the particular use of the knowledge they describe (terms and semantics).

Display

A study of ontology visualization techniques is presented by Katifori et al. An evaluation of the two most widely used ontology visualization techniques: trees and graphs is discussed in. A visual language for ontologies represented in OWL is specified by Visual Notation for OWL Ontologies (VOWL).

Engineering

Ontology engineering (or ontology construction) is a branch of knowledge engineering. It studies the ontology development process, its life cycle, the methods and methodologies for building ontologies, as well as the tools and languages that support them.

Ontology engineering aims to make content explicit within software applications, and within business procedures and enterprises for a particular domain. Ontology engineering offers a direction towards solving interpretive problems brought about by semantic obstacles, such as those related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies in a specific domain.

Learning

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting domain terms from a natural language text. Since manually building an ontology is an intensely complex and time consuming task, there is a motivation to automate the process. Information extraction and data mining methods have been exploited to automatically match ontologies with documents, eg. In the context of the BioCreative challenges.

Languages

An ontology language is a formal language used to encode an ontology. There are a large number of such languages:

• Common Algebraic Specification Language is a general specification language based on logic developed within the IFIP 1.3 "Foundations of System Specifications" working group and functions as a standard within the software specifications area. It is now being applied to the specification of ontologies with the aim of obtaining structured and modulated mechanisms.
• Common logic is ISO standard 24707, a specification for a family of ontology languages that can be perfectly translated from each other.
• The Cyc project has its own language called CycL, based on calculation of first order preaching with others of a higher order.
• DOGMA (Developing Ontology-Grounded Methods and Applications) adopts modeling technique to have a higher level of semantic stability,
• Gellish language includes rules for its own extension and integrates an ontology with an ontology language.
• IDEF5 is a software engineering method to develop and maintain usable and reliable domain ontologies.
• KIF is a syntax for first order logic based on S-Expressions.
• MOF and UML are OMG sedanres
• Olog is a theoretical categorization method for ontologies, emphasizing translations between ontologies using functors.
• OBO, a language used for biological and biomedical ontologies.
• OntoUML is an ontologically well-founded profile of UML for conceptual modeling of domain ontologies.
• OWL is a language to make ontological statements, developed as a follow-up to RDF and RDFS. OWL is trying to be used on the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs.
• Rule Interchange Format (RIF) and F-Logic combine ontologies and rules.
• Semantic Application Design Language (SADL) captures a subset of OWL expressions, using a language similar to English introduced via plug-in in Eclipse.
• SBVR (Semantics of Business Vocabularies and Rules) is a OMG standard adopted in the industry to build ontologies.
• TOVE Project, TOronto Virtual Enterprise project.

Published Examples

• BabelNet, a very large ontology and multilingual semantic network, with lexicon in many languages.
• Basic Formal Ontology, an ontology designed to support scientific research.
• BioPAX, an ontology for the exchange of ontology for the exchange and interoperability of biological path data (cellular process).
• BMO, a model ontology based on analysis of interpreted ontologies and business model literature.
• CCO and GexKB, Application Ontologies (APO) that integrate various types of knowledge with the Cell Cycle Ontology (CCO) and the Gene Expression Knowledge Base (GexKB).
• CContology (Customer Complaint Ontology), an e-business ontology that supports the management of complaints from customers online.
• CIDOC Conceptual Reference Model, an ontology for cultural heritage
• COSMO, a Foundation Ontology (the current version is in OWL) that is designed to contain representation of all primitive concepts needed to logically specify the meaning of any domain. It is an attempt to serve as a basic ontology that can be used to translate between representations in other ontologies or databases. It began as a mixture of basic elements of OpenCyc and SUMO ontologies, and has been supplemented with other elements of ontologies as well as including representations of all words in the Longman dictionary defining vocabulary.
• Cyc, a large Foundation Ontology for formal representation of the universe of discourse.
• Disease Ontology, designed to facilitate the recognition of diseases and conditions associated with a particular medical code.
• DOLCE, a descriptive ontology for linguistic and cognitive engineering
• Dublin Core, a simple ontology for documents and publications
• Foundational, Core and Linguistic Ontologies
• Foundational Model of Anatomy, an ontology for human anatomy.
• Friend of a Friend, an ontology that describes people, their activities and their relationships with other people and objects.
• Gene Ontology for genomes.
• Gellish English dictionary, an ontology that includes a dictionary and a taxonomy that focus on applications in industry, engineering business, technology, etc. See also Gellish open source project in SourceForge.
• Geopolitical ontology, an ontology that describes geopolitical information created by FAO. Geopolitical ontology includes names in different languages (Spanish, French, Spanish, Arabic, Chinese, Russian and Italian); with systems codes (UN, ISO, FAOSTAT, AGROVOC, etc.); facilitating a relationship between territories (borders, membership groups, etc.); and locating historical changes. In addition, FAO provides web services ≤ https://web.archive.org/web/20110517082103/http://www.fao.org/countryprofiles/webservices.asp?lang=en/2005 de ontología geopolitics y construcción de Modules ≤ http://www.fao.org/countryprofiles/geoinfo/modulemaker/index.html See more information at FAO Country Profiles on the web page ≤ http://www.fao.org/countryprofiles/geoinfo.asp?lang=en español.
• GOLD, General Ontology for Linguistic Description.
• GUM (Generalized Upper Model), an ontology linguistically motivated by intervention between client systems and natural language technologies.
• IDEAS Group, a formal ontology for interpreted architectures being developed by defense departments of Australia, Canada and the United States.
• Linkbase, a formal representation of a biomedical domain, founded on Basic Formal Ontology.
• LPL, Lawson Pattern Language
• NCBO Bioportal, Biomedical and biological ontologies with tools for search, navigation and visualization.
• Neuroscience Information Framework NIFSTD ontologies: a modular set of domain ontologies related to neuroscience. See http://neuinfo.org Archived on 25 January 2007 at Wayback Machine.
• OBO-Edit, a mostly biologic and biomedical ontology search engine.
• OBO Foundry, a suite of interoperable ontological references in biology and medicine.
• OMNIBUS Ontology, an ontology of learning, instruction and design.
• Ontology for Biomedical Investigations, open access software, integrated ontology for the description of biological and clinical research.
• ONSTR, Ontology for Newborn Screening Follow-up and Translational Research [4], Newborn Screening Follow-up Data Integration Collaborative, Emory University, Atlanta, GA. See also https://web.archive.org/web/201508020529/https://nbsdc.org/projectmission.php
• Plant Ontology for plant structures and growth/development states, etc.
• POPE, Purdue Ontology for Pharmaceutical Engineering.
• PRO, the Protein Ontology of the Protein Information Resource, Georgetown University
• Abstraction programs of taxonomy abstraction taxonomy
• Protein Ontology
• RXNO Ontology, for reaction names in chemistry.
• SNOMED CT (Systematized No.menclature of Medicine -- Clinical Terms)
• Suggested Upper Merged Ontology
• Systems Biology Ontology (SBO), for computer models in Biology.
• SWEET, Semantic Web for Earth and Environmental Terminology
• Ontología ThoughtTreasure
• TIME-ITEM, Topics for Indexing Medical Education
• Uberon represents animal anatomical structures.
• UMBEL, a light-reference structure of 20,000 themes, concepts, classes and their relationships derived from OpenCyc
• WordNet, a lexical reference system.
• YAMATO, Yet Another More Advanced Top-level Ontology

Libraries

The development of ontologies for the web has led to the emergence of services providing searchable lists or directories of ontologies. Such directories have been called ontology libraries. Some examples:

• COLORE is an open repository of first order ontologies in the Common Logic with formal links between ontologies in the repository.
• DAML Ontology Library maintains a legacy of DAML ontologies.
• Ontology Design Patterns portal is a wiki repository of reusable components and practices for the design of ontologies, and also maintains a list of “exemplary ontologies”. Started within the EU NeOn project.
• Protégé Ontology Library contains a set of OWL ontologies, based on structures and other formats.
• SchemaWeb is a directory of RDF schemes expressed in RDFS, OWL and DAML+OIL.

The following are directories and search engines at the same time. They include search with crawlers.

• OBO Foundry is a suite of interoperable ontology references in biology and biomedicine.
• Bioportal (NCBO repository)
• OntoSelect Ontology Library offering similar services for RDF/S ontologies, DAML and OWL.
• Ontaria is a “buscable” and “navegable” directory of data on the Semantic Web, focused on RDF vocabulary and OWL ontologies.
• Swoogle is a directory and search engine for all available RDF resources on the Web, including ontologies.
• OOR - the Open Ontology Repository initiative - http://oor.net
• ROMULUS is a foundational repository of ontologies designed to improve interoperability in semantics. There are currently 3 foundational ontologies in the repository: DOLCE, BFO and GFO.

Example of your application

• In general, ontologies can be used beneficially in interpreted applications A more concrete example is SAPPHIRE (medical insurance) or Situational Awareness and Preparedness for Public Health Incidences and Reasoning Engines which is a semantic-based medical information system, trained to follow and evaluate situations that may affect public health.

Criticism

Werner Ceusters has noted the confusion caused by the different meanings of the word ontology when used in philosophy compared to the use of the word in computer science, and advocates great precision in the use of the word by scholars. that members of various disciplines use various definitions of the word. He wrote: "Before answering the question 'what is an ontology?', one should first answer the question 'what is an ontology?' what does the word ontology mean?'. It is also not very clear to us how ontologies fit into NoSQL databases.

Recommended reading

• Oberle, D., Guarino, N., " Staab, S. (2009) What is an ontology? Archived on 30 May 2013 in Wayback Machine.. In: "Handbook on Ontologies". Springer, 2nd edition, 2009.
• Fensel, D., van Harmelen, F., Horrocks, I., McGuinness, D.L., " Patel-Schneider, P. F. (2001). "OIL: an ontology infrastructure for the Semantic Web." In: Intelligent Systems. IEEE, 16(2): 38–45.
• Gangemi A., Presutti V. (2009). Ontology Design Patterns (breakable link available on the Internet Archive; see history, first version and last).. In Staab S. et al. (eds.): Handbook on Ontologies (2nd edition), Springer, 2009.
• Maria Golemati, Akrivi Katifori, Costas Vassilakis, George Lepouras, Constantin Halatsis (2007). "Creating an Ontology for the User Profile: Method and Applications". In: Proceedings of the First IEEE International Conference on Research Challenges in Information Science (RCIS), Morocco 2007.
• Mizoguchi, R. (2004). "Tutorial on ontological engineering: part 3: Advanced course of ontological engineering" Archived on March 9, 2013 at Wayback Machine.. In: New Generation Computing. Ohmsha & Springer-Verlag, 22(2):198-220.
• Gruber, T.R. 1993. "A translation approach to portable ontology specifications". In: Knowledge Acquisition5: 199–199.
• Maedche, A. " Staab, S. (2001). "Ontology learning for the Semantic Web." In: Intelligent Systems. IEEE, 16(2): 72–79.
• Natalya F. Noy and Deborah L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.
• Prabath Chaminda Abeysiriwardana, Saluka R Kodituwakku, "Ontology Based Information Extraction for Disease Intelligence". International Journal of Research in Computer Science, 2 (6): pp. 7–19, November 2012. doi:10.7815/ijorcs.26.2012.051
• Razmerita, L., Angehrn, A., & Maedche, A. 2003. "Ontology-Based User Modeling for Knowledge Management Systems" (breakable link available on the Internet Archive; see history, first version and last).. In: Lecture Notes in Computer Science: 213-17.
• Soylu, A., Causmaecker, Patrick. 2009.Merging model driven and ontology driven system development approaches pervasive computing perspective. in Proc 24th Intl Symposium on Computer and Information Sciences. pp 730-735.
• Smith, B. Ontology (Science), in C. Eschenbach and M. Gruninger (eds.), Formal Ontology in Information Systems. Proceedings of FOIS 2008, Amsterdam/New York: ISO Press, 21–35.
• Uschold, Mike & Gruninger, M. (1996). Ontologies: Principles, Methods and Applications. Knowledge Engineering Review, 11(2).
• W. Pidcock, What are the differences between a vocabulary, a taxonomy, a thesaurus, an ontology, and a meta-model?
• Yudelson, M., Gavrilova, T., & Brusilovsky, P. 2005. Towards User Modeling Meta-ontology. Lecture Notes in Computer Science, 3538: 448.
• Movshovitz-Attias, Dana and Cohen, William W. (2012) Bootstrapping Biomedical Ontologies for Scientific Text using NELL. BioNLP in NAACL, Association for Computational Linguistics, 2012.