Ininformation science, anontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or alldomains 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 terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to asapplied ontology.[1]
Everyacademic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain,interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain acontrolled vocabulary ofjargon between each of their languages.[2] For instance, thedefinition and ontology of economics is a primary concern inMarxist economics,[3] but also in othersubfields of economics.[4] 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 whatcapital assets are at risk and by how much (seerisk management).
What ontologies in bothinformation science andphilosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems ofontology engineering (e.g.,Quine andKripke in philosophy,Sowa andGuarino in information science),[5] and debates concerning to what extentnormative ontology is possible (e.g.,foundationalism andcoherentism in philosophy,BFO andCyc in artificial intelligence).
Ontology is a branch ofphilosophy and intersects areas such asmetaphysics,epistemology, andphilosophy of language, as it considers how knowledge, language, and perception relate to the nature of reality.Metaphysics 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 betweenparticulars anduniversals,intrinsic and extrinsic properties, oressence andexistence. Metaphysics has been an ongoing topic of discussion since recorded history.
Since the mid-1970s, researchers in the field ofartificial intelligence (AI) have recognized thatknowledge engineering is the key to building large and powerful AI systems[citation needed]. AI researchers argued that they could create new ontologies ascomputational models that enable certain kinds ofautomated reasoning, which was onlymarginally successful. In the 1980s, the AI community began to use the termontology to refer to both a theory of a modeled world and a component ofknowledge-based systems. In particular, David Powers introduced the wordontology to AI to refer to real world or robotic grounding,[12][13] 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.[14] Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy.[15]
In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" byTom Gruber[16] usedontology as a technical term incomputer science closely related to earlier idea ofsemantic networks andtaxonomies. Gruber introduced the term asa 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.[17]
Attempting to distance ontologies from taxonomies and similar efforts inknowledge modeling that rely onclasses andinheritance, 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 toconservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, one needs to state axioms thatdo constrain the possible interpretations for the defined terms.[16]
Recent experimental ontology frameworks have also explored resonance-based AI-human co-evolution structures, such as IAMF (Illumination AI Matrix Framework) and OntoMotoOS (a meta-operating system concept for ethical and ontological AI-human co-evolution). Though not yet widely adopted in academic discourse, such models propose phased approaches to ethical harmonization and structural emergence.[18]
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."[19]
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.
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 wordcard has many different meanings. An ontology about the domain ofpoker would model the "playing card" meaning of the word, while an ontology about the domain ofcomputer 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 commonupper 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,[20] 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 theOBO Foundry.
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 acore glossary that overarches the terms and associated object descriptions as they are used in various relevant domain ontologies.
Standardized upper ontologies available for use includeBFO,Dublin Core,GFO,Cyc,SUMO,UMBEL, andDOLCE.[21][22]WordNet has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontologies.[23]
A survey of ontology visualization methods is presented by Katifori et al.[24] An updated survey of ontology visualization methods and tools was published by Dudás et al.[25] The most established ontology visualization methods, namely indented tree and graph visualization are evaluated by Fu et al.[26] A visual language for ontologies represented inOWL is specified by theVisual Notation for OWL Ontologies (VOWL).[27]
Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain.[28] It is a subfield ofknowledge 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.[29][30]
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:
Ensuring the ontology iscurrent withdomain knowledge and term use
Providingsufficient specificity and concept coverage for the domain of interest, thus minimizing thecontent completeness problem
Ontology editors are applications designed to assist in the creation or manipulation of ontologies. It is common for ontology editors to use one or moreontology languages.
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 andtext mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges.[32]
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]
Anontology language is aformal language used to encode an ontology. There are a number of such languages for ontologies, both proprietary and standards-based:
Common Algebraic Specification Language is a general logic-based specification language developed within the IFIP working group 1.3 "Foundations of System Specifications" and is ade facto standard language for software specifications. It is now being applied to ontology specifications in order to provide modularity and structuring mechanisms.
Common logic is ISO standard 24707, a specification of a family of ontology languages that can be accurately translated into each other.
DOGMA (Developing Ontology-Grounded Methods and Applications) adopts the fact-oriented modeling approach to provide a higher level of semantic stability.
TheGellish language includes rules for its own extension and thus integrates an ontology with an ontology language.
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 for making ontological statements, developed as a follow-on fromRDF andRDFS, as well as earlier ontology language projects includingOIL,DAML, andDAML+OIL. OWL is intended to be used over theWorld Wide Web, and all its elements (classes, properties and individuals) are defined as RDFresources, and identified byURIs.
Arabic Ontology, a linguistic ontology for Arabic, which can be used as an Arabic Wordnet but with ontologically-clean content.[34]
AURUM – Information Security Ontology,[35] An ontology forinformation security knowledge sharing, enabling users to collaboratively understand and extend the domain knowledge body. It may serve as a basis for automated information security risk and compliance management.
BabelNet, a very large multilingual semantic network and ontology, lexicalized in many languages
BioPAX,[37] an ontology for the exchange and interoperability of biological pathway (cellular processes) data
BMO,[38] an e-Business Model Ontology based on a review of enterprise ontologies and business model literature
SSBMO,[39] a Strongly Sustainable Business Model Ontology based on a review of the systems based natural and social science literature (including business). Includes critique of and significant extensions to the Business Model Ontology (BMO).
Common Core Ontologies (CCO)[40], a suite of eleven ontologies that extendBasic Formal Ontology, which were developed under anIARPA Knowledge Discovery and Dissemination grant and are widely-used in US defense and intelligence sectors.
CCO and GexKB,[41] Application Ontologies (APO) that integrate diverse types of knowledge with the Cell Cycle Ontology (CCO) and the Gene Expression Knowledge Base (GexKB)
CContology (Customer Complaint Ontology),[42] an e-business ontology to support online customer complaint management
COSMO,[44] a Foundation Ontology (current version in OWL) that is designed to contain representations of all of the primitive concepts needed to logically specify the meanings of any domain entity. It is intended to serve as a basic ontology that can be used to translate among the representations in other ontologies or databases. It started as a merger of the basic elements of the OpenCyc and SUMO ontologies, and has been supplemented with other ontology elements (types, relations) so as to include representations of all of the words in theLongman dictionarydefining vocabulary.
Gellish English dictionary, an ontology that includes a dictionary and taxonomy that includes an upper ontology and a lower ontology that focuses on industrial and business applications in engineering, technology and procurement.
Geopolitical ontology, an ontology describing geopolitical information created byFood and Agriculture Organization(FAO). The geopolitical ontology includes names in multiple languages (English, French, Spanish, Arabic, Chinese, Russian and Italian); maps standard coding systems (UN, ISO, FAOSTAT, AGROVOC, etc.); provides relations among territories (land borders, group membership, etc.); and tracks historical changes. In addition, FAO provides web services of geopolitical ontology and a module maker to download modules of the geopolitical ontology into different formats (RDF, XML, and EXCEL). See more information atFAO Country Profiles.
GAO (General Automotive Ontology) – an ontology for the automotive industry that includes 'car' extensions[50]
SCDO, the Sickle Cell Disease Ontology,[66] facilitates data sharing and collaborations within the SDC community, amongst other applications (see list onSCDO website).
Schema.org, for embedding structured data into web pages, primarily for the benefit of search engines
SWEET,[69] Semantic Web for Earth and Environmental Terminology
SSN/SOSA,[70] The Semantic Sensor Network Ontology (SSN) and Sensor, Observation, Sample, and Actuator Ontology (SOSA) are W3C Recommendation and OGC Standards for describing sensors and their observations.
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.
COLORE[73] is an open repository of first-order ontologies inCommon Logic with formal links between ontologies in the repository.
DAML Ontology Library[74] maintains a legacy of ontologies in DAML.
Ontology Design Patterns portal[75] is a wiki repository of reusable components and practices for ontology design, and also maintains a list ofexemplary ontologies.
Protégé Ontology Library[76] contains a set of OWL, Frame-based and other format ontologies.
SchemaWeb[77] is a directory of RDF schemata expressed in RDFS, OWL and DAML+OIL.
The following are both directories and search engines.
OBO Foundry is a suite of interoperable reference ontologies in biology and biomedicine.[58][78]
OntoSelect[79] Ontology Library offers similar services for RDF/S, DAML and OWL ontologies.
Ontaria[80] is a "searchable and browsable directory of semantic web data" with a focus on RDF vocabularies with OWL ontologies. (NB Project "on hold" since 2004).
Swoogle is a directory and search engine for all RDF resources available on the Web, including ontologies.
ROMULUS is a foundational ontology repository aimed at improving semantic interoperability. Currently there are three foundational ontologies in the repository:DOLCE,BFO andGFO.[82]
In general, ontologies can be used beneficially in several fields.
Enterprise applications.[83] A more concrete example isSAPPHIRE (Health care) orSituational Awareness and Preparedness for Public Health Incidences and Reasoning Engines which is asemantics-basedhealth information system capable of tracking and evaluating situations and occurrences that may affectpublic health.
Geographic information systems bring together data from different sources and benefit therefore from ontological metadata which helps to connect the semantics of the data.[84]
Domain-specific ontologies are extremely important in biomedical research, which requires named entity disambiguation of various biomedical terms and abbreviations that have the same string of characters but represent different biomedical concepts. For example, CSF can represent Colony Stimulating Factor or Cerebral Spinal Fluid, both of which are represented by the same term, CSF, in biomedical literature.[85] This is why a large number of public ontologies are related to the life sciences. Life science data science tools that fail to implement these types of biomedical ontologies will not be able to accurately determine causal relationships between concepts.[86]
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Ontology Summit - an annual series of events (first started in 2006) that involves the ontology community and communities related to each year's theme chosen for the summit.
