Since Google started an initiative called Knowledge Graph, a substantial amount of research has gone on using 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. One common interpretation is that a knowledge graph represents a collection of interlinked descriptions of entities - real-world objects, events, situations or abstract concepts. Unlike ontologies, knowledge graphs, such as Google's Knowledge Graph and DBpedia, 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.
While the etymology is Greek, the oldest extant record of the word itself, the New 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.
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.
In the early 1990s, the widely cited Web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber is credited with a deliberate definition of ontology as a technical term in computer science. 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.
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. To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms.
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."
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:
Instances or objects (the basic or "ground level" objects)
Ways in which classes and individuals can be related to one another
Complex structures formed from certain relations that can be used in place of an individual term in a statement
Formally stated descriptions of what must be true in order for some assertion to be accepted as input
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
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
A domain ontology (or domain-specific ontology) represents concepts which belong to a part 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.).
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, but this area of research is still ongoing, and it's a recent event to see the issue sidestepped by having multiple domain ontologies using the same upper ontology like the OBO Foundry.
An upper ontology (or foundation ontology) is a model of the common relations and objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain ontologies.
The Gellish ontology is an example of a combination of an upper and a domain ontology.
A survey of ontology visualization methods is presented by Katifori et al. An updated survey of ontology visualization methods and tools was published by Dudás et al. The most established ontology visualization methods, namely indented tree and graph visualization are evaluated by Fu et al. A visual language for ontologies represented in OWL is specified by the Visual Notation for OWL Ontologies (VOWL).
Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain. 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.
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:
A visual, collaborative and real time ontology and knowledge graph schema editor. Features include sharing documents, commenting, search and tracking history. Support W3C Semantic Web standards: RDF, RDFS, OWL and also Property Graph schemas.
Ontology editor for the KM language. km: The Knowledge Machine
web application/service that is an ontology editor, wiki, and ontology registry. Supports creation of communities where members can collaboratively import, create, discuss, document and publish ontologies. Supports OWL, RDF, RDFS, and SPARQL queries.
downloadable, support for RDF(S), OWL and ObjectLogic (derived from F-Logic), graphical rule editor, visualizations
Collaborative web application for managing ontologies and knowledge engineering, web-browser-based graphical rules editor, sophisticated search and export interface. Web service available to link ontology information to existing data
Collaborative Web Platform for Management of SKOS thesauri, OWL ontologies and OntoLex lexicons, now in its third incarnation supported by the ISA2 program of the EU
originally developed on a joint effort between University of Rome Tor Vergata and the Food and the Agriculture Organization of the United Nations: FAO
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.
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:
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 a de 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.
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 from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML, and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs.
AURUM - Information Security Ontology, An ontology for information 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
Basic Formal Ontology, a formal upper ontology designed to support scientific research
BioPAX, an ontology for the exchange and interoperability of biological pathway (cellular processes) data
BMO, an e-Business Model Ontology based on a review of enterprise ontologies and business model literature
SSBMO, 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).
CCO and GexKB, 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), an e-business ontology to support online customer complaint management
COSMO, 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 the Longman dictionarydefining vocabulary.
Cyc, a large Foundation Ontology for formal representation of the universe of discourse
Disease Ontology, designed to facilitate the mapping of diseases and associated conditions to particular medical codes
DOLCE, a Descriptive Ontology for Linguistic and Cognitive Engineering
Drammar, ontology of drama
Dublin Core, a simple ontology for documents and publishing
Financial Industry Business Ontology (FIBO), a business conceptual ontology for the financial industry
Gellish English dictionary, an ontology that includes a dictionary and taxonomy that includes an upper ontology and a lower ontology that focusses on industrial and business applications in engineering, technology and procurement.
Geopolitical ontology, an ontology describing geopolitical information created by Food 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 at FAO Country Profiles.
GAO (General Automotive Ontology) - an ontology for the automotive industry that includes 'car' extensions
^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. ISBN978-1-85233-551-9.
^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.
Oberle, D., Guarino, N., & Staab, S. (2009) What is an ontology?. In: "Handbook on Ontologies". Springer, 2nd edition, 2009.
Chaminda Abeysiriwardana, Prabath; Kodituwakku, Saluka R (2012). "Ontology Based Information Extraction for Disease Intelligence". International Journal of Research in Computer Science. 2 (6): 7-19. arXiv:1211.3497. doi:10.7815/ijorcs.26.2012.051.
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