"Knowledge graph" redirects here. For the Google knowledge base, see Knowledge Graph. For other uses, see Knowledge engine (disambiguation).
This article is about ontology in information science. For the study of the nature of being, see Ontology.
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.
Every field creates ontologies to limit complexity and organize information into data and knowledge. As new ontologies are made, their use hopefully improves 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.
Since Google started an initiative called Knowledge Graph, a substantial amount of research has used the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is sometimes used as synonym for ontology. 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, 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.
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.
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.
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), 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 spiritual 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.
Every field uses ontological assumptions to frame explicit theories, research and applications. For instance, the definition and ontology of economics is a primacy concern in Marxist economics, but also in other subfields of economics. 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).
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.
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. 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. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy.
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.
Ontologies are commonly encoded using ontology languages.
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.
Standardized upper ontologies available for use include BFO, BORO method, Dublin Core, GFO, OpenCyc/ResearchCyc, SUMO, UMBEL, the Unified Foundational Ontology (UFO), and DOLCE. WordNet has been considered an upper ontology by some and has been used as a linguistic tool for learning 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 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.
dot15926 Editor Python? Open source ontology editor for data compliant to engineering ontology standard ISO 15926. Allows Python scripting and pattern-based data analysis. Supports extensions.
Fluent Editor ontology editor for OWL and SWRL with Controlled Natural Language (Controlled English). Supports OWL, RDF, DL and Functional rendering, unlimited imports and built-in reasoning services.
HOZO Java graphical editor especially created to produce heavy-weight and well thought out ontologies Osaka University and Enegate Co, ltd.
Knoodl Free 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. Revelytix, Inc..
Synaptica Ontology, taxonomy and thesaurus management software. Web based, supports OWL and SKOS. Synaptica, LLC.
Thesaurus Master Manages creation and use of ontologies for use in data management and semantic enrichment by enterprise, government, and scholarly publishers.
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.
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.
The Cyc project has its own ontology language called CycL, based on first-order predicate calculus with some higher-order extensions.
DOGMA (Developing Ontology-Grounded Methods and Applications) adopts the fact-oriented modeling approach to provide a higher level of semantic stability.
The Gellish language includes rules for its own extension and thus integrates an ontology with an ontology language.
IDEF5 is a software engineering method to develop and maintain usable, accurate, domain ontologies.
KIF is a syntax for first-order logic that is based on S-expressions. SUO-KIF is a derivative version supporting the Suggested Upper Merged Ontology.
Olog is a category theoretic approach to 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 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.
Rule Interchange Format (RIF) and F-Logic combine ontologies and rules.
Semantic Application Design Language (SADL) captures a subset of the expressiveness of OWL, using an English-like language entered via an Eclipse Plug-in.
SBVR (Semantics of Business Vocabularies and Rules) is an OMG standard adopted in industry to build ontologies.
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.
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).
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 dictionary defining vocabulary.
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.
IDEAS Group, a formal ontology for enterprise architecture being developed by the Australian, Canadian, UK and U.S. Defence Depts.
Linkbase, a formal representation of the biomedical domain, founded upon Basic Formal Ontology.
NIFSTD Ontologies from the Neuroscience Information Framework: a modular set of ontologies for the neuroscience domain.
ONSTR, Ontology for Newborn Screening Follow-up and Translational Research, Newborn Screening Follow-up Data Integration Collaborative, Emory University, Atlanta.
Plant Ontology for plant structures and growth/development stages, etc.
ProbOnto, knowledge base and ontology of probability distributions.
The W3C Linking Open Data community project coordinates attempts to converge different ontologies into worldwide Semantic Web.
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 is an open repository of first-order ontologies in Common Logic with formal links between ontologies in the repository.
DAML Ontology Library maintains a legacy of ontologies in DAML.
Ontology Design Patterns portal is a wiki repository of reusable components and practices for ontology design, and also maintains a list of exemplary ontologies.
Protégé Ontology Library contains a set of OWL, Frame-based and other format ontologies.
SchemaWeb 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.
OntoSelect Ontology Library offers similar services for RDF/S, DAML and OWL ontologies.
Ontaria 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 and GFO.
In general, ontologies can be used beneficially in several fields.
Enterprise applications. A more concrete example is SAPPHIRE (Health care) or Situational Awareness and Preparedness for Public Health Incidences and Reasoning Engines which is a semantics-based health information system capable of tracking and evaluating situations and occurrences that may affect public 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.
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. 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.
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Wikimedia Commons has media related to Ontology.
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.
This page was last edited on 19 April 2019, at 23:31 (UTC).


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Servi Sulpici Galba () va ser un magistrat romà. Era net del cònsol Servi Sulpici Galba, pare de Gai Sulpici Galba i besavi de l'emperador Galba. Formava part de la gens Sulpícia i era de la família dels Galba, d'origen patrici.

Juli Cèsar el va enviar contra els nantuates, veragres i seduns al començament de la seva campanya a la Gàl·lia i els va derrotar. Després va dirigir la Legió XII Fulminata a Octodurus, al país dels al·lòbroges, però va haver d'abandonar el Setge d'Octodurus on havia de passar l'hivern, per la pressió enemiga, fins al territori dels al·lòbroges. L'any 52 aC, al final de la campanya es va estacionar entre els hedus per passar l'hivern, amb la Legió XIV Gemina. El 54 aC va ser pretor urbà. El 49 aC va ser candidat a cònsol recolzat per Juli Cèsar, però no va ser elegit, segurament per la seva amistat amb Cèsar, que ja començava les seves disputes amb Pompeu.

Va ser amic de Dècim Brut i de Ciceró, i durant la guerra de Mutina va dirigir la legió Màrtia, que havia estat estacionada a Macedònia. Segons Suetoni, va prendre part a la conspiració contra Juli Cèsar. A la Tercera guerra civil, l'any 43 aC va ser legat de l'exèrcit senatorial contra Marc Antoni i va participar en la batalla on Antoni va ser derrotat. El seu paper a la batalla l'explica ell mateix en una carta a Ciceró que encara es conserva. Després de la batalla de Mutina va ser enviat a Roma per Dècim Juni Brut Albí, en un intent d'aturar Marc Antoni. Però un canvi en la situació política fa que s'aturi aquesta persecució. Després de la instauració del Segon Triumvirat, va ser condemnat a mort per la Lex Pedia. Segurament va ser jutjat, condemnat a mort i executat.

Referències

Assassins de Juli Cèsar
Galba, Servi Sulpici pretor 54 aC
Pretors urbans
Militars romans de la Guerra de les Gàl·lies

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