Incomputer science,information science andsystems engineering,ontology engineering is a field which studies the methods and methodologies for buildingontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. 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.[2] Ontology engineering is one of the areas ofapplied ontology, and can be seen as an application ofphilosophical ontology. Core ideas and objectives of ontology engineering are also central inconceptual modeling.
Ontology engineering aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the inter-operability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies for a particular domain.
— Line Pouchard, Nenad Ivezic and Craig Schlenoff,[3]
Automated processing of information not interpretable bysoftware agents can be improved by adding richsemantics to the corresponding resources, such as video files. One of the approaches for the formalconceptualization of representedknowledge domains is the use of machine-interpretable ontologies, which providestructured data in, or based on,RDF,RDFS, andOWL. Ontology engineering is the design and creation of such ontologies, which can contain more than just the list of terms (controlled vocabulary); they contain terminological, assertional, and relationalaxioms to define concepts (classes), individuals, and roles (properties) (TBox,ABox, and RBox, respectively).[4] Ontology engineering is a relatively new field of study concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies,[5][6] and the tool suites and languages that support them.A common way to provide the logical underpinning of ontologies is to formalize the axioms withdescription logics, which can then be translated toany serialization of RDF, such asRDF/XML orTurtle. Beyond the description logic axioms, ontologies might also containSWRL rules. The concept definitions can be mapped to any kind of resource or resource segment inRDF, such as images, videos, andregions of interest, to annotate objects, persons, etc., and interlink them with related resources acrossknowledge bases, ontologies, andLOD datasets. This information, based on human experience and knowledge, is valuable forreasoners for the automated interpretation of sophisticated and ambiguous contents, such as the visual content of multimedia resources.[7] Application areas ofontology-based reasoning include, but are not limited to,information retrieval, automated scene interpretation, andknowledge discovery.
Anontology language is aformal language used to encode the ontology. There are a number of such languages for ontologies, both proprietary and standards-based:
Common logic is ISO standard 24707, a specification for a family of ontology languages that can be accurately translated into each other.
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.
OntoUML is a well-founded language for specifying reference ontologies.
SHACL (RDF SHapes Constraints Language) is a language for describing structure of RDF data. It can be used together with RDFS and OWL or it can be used independently from them.
XBRL (Extensible Business Reporting Language) is a syntax for expressing business semantics.
Life sciences is flourishing with ontologies that biologists use to make sense of their experiments.[9] For inferring correct conclusions from experiments, ontologies have to be structured optimally against the knowledge base they represent. The structure of an ontology needs to be changed continuously so that it is an accurate representation of the underlyingdomain.
Recently, an automated method was introduced for engineering ontologies in life sciences such asGene Ontology (GO),[10] one of the most successful and widely used biomedical ontology.[11] Based on information theory, it restructures ontologies so that the levels represent the desired specificity of the concepts. Similar information theoretic approaches have also been used for optimal partition of Gene Ontology.[12] Given the mathematical nature of such engineeringalgorithms, these optimizations can be automated to produce a principled and scalable architecture to restructure ontologies such as GO.
Open Biomedical Ontologies (OBO), a 2006 initiative of the U.S. National Center for Biomedical Ontology, provides a common 'foundry' for various ontology initiatives, amongst which are:
^Sikos, L. F. (14 March 2016). "A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets".Lecture Notes in Artificial Intelligence. Vol. 9621. Springer. pp. 1–13.arXiv:1608.08072.doi:10.1007/978-3-662-49381-6_1.
^Zarka, M; Ammar, AB; AM, Alimi (2015). "Fuzzy reasoning framework to improve semantic video interpretation".Multimedia Tools and Applications.75 (10):5719–5750.doi:10.1007/s11042-015-2537-1.S2CID16505884.
Mustafa Jarrar and Robert Meersman (2008)."Ontology Engineering -The DOGMA Approach". Book Chapter (Chapter 3). In Advances in Web Semantics I. Volume LNCS 4891, Springer.
This article incorporatespublic domain material from the National Institute of Standards and Technology
