Ontology engineering

Example of a constructed MBED Top Level Ontology based on the nominal set of views.[1]

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, 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 of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual 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 by software agents can be improved by adding rich semantics to the corresponding resources, such as video files. One of the approaches for the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide structured data in, or based on, RDF, RDFS, and OWL. 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 relational axioms 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 with description logics, which can then be translated to any serialization of RDF, such as RDF/XML or Turtle. Beyond the description logic axioms, ontologies might also contain SWRL rules. The concept definitions can be mapped to any kind of resource or resource segment in RDF, such as images, videos, and regions of interest, to annotate objects, persons, etc., and interlink them with related resources across knowledge bases, ontologies, and LOD datasets. This information, based on human experience and knowledge, is valuable for reasoners for the automated interpretation of sophisticated and ambiguous contents, such as the visual content of multimedia resources.[7] Application areas of ontology-based reasoning include, but are not limited to, information retrieval, automated scene interpretation, and knowledge discovery.

  1. ^ Peter Shames, Joseph Skipper. "Toward a Framework for Modeling Space Systems Architectures" Archived 2009-02-27 at the Wayback Machine. NASA, JPL.
  2. ^ http://ontology.buffalo.edu/bfo/BeyondConcepts.pdf [bare URL PDF]
  3. ^ Line Pouchard, Nenad Ivezic and Craig Schlenoff (2000) "Ontology Engineering for Distributed Collaboration in Manufacturing". In Proceedings of the AIS2000 conference, March 2000.
  4. ^ 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.
  5. ^ Asunción Gómez-Pérez, Mariano Fernández-López, Oscar Corcho (2004). Ontological Engineering: With Examples from the Areas of Knowledge Management, E-commerce and the Semantic Web. Springer, 2004.
  6. ^ De Nicola, A; Missikoff, M; Navigli, R (2009). "A software engineering approach to ontology building" (PDF). Information Systems. 34 (2): 258. CiteSeerX 10.1.1.149.7258. doi:10.1016/j.is.2008.07.002.
  7. ^ 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. S2CID 16505884.