Ontology engineering

Source: Wikipedia, the free encyclopedia.
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.

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.

Automated processing of information not interpretable by

ABox, and RBox, respectively).[3] 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,[4][5]
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.[6] Application areas of ontology-based reasoning include, but are not limited to, information retrieval, automated scene interpretation, and knowledge discovery.

Ontology languages

Further information: ontology language

An ontology language is a formal 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.
  • The
    first-order predicate calculus
    with some higher-order extensions.
  • 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.
  • F-Logic
    and its successor ObjectLogic combine ontologies and rules.
  • URIs
    .
  • 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.

Ontology engineering in life sciences

Life sciences is flourishing with ontologies that biologists use to make sense of their experiments.[7] 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 underlying domain.

Recently, an automated method was introduced for engineering ontologies in life sciences such as

algorithms
, 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:

and more

Methodologies and tools for ontology engineering

See also

References

Public Domain This article incorporates public domain material from the National Institute of Standards and Technology

  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. doi:10.1007/978-3-662-49381-6_1
    .
  4. ^ 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.
  5. doi:10.1016/j.is.2008.07.002
    .
  6. S2CID 16505884
    .
  7. PMID 20200009
    .
  8. PMID 20139945
    .
  9. PMID 10802651. Archived from the original
    (PDF) on 2011-05-26.
  10. PMID 17098937
    .
  11. ^ Falbo, Ricardo (2014). "SABiO: Systematic Approach for Building Ontologies" (PDF). Proceedings of the 1st Joint Workshop ONTO.COM / ODISE on Ontologies in Conceptual Modeling and Information Systems Engineering Co-located with 8th International Conference on Formal Ontology in Information Systems, ONTO.COM/ODISE@FOIS 2014, Rio de Janeiro, Brazil, September 21, 2014. 1301 – via CEUR-WS.org.

Further reading

External links
Retrieved from "https://en.wikipedia.org/w/index.php?title=Ontology_engineering&oldid=1179124025"