Ontology engineering

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

Ontology engineering in computer science, information science and systems engineering is a field which studies the methods and methodologies for building ontologies: formal representations of a set of concepts within a domain and the relationships between those concepts. 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.

Overview[edit]

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).[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[edit]

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 Cyc project has its own ontology language called CycL, based on 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.
  • Rule Interchange Format (RIF), F-Logic and its successor ObjectLogic combine ontologies and rules.
  • 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.
  • 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[edit]

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 Gene Ontology (GO),[8] one of the most successful and widely used biomedical ontology.[9] 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.[10] Given the mathematical nature of such engineering 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, that provides a common 'foundry' for various ontology initiatives, amongst which are:

and more

Methodologies and Tools for ontology engineering[edit]

See also[edit]

References[edit]

 This article incorporates public domain material from the National Institute of Standards and Technology website https://www.nist.gov.

  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
  3. ^ 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. 9621. Springer. pp. 1–13. arXiv:1608.08072. 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. ^ 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.
  6. ^ 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.
  7. ^ Malone, J; Holloway, E; Adamusiak, T; Kapushesky, M; Zheng, J; Kolesnikov, N; Zhukova, A; Brazma, A; Parkinson, H (2010). "Modeling sample variables with an Experimental Factor Ontology". Bioinformatics. 26 (8): 1112–1118. doi:10.1093/bioinformatics/btq099. PMC 2853691. PMID 20200009.
  8. ^ Alterovitz, G; Xiang, M; Hill, DP; Lomax, J; Liu, J; Cherkassky, M; Dreyfuss, J; Mungall, C; et al. (2010). "Ontology engineering". Nature Biotechnology. 28 (2): 128–30. doi:10.1038/nbt0210-128. PMC 4829499. PMID 20139945.
  9. ^ Botstein, David; Cherry, J. Michael; Ashburner, Michael; Ball, Catherine A.; Blake, Judith A.; Butler, Heather; Davis, Allan P.; Dolinski, Kara; et al. (2000). "Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium" (PDF). Nature Genetics. 25 (1): 25–9. doi:10.1038/75556. PMC 3037419. PMID 10802651. Archived from the original (PDF) on 2011-05-26.
  10. ^ Alterovitz, G.; Xiang, M.; Mohan, M.; Ramoni, M. F. (2007). "GO PaD: The Gene Ontology Partition Database". Nucleic Acids Research. 35 (Database issue): D322–7. doi:10.1093/nar/gkl799. PMC 1669720. PMID 17098937.

Further reading[edit]

External links[edit]

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