Abstract
We can trace five origins of ontology engineering, and all five still play a major role in ontology engineering. Each of these roots gives a different perspective on content and use of ontologies. Philosophical ontology is concerned with “reality”; Information science with systematic terminology; Artificial Intelligence (AI) with terminological knowledge, Knowledge Engineering with the specification of knowledge bases, and Information Management with semantics. Associated with these roots, the applications differ and range from analytic clarification to automated reasoning. Also mismatches between formalism and aim occur frequently. These mismatches can often be traced to an unclear distinction between knowledge and semantics. We explain this difference in Section 4.3 using a simple cognitive architecture for natural language production. A Cognitive Science perspective is however well suited where top ontologies try to cover the core concepts of common sense, as a wealth of empirical studies have become available on the content of our “knowledge instincts”. We present an example on the modeling of spatial concepts and refer to our still ongoing work on a common-sense based core ontology for legal domains: LKIF-Core (Hoekstra et al. 2007; Hoekstra 2009).
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Notes
- 1.
Paradoxically formal ontology is sometimes called ‘applied ontology’! see Wikipedia under http://wikipedia.org/wiki/Applied_ontology
- 2.
The reader is referred to (Ecco 1997) for a detailed review.
- 3.
Conceptual Modeling Language for CommonKADS (Breuker and Van De Velde 1994)
- 4.
Another term has been: long term memory. At least three subsystems can be distinguished: episodic memory, containing past memories (instances); semantic memory (which contains generic knowledge, facts and beliefs), and an associative memory that accounts for skills and other empirical contingencies. These sub-sytems work relatively independently. For instance amnesia, due to damage of specific areas of the brain, is a disturbance of episodic memory; not of semantic memory.
- 5.
As DAML+OIL, and RDFS/OWL are in fact (precursors to) OWL, we can state that except for Ontolingua (history) and XML all these ontologies are OWL based.
- 6.
It is hard to state what is not constrained by the primitive dispositions to interpret the world on which human builds in order to act effectively. However, we have obtained a more “conscious”, “rational” way to interpret the world by delayed reflection. This has for instance ended in models of reality which have become even inconceivable, as in quantum theory. See for instance on ‘physical ontology: http://en.wikipedia.org/wiki/Physical_ontology. Also core ontologies with a highly specialized technical domain may make shortcuts, see e.g. (West 2004).
- 7.
Thus far we have not read about cases where such a concept gets “overwritten” by new information in the common sense domain.
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Breuker, J., Hoekstra, R. (2011). A Cognitive Science Perspective on Legal Ontologies. In: Sartor, G., Casanovas, P., Biasiotti, M., Fernández-Barrera, M. (eds) Approaches to Legal Ontologies. Law, Governance and Technology Series, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0120-5_4
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