Abstract
We distinguish between ontological architecture and ontology architecture, though they are closely related. Ontology architecture is emerging as a distinct discipline in ontology engineering – as an ontology development and deployment structure and methodology (Fernandéz et al., 1997). It necessarily also includes aspects of what is sometimes termed ontology lifecycle management (Andersen et al., 2006). In fact, ontology architecture can be considered to encompass ontology lifecycle management because the former lays out a general framework for the development, deployment, and maintenance of ontologies (which is the focus of lifecycle management), but also includes the interaction of applications and services that use ontologies, and an ontology tool and service infrastructure to support these. Ontological architecture is the architecture that is used to structure the ontologies that are employed by ontology architecture. As such, it addresses the levels of ontologies required (foundational, upper, middle, utility, reference, domain, and sub-domain ontologies), and mathematical, logical, and engineering constructs used to modularize ontologies in a large ontological space. This chapter focuses on ontological architecture, but it must be understood to underpin ontology architecture if only to ground/situate and enable the latter. Both kinds of architecture are relevant to ontology engineering, but we cannot address ontology architecture here until the very last section, when we look ahead. Instead, we focus on ontological architecture, which as it turns out, is a large enough topic.
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Notes
- 1.
- 2.
We differentiate and define term and concept in Table 2.1, below.
- 3.
There is, however, a vast literature on the notion of concept in philosophy, cognitive science/psychology, and linguistics. Often in cognitive science/psychology, a concept is considered to be mental particular, having a structured mental representation of a certain type (Margolis and Laurence, 1999b, p. 5–6). However, in philosophy, a concept is considered an abstract entity, signifying a general characterizing idea or universal which acts as a category for instances (individuals in logic, particulars in metaphysics and philosophical ontology) (Smith, 2004). Even in the philosophical literature, the notion of concept will vary according to philosophical stance, i.e., according to whether the adherent to the particular notion is an idealist, nominalist, conceptualist, or realist, or some combination or refraction of those (Poli, 2010). For example, some will consider a concept to be simply a placeholder for a real world entity, either a universal or a particular; example: Joe Montana (a former USA football quarterback) or Winston Churchill (a former UK prime minister) as concepts. That is, the mental placeholder or idea can be about anything. This notion of concept is a surrogate for anything that a philosophical or many linguistic theories may opine. Often, therefore (and this is our view here), concepts are best understood as conceptions, a term which has perhaps less technical baggage, insofar as conception emphasizes that we are talking about a mental representation which may or may not be reified as a concept, perhaps a stronger notion. But for purposes of simplicity, we use the term concept in this chapter to mean roughly an abstract entity signifying a general characterizing idea or universal which acts as a category for instances.
- 4.
The subsumption relation is typically defined to be the subset relation, i.e., intuitively a class is similar to a set, and the instances of that class are similar to elements of the set. A more general class (set), therefore, like mammal will contain a subclass (subset) of primate, among whose instances (elements) will be specific humans like Ralph Waldo Emerson. Concept subsumption as an ontology reasoning problem means that “given an ontology O and two classes A, B, verify whether the interpretation of A is a subset of the interpretation of B in every model of O” (OWL 1.1. http://www.w3.org/Submission/owl11-tractable/).
- 5.
There are both lower levels of interoperability and higher levels. Lower levels include logical and physical accessibility and connectivity interoperability, e.g., having two information sources on the communication network, with network addresses known by those who might wish to access those sources. A higher level might be pragmatic interoperability (intending a formal pragmatics account), which factors in the intent of the represented semantics.
- 6.
6“Recall is like throwing a big fishing net into the pond. You may be sure to get all the trout, but you’ve probably also pulled up a lot of grouper, bass, and salmon, too. Precision is like going spear fishing. You’ll be pretty sure to ONLY get trout, but you’ll no doubt miss a lot of them, too.”
– Jim Robertson, http://www-ec.njit.edu/∼robertso/infosci/recall-precision.html
- 7.
Standard Upper Ontology (SUO) Working Group Website, http://suo.ieee.org/.
- 8.
OpenCyc Website, http://www.opencyc.org/.
- 9.
Another choice we will not investigate here is that between presentism and eternalism (Partridge, 2002). Presentism argues that time is real; eternalism that time is not real, that entities change but their properties do not change over time. Presentism typically goes with endurantism; eternalism goes with perdurantism.
- 10.
Suggested Upper Merged Ontology (SUMO) Website. http://www.ontologyportal.org/.
- 11.
IEEE Standard Upper Ontology. http://suo.ieee.org/.
- 12.
WonderWeb Website. http://wonderweb.semanticweb.org/objectives.shtml. Completed, July 2004.
- 13.
Upper Cyc. http://www.cyc.com/cycdoc/vocab/vocab-toc.html.
- 14.
Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) Website. http://www.loa-cnr.it/DOLCE.html.
- 15.
Upper Ontology Summit, Ontolog Forum, 2006. http://ontolog.cim3.net/cgibin/wiki.pl?UpperOntologySummit.
- 16.
Portions of this section are adapted from Semy, Pulvermacher, Obrst (2005), pp. 5-13.
- 17.
Common Logic. http://cl.tamu.edu/.
- 18.
Adapted from Herre and Loeb (2005).
- 19.
So, the theories are propositional theories here, for purposes of simplicity, but they should be understood as being formed from FOL or higher-order logical formulae, with the additional understanding that fully saturated (with instantiated, non-variable, i.e., ground terms) FOL or higher-order formulae are propositions.
- 20.
We use propositions here, but the general case is formulae.
- 21.
For further discussion of LMS, see Obrst et al. (1999) on which this current discussion is based.
- 22.
Interoperable Knowledge Representation for Intelligence Support (IKRIS). 2006. http://nrrc.mitre.org/NRRC/Docs_Data/ikris.
- 23.
What’s in Cyc? http://www.cyc.com/cyc/technology/whatiscyc_dir/whatsincyc.
- 24.
Developing Ontology-Grounded Methods and Applications (DOGMA); a research initiative of the Free University of Brussels, Semantic Technologies and Applications Lab (VUB STARLab). http://www.starlab.vub.ac.be/website/.
- 25.
Information Flow Framework. http://suo.ieee.org/IFF/. See also: http://www.ontologos.org/IFF/IFF.html.
- 26.
IEEE Standard Upper Ontology. http://suo.ieee.org/.
- 27.
- 28.
Originally, Knowledge Interchange Format (KIF) Specification (draft proposed American National Standard [dpANS] NCITS.T2/98-004: http://logic.stanford.edu/kif/dpans.html. However, KIF has been superseded by Common Logic, which includes a KIF-like instantiation called CLIF, and is now an ISO standard.
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Acknowledgments and Disclaimers
The author’s affiliation with The MITRE Corporation is provided for purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions or viewpoints expressed by the authors. I note that the views expressed in this paper are those of this author alone and do not reflect the official policy or position of any other organization or individual. I acknowledge deep appreciation for reviews of this material and suggestions to improve it by Roberto Poli and anonymous reviewers. Discussions (mostly electronic, but occasionally live) on these topics have been much appreciated, with Roberto Poli, John Sowa, Bill Andersen, members of the MITRE Information Semantics group including Ken Laskey, David Ferrell, Deborah Nichols, Mary Parmelee, Merwyn Taylor, Karla Massey, Jonathan Tivel, Frank Zhu, Richard MacMillan, and former or auxiliary members including Ken Samuel and Allen Ginsberg, and friends and colleagues including especially Suzette Stoutenburg (my best colleague and friend), Arnie Rosenthal (my best devil’s advocate and good friend), Len Seligman, Marwan Sabbouh, Peter Mork, my persevering and nascent ontologist wife Christy Obrst, and a pack of good dogs, cats, and chickens to mostly keep me level.
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Obrst, L. (2010). Ontological Architectures. In: Poli, R., Healy, M., Kameas, A. (eds) Theory and Applications of Ontology: Computer Applications. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8847-5_2
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