Abstract
What exactly does interoperability mean in the context of information science? Which entities are supposed to interoperate, how can they interoperate, and when can we say they are interoperating? This question, crucial to assessing the benefit of semantic technology and information ontologies, has been understood so far primarily in terms of standardization, alignment and translation of languages. In this article, we argue for a pragmatic paradigm of interoperability understood in terms of conversation and reconstruction. Based on examples from geographic information and land cover classification, we argue that semantic heterogeneity is to a large extent a problem of multiple perspectives. It therefore needs to be addressed not by standardization and alignment, but by articulation and reconstruction of perspectives. Reconstruction needs to be grounded in shared operations. What needs to be standardized is therefore not the perspective on a concept, but the procedure to arrive at different perspectives. We propose conceptual imitation as a synthetic learning approach, and conceptual spaces as a constructive basis. Based on conceptual imitation, information provider and user concepts can be checked for perspectival correspondence.
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Notes
- 1.
Compare the role of metadata in Gray et al. (2005).
- 2.
- 3.
- 4.
Similar to traffic locomotion affordances (Scheider and Kuhn 2010).
- 5.
International Panel on Climate Change, http://www.ipcc.ch/
- 6.
- 7.
Better captured by the German term “Kulturtechniken”.
- 8.
Compare Chapter 6.1 in Stuckenschmidt and van Harmelen (2003).
- 9.
- 10.
The history of this fundamental misunderstanding can be traced back to Morris’ naturalized semiotic process and Shannon and Weaver’s mechanistic information theory, and can be currently studied in terms of modern nonsense about “information” allegedly being “transferred and understood by machines, computers, and DNA molecules” (Janich 2006).
- 11.
Uniform resource identifier.
- 12.
Resource Description Framework, http://www.w3.org/RDF/
- 13.
For a proposal how central spatial concepts can be based on Braitenberg vehicles, see Both et al. (2013).
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- 15.
Machine learning is analytic in the sense that it prescribes a constructive basis (e.g., in terms of a vector calculus in support vector machines (Hastie et al. 2001)) or automatically selects it based on observed behaviour (as in Bayesian model selection).
- 16.
The “instantaneous field of view” (IFoV) of a satellite is an example for the latter.
- 17.
According to Lorenzen, a calculus is a set of rules used to generate “figures from other figures” (Lorenzen 1955).
- 18.
These are sentences without variables.
- 19.
Note that we do not require a decision procedure for the entire constructive calculus, only for the predicates of interest. This allows to use unrestricted FOL or HOL as the most flexible syntactic standard, but comes at the price of caring about the computation of decisions on a case-to-case basis.
- 20.
This aspect of similarity is based on experiential equivalence and is not discussed in this article.
- 21.
This can be gleened from the fact that Lund (2006) proposes a decision tree which enforces mutual exclusiveness of cropland and forest by defining cropland based on cultivation as well as the logical complement of forest.
- 22.
- 23.
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Acknowledgements
A draft of the ideas in this article was presented at the EarthScienceOntolog session-3 at 10/11/2012.Footnote 22 Research was funded by the International Research Training Group on Semantic Integration of Geospatial Information (DFG GRK 1498), and by the research fellowship grant DFG SCHE 1796/1-1. We thank Helen Couclelis, Benjamin Adams, Krzysztof Janowicz and MUSILFootnote 23 for discussions that helped shape this article.
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Scheider, S., Kuhn, W. (2015). How to Talk to Each Other via Computers: Semantic Interoperability as Conceptual Imitation. In: Zenker, F., Gärdenfors, P. (eds) Applications of Conceptual Spaces. Synthese Library, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-319-15021-5_6
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