Abstract
Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities on the same system, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (core-concepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semi-automatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different research teams.
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Notes
- 1.
This article is an extension of Using Formal Concept Analysis to Extract a Greatest Common Model published in the ICEIS 2012 conference.
- 2.
Here, we refer to the relational normal form used in database schema normalization, which has the same objective: eliminate redundancies.
- 3.
In the literature, standard notation is \(K = (G, M, I)\). We use \(K = (E, C, R)\) for readability reasons and to get a better understanding toward our thematic partners.
- 4.
http://www.objecteering.com/. The development has been done with Objecteering but we are migrating it to Modelio, the last version of Objecteering.
- 5.
- 6.
In these tables, new and merged concepts must be still validated by an expert.
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Amar, B., Guédi, A.O., Miralles, A., Huchard, M., Libourel, T., Nebut, C. (2013). Finding Semi-Automatically a Greatest Common Model Thanks to Formal Concept Analysis. In: Cordeiro, J., Maciaszek, L.A., Filipe, J. (eds) Enterprise Information Systems. Lecture Notes in Business Information Processing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40654-6_5
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