Consistency Assessment Between Multiple Representations of Geographical Databases: a Specification-Based Approach

There currently exist many geographical databases that represent a same part of the world, each with its own levels of detail and points of view. The use and management of these databases therefore sometimes requires their integration into a single database. The main issue in this integration process is the ability to analyse and understand the differences among the multiple representations. These differences can of course be explained by the various specifications but can also be due to updates or errors during data capture. In this paper, we propose an new approach to interpret the differences in representation in a semiautomatic way. We consider the specifications of each database as the “knowledge” to evaluate the conformity of each representation. This information is grasped from existing documents but also from data, by means of machine learning tools. The management of this knowledge is enabled by a rule-based system. Application of this approach is illustrated with a case study from two IGN databases. It concerns the differences between the representations of traffic circles.

[1]  Stefano Spaccapietra,et al.  On Spatial Database Integration , 1998, Int. J. Geogr. Inf. Sci..

[2]  Stefano Spaccapietra,et al.  Modelling and Manipulating Multiple Representations of Spatial Data , 2002 .

[3]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[4]  Volker Walter,et al.  Matching spatial data sets: a statistical approach , 1999, Int. J. Geogr. Inf. Sci..

[5]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[6]  Stefano Spaccapietra,et al.  Database Integration: the Key to Data Interoperability , 2022 .

[7]  Monika Sester,et al.  Knowledge acquisition for the automatic interpretation of spatial data , 2000, Int. J. Geogr. Inf. Sci..

[8]  Frederico T. Fonseca,et al.  Using Ontologies for Integrated Geographic Information Systems , 2002, Trans. GIS.

[9]  Reid Simmons,et al.  Second Generation Expert Systems , 1993, Springer Berlin Heidelberg.

[10]  Robert Weibel,et al.  Overcoming the Knowledge Acquisition Bottleneck in Map Generalization: The Role of Interactive Systems and Computational Intelligence , 1995, COSIT.

[11]  Alia I. Abdelmoty,et al.  A Qualitative Approach to Integration in Spatial Databases , 1998, DEXA.

[12]  J. Paiva.  Topological Equivalence and Similarity in Multi-Representation Geographic Databases , 1998 .

[13]  Thomas Devogele,et al.  Processus d'intégration et d'appariement de Bases de Données Géographiques; Application à une base de données routières multi-échelles , 1997 .

[14]  Jean-Daniel Zucker,et al.  An Abstraction-based Machine Learning Approach to Cartographic Generalisation , 2005, IICAI.

[15]  M. Egenhofer.  Evaluating Inconsistencies Among Multiple Representations , 2000 .