A GEOMETRY-DRIVEN APPROACH FOR THE SEMANTIC INTEGRATION OF GEODATA SETS

The paper tackles the problem of data integration and describes an approach for instancebased semantic integration, which improves interoperability and information sharing between data sets of different origin. The approach automatically identifies semantic correspondences between object groups of two different geodata sets by the analysis of geometrical characteristics of the objects. For data sets with the same geographic extent corresponding objects will be detected in a first step by a simple geometric overlay. From these results on instance level the semantic correspondences on class level will be derived. Subsequently, the approach will be extended for the case that geodata sets are not situated within the same area. Therefore a function will be established for a uniform, generic and objective description of subjectively perceived object groups like lakes, woodland or roads. This function contains geometrical and topological characteristics and existing attribute values and allows the identification of similar semantic object groups. INTRODUCTION The growing availability of data, especially over the Internet (world wide web) via web services allows for a growing interoperability and combination of data sets of different Kieler, B. : A geometry-driven approach for the semantic integration of geodata sets. In: Proc. of 23rd International Cartographic Conference, 4-10 August 2007, Moscow, Russia. origin. This, however, presumes that the content of the data is known in order to draw meaningful and valid conclusions. Thus, for beneficial data integration, both the semantic and the geometric correspondences of these data sets have to be known. If the semantic relationships between different ontologies are known, a geometric integration for example a fusion or alignment process between the data sets regarding their geometry is possible. However, the normal case is that the semantic relationships are unknown due to the lack of explicit semantics and the poor documentation of these data. Currently, the identification of corresponding semantic object classes is typically done manually, because the process requires expert knowledge based on the precise meaning of the terminology used by the organizations, which capture and model the data sets. Such a manual process is adequate for a limited number of data sets but no longer feasible for an integration of arbitrary data sets that can be loaded from the Internet. Therefore, our goal is to identify corresponding semantic object groups like lakes, woodland or roads in different data sets automatically without expert knowledge, only by the analysis of geometrical and topological characteristics of the object classes itself. In this paper two cases are discussed. In the first case, we use two geodata sets of similar scale within the same geographical extent. Our approach is to use an instance based determination of semantic transformation rules between their ontologies. In the second case we try to infer semantic correspondences between data sets that are not within the same area. For the identification of similar semantic objects a function that contains geometrical and topological characteristics of certain object groups is used. Using these instance correspondences a derivation of semantic class correspondences becomes also possible. The paper is organized as follows. In the next section the background of research in this field is sketched as well as references to existing work are given. Then, our methods for the two integration cases are presented: first the semantic integration of data with similar scale and same extent, then for data of different extent. First results are given introducing some examples. The paper concludes giving some thoughts on future work. RELATED WORK Interoperability and especially data integration faces different types of problems (BISHR 1997): it has to take structural, semantic and geometric differences in the data sets into account. Structural interoperability can be achieved using standardized data formats (e.g. ISO, OGC). The most difficult problem is the semantic interoperability. The general Kieler, B. : A geometry-driven approach for the semantic integration of geodata sets. In: Proc. of 23rd International Cartographic Conference, 4-10 August 2007, Moscow, Russia. approach identifying semantic correspondences is to do schema integration manually using expert knowledge of given object catalogues or ontologies (KOKLA 2006). Such a process is not adequate and not feasible if arbitrary data sets, e.g. downloaded from the Internet have to be integrated. RODRIGUEZ & EGENHOFER (2003) use equality and similarity measures to determine relations between classes from different ontologies. Another method to automate the integration process is a so called instance-based or extensional determination of schema transformation rules (VOLZ 2005, DUCKHAM & WORBOYS 2005). The underlying idea of this approach is that, if two objects have an identical name and / or geometrically coincide, then they probably also have something in common on the semantic level. By contrast FONSECA et al. (2006) presented a framework for measuring the degree of interoperability between geo-ontologies, which only compares the descriptions of the ontologies and not the data themselves. The drawback of this approach is, that in the general case, it can not be assumed, that the names or descriptions of objects in different data sets are the same – except objects with a unique given name like names of cities or roads. Thus using the geometric relations to infer a semantic relation can be more promising. VOLZ (2006) used this approach to link two linear data sets of similar scale. INTRODUCTION OF TEST DATA For this work two topographic geodata sets, ATKIS and GDF data, were used: whereas ATKIS (Authoritative topographic cartographic information system) data provides a basic set of topographic objects and is available for the whole area of Germany in four different scales (e.g. 1:25K, 1:50K, 1:250K and 1:1000K), GDF (Geographic Data Files) data was specially developed for purposes of vehicle navigation and is captured for most areas in Western Europe. In our work, we used the most detailed scale of 1:25K that exists for both data sets. For the determination of semantic transformation rules between the above introduced data sets, the results of a simple geometric overlay of data covering the same geographical extent are used in the first case. For the second case we used data of different extent. Our test areas are located within the urban area of Hanover and have a size of 25 km2. ATKIS as well as GDF model objects using all geometric types: points, lines and polygons. In this analysis only polygon objects from both data sets were used. Tab. 1 lists the object groups and the quantities of objects in the respective groups exemplarily for the first analysis case. The obviously different number of objects in both data sets indicate Kieler, B. : A geometry-driven approach for the semantic integration of geodata sets. In: Proc. of 23rd International Cartographic Conference, 4-10 August 2007, Moscow, Russia. different modelling techniques, namely that the ATKIS data are modelled in much greater detail than the GDF data, or, vice versa, that GDF contains many aggregated objects. In the analysis process we assume that the data sets are organized in layers. That means, the objects of the data sets are not modelled in a tessellation. From this follows that at one location more than one object class can exist, as illustrated in Fig. 1. This is especially true for administrative boundary objects that often encompass larger areas and are so called “container objects”. Tab. 1: Used object groups and quantities from ATKIS and GDF data sets in the test area for the first case Fig. 1: Simplified representation of the layer structure METHOD OF THE GEOMETRIC OVERLAY For the identification of corresponding objects a simple geometric overlay of the data sets representing the same geometric extent is done. Due to the data organization in layers an intersection of the data sets returns more than one matching candidate which could cause difficulties for the further analysis. But not only the layer structure, also the geometric Kieler, B. : A geometry-driven approach for the semantic integration of geodata sets. In: Proc. of 23rd International Cartographic Conference, 4-10 August 2007, Moscow, Russia. discrepancies at the object boundaries themselves, causes an increasing number of possible matching candidates, because the adjacent objects will be taken into account. To restrict this number of correspondences and to improve the results the additional geometric criterion area, especially the intersection area I and object size O, is introduced in the analysis. To accomplish finding corresponding objects all objects of data set D1 (ATKIS) have to be intersected with all layers of data set D2 (GDF) and vice versa. In this process the overlay ratios of the intersection area and the size of the objects as illustrated in Fig. 2 and Eq. 1 are analysed to get further information about possible matching partners. RO,1 = I ⋅100% O1 and RO,2 = I ⋅100% O2 Eq. (1) Fig. 2: Different ratios between the intersection area I and object sizes O. j=1 % 80 % 80 2 , 1 , ≥ ∧ ≥ O O R R ; j=2 % 100 2 , = O R ; j=3 % 100 1 , = O R Taking small geometric differences into account, we consider objects as matching, when the ratio is 80% or better. In the case of a 1:1 relationship the following condition has to hold: RO,1 ≥ 80%∧ RO,2 ≥ 80%. But if the data sets are differently modelled and as described above contain more aggregated objects than the other data set, the search for 1:1 relations returns only few matching candidates. In our example only seven objects meet the condition. In Fig. 3 some examples of such relations of selected object classes are presented. Kieler, B. : A geometry-driven approach for the semantic integration of ge