Interoperability in GIS Enabling Technologies

Transcription

1 Interoperability in GIS Enabling Technologies Ubbo Visser, Heiner Stuckenschmidt, Christoph Schlieder TZI, Center for Computing Technologies University of Bremen D Bremen, Germany {visser heiner Abstract: We present a new approach, which proposes to minimize the numerous problems existing in order to have fully interoperable GIS. We discuss the existence of these heterogeneity problems and the fact that they must be solved to achieve interoperability. These problems are addressed on three levels: the syntactic, structural and semantic level. In addition, we identify the needs for an approach performing semantic translation for interoperability and introduce a uniform description of contexts. Furthermore, we discuss a conceptual architecture BUSTER (Bremen University Semantic Translation for Enhanced Retrieval), which can provide intelligent information integration, based on a reclassification of information entities in a new context. Also, we demonstrate our theories with the implemented prototype of our approach sketching a real life scenario. Lastly, we will bridge the gap between terminological and spatial reasoning and show how a combination of both would benefit users. Keywords: Semantic Interoperability Introduction Over the last few years much work has been conducted in regards to the research topic fully interoperable GIS. Vckovski (1998) for example gives an overview of the problems regarding data integration and geographical information systems. Furthermore, numerous contributions within the proceedings of the 2nd International Conference on Interoperating Geographic Information Systems (Interop99) (Vckovski, Brassel, & Schek, 1999) are related to this topic (e. g. Wiederhold, 1992; Landgraf, 1999). GIS s share the need to store and process large amounts of diverse data, which is often geographically distributed. Most GIS s use specific data models and databases for this purpose. This implies, that making new data available to the system requires the data to be transferred into the system s specific data format. This is a process which is very time consuming and tedious. Data acquisition, automatically or semi-automatically, often makes large-scale investment in technical infrastructure and/or manpower inevitable. These obstacles are some of the motivation behind the concept of information integration. The solution of information integration applies here because existing information can be accessed by remote systems in order to supplement their own data basis. However, before we can establish efficient information integration, difficulties arising from organizational, competence questions and many other technical problems have to be solved. Firstly, a suitable information source must be located which contains the data needed for a given task. Once the information source has been found, access to the data contained therein has to be provided. Furthermore, access has to be provided on both a technical and an informational level. In short, information integration not only needs to provide full accessibility to the data, it also requires that the accessed data may be interpreted by the remote system. While the problem of providing access to information has been largely solved by the invention of large-scale computer networks, the problem of processing and interpreting retrieved information remains an important research topic. This paper will address three of the problems mentioned above: finding suitable information sources, enabling a remote system to process the accessed data, and solutions to help the remote system interpreting the accessed data as intended by its source. In addressing these questions we will explore technologies, which enable systems to interoperate, always bearing in mind the special needs of GIS. Enabling Technologies Our modern information society requires complete access to all information available. The opening of information systems towards integrated access, which has been encouraged in order to satisfy this demand, creates new challenges for many areas of computer science. In this paper, we distinguish different integration tasks that need to be solved in order to achieve complete integrated access to information: Syntactic Integration: 1

2 The typical task of syntactic data integration is, to specify the information source on a syntactic level. This means, that different data type problems can be solved (e. g. short int vs. int and/or long). This first data abstraction is used to re-structure the information source. The standard technologies to overcome problems on this level are wrappers. Wrappers hide the internal data structure model of a source and transform the contents to a uniform data structure model. Structural Integration: The task of structural data integration is, to re-format the data structures to a new homogeneous data structure. This can be done with the help of a formalism that is able to construct one specific information source out of numerous other information sources. This is a classical task of a middleware, which can be done with CORBA (OMG, 1992) on a low level or rule-based mediators (Wiederhold, 1992) on a higher level. Mediators provide flexible integration of several information systems such as database management systems, GIS, or the World Wide Web. A mediator combines, integrates, and abstracts the information provided by the sources. Normally wrappers encapsulate the sources. Mediators provide flexible integration of several information systems such as database management systems, GIS, or the World Wide Web. A mediator combines, integrates, and abstracts the information provided by the sources. Normally wrappers encapsulate the sources. Over the last few years numerous mediators have been developed. A popular example is the rule-driven TSIMMIS mediator (Chawathe et al., 1994), (Papakonstantinou, Garcia-Molina, & Ullman, 1996). The rules in the mediator describe how information of the sources can be mapped to the integrated view. In simple cases, a rule mediator converts the information of the sources into information on the integrated view. The mediator uses the rules to split the query, which is formulated with respect to the integrated view, into several sub-queries for each source and combine the results according to query plan. A mediator has to solve the same problems, which are discussed in the federated database research area, i.e. structural heterogeneity (schematic heterogeneity) and semantic heterogeneity (data heterogeneity) (Kim & Seo, 1991), (Naiman & Ouksel, 1995), (Kim, Choi, Gala, & Scheevel, 1995). Structural heterogeneity means that different information systems store their data in different structures. Semantic heterogeneity considers the content and semantics of an information item. In rule-based mediators, rules are mainly designed in order to reconcile structural heterogeneity. Where as discovering semantic heterogeneity problems and their reconciliation play a subordinate role. But for the reconciliation of the semantic heterogeneity problems, the semantic level must also be considered. Contexts are one possibility to describe the semantic level. A context contains meta data relating to its meaning, properties (such as its source, quality, and precision), and organization (Kashyap & Sheth, 1997). A value has to be considered in its context and may be transformed into another context (so-called context transformation). Semantic Integration: The semantic integration process is by far the most complicated process and presents a real challenge. As with database integration, semantic heterogeneities are the main problems that have to be solved within spatial data integration (Vckovski, 1998). Other authors from the GIS community call this problem inconsistencies (Shepherd, 1991). Worboys & Deen (Worboys & Deen, 1991) have identified two types of semantic heterogeneity in distributed geographic databases: Generic semantic heterogeneity: Heterogeneity resulting from field- and object-based databases. Contextual semantic heterogeneity: Heterogeneity based on different meanings of concepts and schemes. The generic semantic heterogeneity is based on the different concepts of space or data models being used. In this paper, we will focus on contextual semantic heterogeneity, which is based on different semantics of the local schemata. In order to discover semantic heterogeneities, a formal representation is needed. Lately, WWW standardized markup languages such as XML and RDF have been developed by the W3C community for this purpose (W3C, 1998), (W3C, 1999). We will describe the value of these languages for the semantic description of concepts and also argue that we need more sophisticated approaches to overcome the semantic heterogeneity problem. Ontologies have been identified to be useful for the integration/interoperation process (Visser, Stuckenschmidt, Schuster, & Vögele, 2001). The advantages and disadvantages of this technology will be discussed in a separate subsection. Ontologies can be used to describe information sources. However, how does the actual integration process work? In the following, we use the term semantic integration or semantic translation, respectively, to denote the resolution of semantic conflicts, which make a one to one mapping between concepts or terms impossible. 2

3 Our approach provides an overall solution to the problem of information integration, taking into account all three levels of integration and combining several technologies, including standard markup languages, mediator systems, ontologies, and a knowledge-based classifier. In order to overcome the obstacles mentioned earlier, it is not sufficient to solve the heterogeneity problems separately. It is important to note that these problems can only be solved with a system taking all three levels of integration into account. In the final paper we describe the above-mentioned technologies in detail and discuss in which form they can contribute towards interoperability of systems. BUSTER A middleware for interoperability The BUSTER approach addresses the above-mentioned questions by providing a common interface to heterogeneous information sources in terms of an intelligent information broker. A user can submit a query request to the network of integrated data sources. In this query phase several components of different levels interact. The first step is to find the desired information source. To do this, BUSTER uses a certain ontology, the lookup ontology, which is built according to a metadata base. Metadata, i.e. data describing a data source, are often used to organize and manage large collections of data sources. Typically, such metadata catalogues are based on standardized metadata formats such as the Dublin Core metadata format (Dublin Core Metadata Initiative, 2000). In a second step user queries are matched against this ontology. If the matching succeeds, the broker establishes a connection to the actual information source. If the matching fails, the broker decides that there is no valuable information available. Otherwise, in the last step of the BUSTER approach, the semantic translation is started. Each data source is represented by a specific ontology, the so-called source ontology. It contains an explicit description of the concepts covered by the data source. In addition, it contains information about the structural and syntactic details of the data source. All these ontologies (especially the source ontologies) have to use the same vocabulary describing the concepts and the metadata concerning the data source. We will now take a closer look at the conceptional organization of the BUSTER system and outline three levels of interacting components (compare figure 1). On the syntactic level, wrappers are used to establish a communication channel to the data source(s), which have been found, that is independent of specific file formats and system implementations. Each generic wrapper covers a specific file- or data-format. For example, generic wrappers may exist for ODBC data sources, XML data files, or specific GIS formats. Still, these generic wrappers have to be configured for the specific requirements of a data source. Figure 1: The BUSTER approach The mediator on the structural level uses information obtained from the wrappers and combines, integrates, and abstracts them. BUSTER allows the use of different mediators, which are configured by transformation rules. These rules describe in a declarative style how the data from several sources can be integrated and transformed to the data structure of original source. On the semantic level, we use two different tools specialized for solving the semantic heterogeneity problems based on the source ontologies describing the contents of the information sources. Both tools the Feature Manipulation Engine FME, a conversion tool for geographical data formats (FME, 1999) and the MeCoTa Mediator (Wache, 1999) are responsible for the context transformation, i.e. transforming data from a source-context to a goal-context. There are several ways how the context transformation can be applied. In BUSTER we consider the functional context transformation and context transformation by re-classification. For the description of content meta-data, BUSTER uses the language OIL (Horrocks et al., 2000), which has been developed in the context of the On-To-Knowledge project ( as a proposal for a language for specifying and exchanging ontologies (Fensel et al., 3

4 2000). OIL tries to provide a core set of features, which have been widely accepted to be useful. OIL combines frame-based modeling primitives, reasoning facilities from description logics, and a tight interaction with metadata standards on the web such as RDF and XML. We use OIL to build a semantic context model of our example data by identifying a set of common properties, which can be used to define a land use class. The BUSTER System A first prototype of the BUSTER approach has been implemented. The current functionality includes ontologydriven search for information sources as well as semantic integration of geographical information sources. The prototype is built upon tools, which have been developed at the university of Manchester to facilitate the use of the OIL language: FaCT, a logical reasoning service that can be used to check ontologies for consistency and for computing subclass relations not explicitly contained in the ontology (Horrocks, 1999). The Ontology Editor OilEd (Bechhofer, Horrocks, Goble, & Stevens, 2001) providing a graphical interface for the definition of complex ontologies and a direct interaction with the FaCT reasoner in a client-server architecture. The editor is used to create meta-data models as well as context definitions used in the semantic translation step. In figure 2 we display the components of the BUSTER system from a functional point of view. A client will be able to connect to the system over the Internet and activate one of the services provided. We will now concentrate on the brokering functionality. The user is asked to restrict the defining properties of a data source in order to restrict the set of all information sources to those of interest. At the moment, the FaCT reasoner is the main inference engine of the BUSTER system. The resulting class definition is passed to the reasoner, which places the query in a hierarchy of classes. Each class is a surrogate for an information source. All classes placed in the sub tree rooted at the query class are returned, because they fulfill the constraints defined in the query. The BUSTER system presents the information sources matching the query. Figure 2: Client-Server architecture of the BUSTER prototype The information source is labeled and several services are shown. In this case, the user can now either directly view the information as an image or define a target file format the information source should be converted to. Currently, for displaying an image FME is used to create the output format. For the semantic transformation any configured mediator could be used as a standard we use MeCoTa. We will discuss a sample query to the system and show how the system will operate. Conclusions and Outlook In order to make GIS interoperable, several problems have to be solved. We argue that these problems can be divided onto three levels of integration, the syntactic, structural, and semantic level. In our opinion it is crucial to note that the problem of interoperable GIS can only be solved if solutions (modules) on all three levels of integration are working together. We believe that it is not possible to solve the heterogeneity problems separately. The BUSTER-approach uses different components for different tasks on different levels and provides an implemented and prototypical middleware for these problems. The components can be any existing systems. We use wrappers for the syntactic level, mediators for the structural level, and both context transformation rule 4

5 engines (CTR-Engines) and classifiers (mappers) for the semantic level. CORBA as low-level middleware is used for the communication of the components. At the moment, a few wrappers are available (e.g. ODBC-, XML-wrapper), a wrapper for shape files will be available soon. The current prototype has shown that this concept is useable for our purposes. One of the key issues of the BUSTER system is the ontology-driven search. The key feature behind this is terminological reasoning based on inference engines such as FaCT. Schlieder et al. (2001) proposed a new footprint for extended gazetteers and argued that they are able to reason spatially. The final paper will discuss the connections between terminological and spatial reasoning and how they can be implemented. We will show the benefits of the combination of terminological and spatial reasoning References Bechhofer, S., Horrocks, I., Goble, C., & Stevens, R. (2001). OilEd: A Reason-able Ontology Editor for the Semantic Web. Paper presented at the KI 2001, Wien. Chawathe, S., Garcia-Molina, H., Hammer, J., Ireland, K., Papakonstantinou, Y., Ullman, J., & Widom, J. (1994, 1994). The TSIMMIS Project: Integration of Heterogeneous Information Sources. Paper presented at the Conference of the Information Processing Society Japan. Dublin Core Metadata Initiative. (2000, ). The Dublin Core: A Simple Content Description Model for Electronic Resources, [Internet Webpage]. Available: Fensel, D., Horrocks, I., Harmelen, F. V., Decker, S., Erdmann, M., & Klein, M. (2000). OIL in a Nutshell. Paper presented at the 12th International Conference on Knowledge Engineering and Knowledge Management EKAW 2000, Juanles-Pins, France. FME. (1999). Semantic translation. Horrocks, I. (1999). FaCT and ifact. In P. Lambrix & A. Borgida & M. Lenzerini & R. M. ö. ller & P. Patel-Schneider (Eds.), Proceedings of the International Workshop on Description Logics (DL'99) (pp ). Horrocks, I., Fensel, D., Goble, C., Harmelen, F. v., Boekstra, J., & Klein, M. (2000). The Ontology Interchange Language OIL: The grease between ontologies. Paper presented at the 12th International Conference on Knowledge Engineering and Knowledge Management EKAW 2000, Juan-les-Pins, France. Kashyap, V., & Sheth, A. (1997). Cooperative Information Systems: Current Trends and Directions. In M. Papazoglou & G. Schlageter (Eds.): Academic Press. Kim, W., Choi, I., Gala, S., & Scheevel, M. (1995). Modern Database: The Object Model, Interoperability, and Beyond. In W. Kim (Ed.) (pp ): ACM Press/Addison-Wesley Publishing Company. Kim, W., & Seo, J. (1991). Classifying schematic and data heterogeinity in multidatabase systems. IEEE Computer, 24(12), Landgraf, G. (1999). Evolution of EO/GIS Interoperability; Towards an Integrated Application Infrastructure. Paper presented at the Interop99, Zürich, Switzerland. Naiman, C. F., & Ouksel, A. M. (1995). A Classification of Semantic Conflicts in Heterogeneous Database Systems. Journal of Organizational Computing, OMG. (1992). The Common Object Request Broker: Architecture and Specification (OMG Document ): The Object Management Group. Papakonstantinou, Y., Garcia-Molina, H., & Ullman, J. (1996, February 1996). MedMaker: A Mediation System Based on Declarative Specifications. Paper presented at the International Conference on Data Engineering, New Orleans. Shepherd, I. D. H. (1991). Information Integration in GIS. In D. J. Maguire & M. F. Goodchild & D. W. Rhind (Eds.), Geographical Information Systems: Principles and applications. London, UK: Longman. Vckovski, A. (1998). Interoperable and Distributed Processing in GIS. London: Taylor & Francis. Vckovski, A., Brassel, K. E., & Schek, H.-J. (1999, March 1999). Proceedings of the 2nd International Conference on Interoperating Geographic Information Systems. Paper presented at the INTEROP 99, Zürich. Visser, U., Stuckenschmidt, H., Schuster, G., & Vögele, T. (2001). Ontologies for Geographic Information Processing. Computers and Geosciences. Wache, H. (1999, Mai '99). Towards Rule-Based Context Transformation in Mediators. Paper presented at the International Workshop on Engineering Federated Information Systems (EFIS 99), Kühlungsborn, Germany. Wiederhold, G. (1992). Mediators in the Architecture of Future Information Systems. IEEE Computer, 25(3), Worboys, M. F., & Deen, S. M. (1991). Semantic heterogeneity in distributed geographical databases. SIGMOID Record, 20(4). 5