Treatments and Mergers in the Spatial Data Framework for Semantic Interoperability of Geographic Information Systems

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1 Treatments and Mergers in the Spatial Data Framework for Semantic Interoperability of Geographic Information Systems Miss Kholladi Sirine &Pr Kholladi Mohamed-Khireddine Mentouri University - Constantine, MISC Laboratory . kholladi@yahoo.fr, kholladi@umc.edu.dz & ksisisi@hotmail.fr Abstract. Interoperability is necessary for many applications in the field of geographic information systems and systems of decision support. It involves sharing and reuse of data from various heterogeneous information systems. Ontologies are emerging as an important solution to build a body of knowledge shared and reusable that support their interaction. They define the common terms of representation providing a shared understanding of scope among user groups. In this paper, we present a general framework for a new mediation system capable of managing semantic interoperability between different information systems, particularly in geographic information systems based on ontologies. We will apply certain techniques in our approach can ensure cooperation between ontologies see the data to achieve semantic interoperability, such as merging and mapping. They are used to allow data sharing between heterogeneous knowledge bases, information reuse of these bases and stop-loss information to ensure the cooperation of an interoperable information system. Keywords. GIS, Semantic Interoperability, Cooperation, Mediation, Ontology, Mapping and Fusion. 1 Introduction Currently, there are many geographic databases that cover the same area of real world data or different geometric representations. The multiplicity and independence of geographic databases pose today a number of problems. The fact that there is no link between spatial objects it implies: a repetition of operations updates, a lack of consistency between different comics, and an inability to perform analyzes at several levels of detail. In this case, interoperability between these information systems is complicated because of the heterogeneity of different data sources and context. So the problem is: 1. How to provide an overall context of heterogeneous data distributed in space? 2. How to create cooperation between the resources exchanged across different systems to facilitate interoperability? 3. How to facilitate and simplify the exchange between geographic data? There are two main methods to facilitate interoperability and information sharing. The first was the solution of global standardization of data via standard and centralized standards. Moreover, as the semantics is an important cooperation in information systems, these semantics allows distributed systems to combine received information with local information or heterogeneous and treat all in a comprehensive and provided a data modeling regardless of their degree of structure. Interoperability and standardization are sometimes used interchangeably for each other, although they should be differentiated. Interoperability through the use of spatial information is pervasive in a field about research in GIS. One of interoperability facing most often during the exchange of geographic data, in the field of geo information, abbreviations such OGC, GML, WMS and WFS are frequently used in connection with the concept of interoperability. An organization (Open Geospatial Consortium) was formed well established in this field from the early ninety, trying to create interfaces independent systems and thereby increase the global availability of geo data based on existing standards in the computing world. A specification is successfully implemented Web Map Service (WMS). This has been successfully implemented by several manufacturers and several open source projects. The most important standard is the Geography Markup Language (GML). GML provides a mechanism for modeling and exchange of geo-oriented data vector. Specifications Web Feature Services (WFS) is an example for such a service. The emergence of the web environment, interoperability is essential for many applications, including geographic information systems that require the integration of traditional information systems and spatial [01] & [02]. Various data sources can be found online, including Web pages, semi-structured documents (XML, RDF, OWL, etc.) and spatially referenced data. Interoperability is evident in some applications in the field of geographic information systems. It implies the need to share and reuse data from various heterogeneous information systems. Ontologies are emerging as a compelling solution for building a body of 1 41

2 knowledge shared and reusable that support their interaction. They define the common terms of representation providing a shared understanding of scope among user groups. They are used to allow data sharing between heterogeneous knowledge bases, information reuse of these bases and stop-loss information to ensure the cooperation of an interoperable information system. Information systems, designed and developed by different organizations, are typical sources of autonomous and heterogeneous data. In this case, interoperability between these information systems [03] is complicated by the heterogeneity of data sources see different contexts [04]. Interoperability involves accessing; manipulating and sharing data across heterogeneous sources requires semantic mediation between sources of information. In this paper, we present a general framework for a new mediation system capable of managing semantic interoperability between different information systems, particularly in geographic information systems based on ontologies. We will apply certain techniques in our approach can ensure cooperation between ontologies see the data to achieve semantic interoperability [05], [06], [07], [08] & [09], such as merging and mapping. We describe our proposal for a new approach. We begin by positioning the level of our proposed system through approaches based on ontologies and semantic mediation, focusing on new contributions it brings. 2 - Approach proposed mediation Proposed approach based on ontologies We are interested in interoperability at the semantic level in geographic information systems. Our solution is based on the addition of a semantic layer to all data irrespective of their degree of structuring. This layer provides background information in a language understandable by machines as well as by the user, is a mediation approach based on ontology. Ontologies have been used in various fields and for different purposes. Their most common uses are classified, according Uschold, into three categories: communication, interoperability and systems engineering [10]. They also paid off within systems of knowledge bases and Semantic Web, figure 1 shows the level of our mediation system based on ontologies interviewed by a SPARQL query language. After the comparison between different approaches to interoperability, we distinguished three levels to represent the 2 semantics in an interoperable system, such as [11]: The overall level to define the domain or application theme, such as thematic layers of geographic information systems (the road works, maps of road networks, power grids maps, city maps, maps of networks river, etc.). Local level to define the various sources of contexts provided by the participants of Cooperation (context producer). The top level is the application to define the use of context (user context). Figure 1. The level of our semantic approach based on ontologies Mediated contexts The mediator's role is to hide the heterogeneity and distribution of data sources. The mediator must ensure the following tasks: locate appropriate data sources; accept requests sent by users; decompose and optimize queries (distributed optimization); send the process to be executed by the adapters of different sources; combine (reconstruct) the results of adapters; possibly make a few extra steps; and ensure scalability when adding or updating a source Goal of a mediator of contexts in our system The mediator of contexts is suitable for environments where information resources can probably change, appear and disappear. These characteristics are suitable for the evolution of a geographic environment. It is also characterized by a support mechanism through the semantics of unification or reconciliation contexts. Mediated 42

3 contexts can be regarded as a semantic-oriented approach. While interoperability for upkeep of the meaning of information exchanged, this is what we call semantic interoperability. A commonly accepted definition for semantic interoperability, "it gives meaning to the information exchanged and ensures that this is common sense in all systems between which trade must be implemented" [12], [13] & [14]. Consideration of this semantics enables distributed systems to combine received information with local information and bring consistency of treatment of all The objective of using the ontology In recent years, the use of ontologies is increasingly used to solve the problem of sharing semantics and also to facilitate semantic interoperability between different systems. The ontology is used as an intermediate model for translation between models of different collections of objects. It refers to the exchange format between systems. The advantage of using ontology allows us to benefit from an inference capabilities it offers, and secondly to use a tool of reasoning. These capabilities will be exploited, especially during the construction of the global ontology concepts or during query processing. Indeed, the combination rules of the ontology with information on the capabilities of sources to construct queries optimized equivalent. 3 - Conceptual architecture of the proposed system Our system of interoperation is divided into three levels (see figure 2): Application level: represents requests from users and also for different applications (layer consumer). Level Mediation: the intermediate layer between users and participants in the cooperation of geographic information systems (intermediate layer). Level of geographic information systems: the approach is a mediation context then each producer of a geographic information system participating in the cooperative must have a context, which should define the semantics of shared data in cooperation (layer supplier). 3 Figure 2. Mediation architecture concepts based on ontologies Definition of our system Our approach based on a system of mediation contexts, can adopt structured information sources such as OWL or semi-structured as XML. It is based on the OWL data model because it allows for complex semantic relations between different contexts, which provide reasoning skills needed to resolve heterogeneities between the different contexts of suppliers. Knowing that, our mediation system contexts are based on ontologies. Each source of local information is described in a context using a local ontology that provides a view on the source. In the case where the sources are described using XML Schema, OWL format will be generated semi-automatically to build the ontology. We aim to represent a common geographical area and compatible between geographic information systems of the cooperating parties, we offer a comprehensive ontology and knowledge to take include a vocabulary of concepts with a formal and precise specification of their meaning from local ontologies. A local ontology allows a user (consumer) to specify their needs by sending queries to the mediator to use the information or data needed by other systems of cooperation. A geographic information system participant must provide information tailored to the needs of other systems and updates based on ontology of contexts (application). 43

4 3.2 - Level of mediation We have defined a semantic framework for mediation; it consists of a description of content and context-based ontology mediation components of information processor and query processing [15]. The mediation components are used to find appropriate responses to requests and to reconcile the semantic differences among heterogeneous data sources. A description of context and ontology provides the common semantics for mediation components. The processor QUERY restate and submit queries on local data repositories and combine the results of sub queries submitted The specification and ontologies in our approach Ontologies are emerging as an important tool to build a body of knowledge shared and reusable that support their interaction. This importance stems from the fact that the ontology defines the common terms of representation providing a shared understanding of scope among user groups. They describe the concepts (classes) and relations (properties) that a group of information systems can be used as a semantic basis on which they can communicate and exchange data. For example, a data provider may use the words of a shared ontology to describe objects, enabling a potential receiver of data to correctly interpret the semantic content related to the data provider. Similarly, a data receiver can use a shared ontology to specify its demands and interpret the results returned. Moreover, ontologies allow declarative and formal descriptions of common terms, reinforcing support when taking into account the reasoning automatic or semiautomatically on a shared data area. It allows the identification of local sources that contain important information about a domain such as land, roads, buildings, rivers, parks, etc. The ontology provides support to identify data sources that can provide information on local semantics of the query. Second, recovery operations object can be used to extract the appropriate objects and convert them into their representation by ontology. This involves using the knowledge stored in ontology to determine the spatial operations that are allowed on space demand. The ontology can be used as an intermediate model for translation between models of different collections of objects. It can be used to define the format for exchange between systems. 4 - Engineering and construction of ontologies Different methods of ontologies have been proposed (e.g. METHONTOLOGY [16], NeOn). They are based on software development methods. The crucial steps are [17]: a conceptualization phase conducted in close collaboration with experts in the field represented by an evaluation phase and a perpetual cycle to improve the ontologies. Other method of constructing ontologies semiautomatic take knowledge as a source of a corpus of domain specific text and applies methods of text mining for ontology of this domain Specification language ontologies in our system The clarification of ontologies is by means of language. A key decision in the process of developing ontologies is to choose the language in which the ontology will be expressed and used. As the range of possible choices for representation languages and ontology specification is very broad, it should choose one that meets the requirements and following technical criteria: readability, the ability to make inferences that is i.e. allow data processing to determine the logical deductions possible [18]. In our approach we use the language most commonly used among the languages developed for ontologies: OWL. It allows an interpretation of Web content by machines greater than that offered by XML, RDF and RDF Schema (RDF- S), by providing additional vocabulary with a formal semantics Types of used ontologies The ontology is a global ontology. In our architecture, we used a global ontology to describe the scope of global cooperation and allow the mediator, the semantic processing of a query. It describes the vocabulary related to a generic domain (e.g. geographical, spatial, medical, business, etc.). The ontology is a generic local ontology. In our approach, it expresses the context seen the scope of a local geographic information system called meta-ontology. Generic knowledge it conveys less abstract than those carried by the domain ontology, but nevertheless quite general for reuse across different domains. The generic ontology that we build can 4 44

5 implement a terminology of the field concerned by interoperability in order to avoid semantic conflicts, to capitalize knowledge, and finally to facilitate the sharing semantics and communication between different systems geographical information of the cooperating parties. The ontology of context is application ontology. Application ontology provides the finest level of specificity, that is to say, it is dedicated to a specific scope within an area (geographic). It aims to describe the context of a concept of the local ontology. Thus, each concept of a local ontology, ontology is associated contexts. Thus, the establishment of a contextual ontology is easy. This is a simple ontology, which models the different semantic properties of a concept of the local ontology and their relationships. This separation between ontology and local ontology of contexts can resolve conflicts related to contexts of users in their queries and other conflicts in the update of context by suppliers or geographical information systems to the participants cooperation by the ontology of contexts. Heterogeneities at the ontological level include different models in how to design a single entity see the same object, different descriptions of concepts, regularity of synonyms, homonyms or variations in the coding. Geographic information is characterized by rapid connected by real-world phenomena. In terms of updates of data or spatial information is a necessary operation. Within this framework, we proposed ontology of contexts derived from local ontology to facilitate updates or changes of spatial objects and also to solve the problem of semantic heterogeneity at the supplier level. Ontologies must be changed when they no longer meet the needs of a participant of a geographic information system as a semantic reference, including the integration or the updating of new concepts or new properties, for introduce a new classification system and finally when editing constraints or changing the definition domain or area of property values Correspondence between ontologies We have applied certain techniques on ontologies in our approach can ensure cooperation between the ontologies to achieve semantic interoperability, such as merging and mapping ontologies. They are used to allow data sharing between heterogeneous knowledge bases and for the re-use of these databases and to minimize loss of information. The definition most relevant is probably that of Noy for whom ontology mapping is a process that specifies a semantic convergence between different ontologies to extract correspondences between certain entities [19]. In our approach, the mapping performed between ontologies of contexts may occur as a result local ontology. The merging of ontologies to create a new ontology, called the merged ontology taking knowledge ontologies sources. The merging of ontologies in our proposal is the creation of a new ontology (global ontology) from two sources ontologies (local ontologies) [20]. The resulting ontology unifies and replaces the original ontologies. The most common approaches use the union or intersection. In the union approach, the resulting ontology contains the union of entities from the original ontologies and assumes resolved differences in representation of the same concept [21] SPARQL query processing SPARQL (Query Language for RDF) is an RDF query language, standardized by the W3C in January This language allows to query any element of the triplet formed by RDF. This is the essential link that allows you to manipulate data. It is somewhat related to SQL that he borrows some of its syntax, the world of relational databases. SPARQL allows for example to extract a sub graph from an RDF graph or simply form another Process request processing Both figures 3 and 4 show the process user requests. This process consists of two phases: Phase 1: the mediator sends the request to the applicant identified local ontologies and enjoy the time to research it uses to get the results of a mapping process in which he performs the mapping between different data sources and solve the problem of complexity of data structures (ontologies of contexts) while increasing 45

6 the response time of the application requester. Phase 2: if the answer in the previous phase not granted then the mediator should review the request to the global ontology that the latter guarantees the applicant response because it results from two processes mapping and merging ontologies it involves providing a mapping between different data sources and ensure the sharing of data to create a vocabulary to minimize information loss. and minimize the maximum possible based on the rules and properties of relational algebra in order to save the time needed for their implementation. Query Decomposition - Decompose the global query into local sub queries using the domain ontology (replace each concept by its equivalent in the ontology), and using the mapping rules. Generator Implementation Plan - Its role is to generate a set of query execution plans. The adoption of a cost model to estimate the execution time of the query associated with each plan. Query Processor - Allows you to send each sub query to the appropriate source via a communications protocol and run on a execution plan chosen (often the most optimal plan). Composer of results - After running each query in the source cited, the composer brings together the results returned by different sources into one final result which will be delivered to the user. Results in XML SPARQL-In, you can obtain the final results in XML format. We have a simple example of a SPARQL query as shown in figure 5. Figure 3. Process of querying ontologies 6 Figure 4. Processing of requests Query translation process by the mediator Translation of SPARQL query - It has as an essential task, receiving a user request written in a particular context and expressed in natural language or a semi-formal language, and convert it into SPARQL RDF. Parsing of requests - Analyze and check the syntax of the request and ensures compliance with the rules of syntax of SPARQL. Query Optimization - Each user expresses his query its way, the role of the optimizer is here to normalize the requests of the users PREFIX vcard: SELECT?prenom FROM systeme.umc.edu.dz/mohamed- Khireddine.Kholladi/inalco/XML/RDF/ Exemples/vcardCat.rdf WHERE {?xvcard:n?c.?c vcard:given?prenom.} Which reads: search in the file "vcardcat.rdf" all channels (here denoted by "? first_name") such that there exists in the graph corresponding two triples of the form "? x vcard: N? c" and "? c vcard: Given? first_name", With the same value for "? c". Execution: sparql - query vcard1.rq first_name ============= "Malika" "Omar" XML result In this section, we presented our proposal based on techniques that were available to achieve semantic interoperability. There are two 46

7 main concepts that are the mapping of ontologies, whose objective is the representation of mappings between ontologies. This allows querying knowledge bases as heterogeneous or homogeneous autonomous databases using a common interface. The merging of ontologies, the goal is to collect mergers between ontologies for sharing of spatial data and create a common vocabulary shared (global ontology). Subsequently, we will implement the techniques used in our mapping preposition, fusion of ontologies and querying by SPARQL queries. a son (a root class owl: Thing cannot have brothers). The creation of the first son of the Thing class is the class device specified by the following syntax: (see figure 6) Figure 6. Creating a hierarchy of classes Classes are defined as follows: Figure 5. Result at SPARQL query in XML 5 - The creation of the ontology Our ontology was created at the location mentioned in the following markup: The Properties object (ObjectProperty) OWL properties represent relationships. There are two main types of properties, object properties and data type properties (Datatype). Figure 7 depicts an example of the type of object property. Once the properties of classes created from DataProperties tab, you specify the type and the class they are assigned Classes OWL classes are interpreted as sets that contain individuals. They are described using descriptions (mathematical) formal setting out with precision the conditions for the adaptation of the class. In OWL, classes are descriptions that specify the conditions that must be met by an individual to be a member of the class. The creation of ontology classes, resulting in a succession of adding subclasses: son or brothers. Once the class to which you want to assign a son or brother is selected, click on the desired operation, the first addition of classes can only be 7 Figure 7. Creating a hierarchy of object properties The object property represents the name of relationship and puts property means its source class. 47

8 The property area represents its membership class and puts the property designates its type and restrictions if any The properties of data type (Datatype) Properties data type bind an individual to a value of data type Datatype. In other words, the data values, we describe these characteristics of properties that apply to data properties thereafter. Data type properties can be created using the figure 8. We properties using the properties of data type (datatype) to describe different spatial characteristics (distance, direction, etc.). Figure 9 shows an example of a geographical ontology editor Protégé visualized. Figure 8. Creating a data property Process mapping and enrichment of fusion The aim of our proposal based on semantic mediation architecture based ontologies with the use of enrichment techniques between the latter for the interoperability of geographic information systems is to provide users with integration architectures and virtual interoperable tools Software enrichment PROMPT PROMPT is a tool that performs interactive type processes of enrichment of ontologies (the merger, mapping, extraction, and also the comparison of ontologies). The set of phases involved in this process comprises the following steps: The user selects one of the suggestions. Figure 10 shows the list of processes performed by PROMPT. Figure 9. The visualization of ontology in Protégé (classes and relations between them) The user must load the address (name) selected sources of ontologies in an enrichment operation to identify the characteristics of choice and perform the operation suggested by PROMPT. The system performs the action requested by the user and automatically executes additional changes derived from this action. The system creates a new list of suggested actions by the user based on the new structure of the ontology. It determines the conflicts presented by the last action, possible solutions to these conflicts and then present them to the user. a set of possible conflicts resulting from the application of these operations (name conflicts, redundancy in the class hierarchy). 8 48

9 Figure 10. Enrichment operations in PROMT. subclass and instance) and also choose the type of mapping (metadata, component or step by step) corresponding to the mapping according to our needs as the figure 13. Implementation and operation of mapping: The component analysis applied is an important task in this phase. In conclusion, in the phase system, the component is a logical order to check consistency and inferences in the global ontology recently created in the last phase Ontologies Mapping Ontology mapping is a process that specifies a convergence see a semantic correspondence between different ontologies. These correspondences are expressed by introducing axioms formulated in a specific language. Three main phases can be distinguished in this process: discovery, representation and exploitation with the execution of the mapping as shown in figure 11. Figure 13. The discovery of the mapping Figure 11. Mapping type The discovery of mapping: we must identify the ontology mapping, in this phase allows us to choose ontologies for mapping according to our needs (see figure 12). The mapping is a solution for a large volume of data conflicts resulting from the diversity and complexity of spatial data structures to provide a mapping between different data sources to ensure the minimization of conflicts (see figure 14). Figure 12. The discovery of the mapping Representation of the mapping: In this phase, we must identify the level mapping (class, 9 Figure 14. The discovery of the mapping 49

10 Merging process of ontologies The fusion process involves the task of merging the local ontologies enriched to create a shared vocabulary and a global ontology by defining three major phases: the initial phase (the two local ontologies to merge), the integration phase and the final phase (global shared ontology). These three phases are semi-automatic in different parts of the fusion process, which are presented in figure 2. In the initial phase, each spatial ontology (local ontology after the process of mapping between ontologies of contexts) open separately into the semantic enrichment process (melting). Figure15 show the two local ontologies that are used in the fusion process. Figure 16. The merging process of ontologies Figure 15. The process of merger between two local ontologies The merger between classes is a semiautomatic task involves selecting two classes (subclasses or instances) and then we run the fusion process. In the second phase of integration the process responsible for the merger of two local ontologies to create standardized global shared ontology is the final step in this process. The component analysis is applied in this phase. In conclusion, in the phase system, the component is a logical order applied in to check consistency and inferences in the global ontology recently created in the last phase. Figure 16 presents the overall outcome of the ontology merging process from the local ontology and the suggestion on the type of merger. The purpose of merging ontologies is to share existing collections of spatial data sets created by different institutions and agencies. However, fusion of ontologies ensures the sharing of spatial data to generate a shared common vocabulary (ontology overall) SPARQL SPARQL (an acronym derived from SQL) is a query language for interrogating databases (files) RDF, standardized by the W3C and implemented in various languages, including Java Framework. The database systems provide effective data recovery by its query language in the form of Structured Query Language (SQL). The data set in an RDF document can be retrieved by the query language called SPARQL. As with its counterparts SPARQL is also used to control the RDF document. This is a key component of the Semantic Web technology. SPARQL does nothing but take the description of what the application wants, as a question, and returns that information as a set of clips or a graph of RDF. In addition, SPARQL can query the OWL ontologies that use RDF graphs to structure. The movement led to the Semantic Web's ability to require effective queries for a large data set of relationships supported include Web resources. OWL is a set of standards for describing and modeling data and is the backbone of semantic web technologies. RDF data sets can be very large, and often are subject to complex queries with the intent to involve the extraction and otherwise invisible connections in the data. Figure 17 shows an embodiment of a SPARQL query on a geographic ontology. The design and development of a system is not as easy a task that the word design, such work requires collaboration of one or more groups of people in multidisciplinary fields. So the implementation of our proposal is considered in this paper as a 50

11 recommendation for the representation of semantic information in a cooperation of geographic information systems interoperable based mediation contexts. Figure 17. The Issuing a SPARQL It does not constitute a complete architecture for interoperation since many aspects are not presented (the communication protocol between wrapper and mediator, mediation model, the fusion algorithms and mapping, etc.). 6 - Conclusion Interoperability involves accessing; manipulating and sharing data across heterogeneous sources requires semantic mediation between sources of information. The need for semantic mediation comes from several facts: the explosive growth of the Internet allows the interconnection of a growing number of heterogeneous information systems, the diversity of data formats on the web and the need to preserve the autonomy and integrity of independent data created and maintained for specific applications. Our proposed approach was based on the possibility of incorporating space technology in a semantic framework. She moves to the scope of semantic interoperability of data while the representation of concepts and made efforts to use the potential of other sectors of semantic web technologies. In this paper, we presented a general framework of a new system capable of managing semantic interoperability between different information systems, especially geographic information systems. We applied some techniques on ontologies in our approach can ensure cooperation between the ontologies to achieve semantic interoperability, such as merging and mapping ontologies, they are used to allow data sharing between knowledge bases heterogeneous, for the re-use of these databases and to ensure the minimization of information loss. The main steps of our approach are: the definition of a semantic mediation architecture based on ontologies, the definition and creation of the ontology of contexts or applications, the generation of the local ontology by the fusion technique ontologies (ontologies application contexts), the generation of the global ontology from the local ontologies by the technique of mapping and querying the model by a SPARQL query language. In future work, we will rely achieve an implementation based on techniques for enriching ontologies belonging to different categories of operations such as merging, matching, mapping... to integrate a system of reasoning in the ontology, as Racer, FaCT + + and Hermit based on predicate logic to solve the problem of automatic enrichment. In this context a second solution is the use of intelligent agents that affect the tasks of enrichments between ontologies. We will also test other uses of ontology in the interoperability of systems such as multi-agent system where each agent has its ontology to increase interoperability through cooperation of agents. 7 - References [01] SHEEREN D., Méthodologie d évaluation de la cohérence inter représentations pour l intégration de bases de données spatiales : Une approche combinant l utilisation de métadonnées et l apprentissage automatique, Thèse doctorat informatique, Université de Paris 6, [02] DEVOGELE T., Processus d intégration et d appariement de bases de données Géographiques : Application à une base de données routières multi-échelles, Thèse de doctorat en Informatique, Université de Versailles, [03] JOUANOT F., Un modèle sémantique pour l interopérabilité des systèmes d information, In XVIIIe congrès INFORSID, France, mai [04] SHETH A.& LARSON J., Federated database systems for managing distributed, heterogeneous, and autonomous databases, ACM Computer, Sept1990. [05] BISHR Y., Semantics Aspects of Interoperable GIS, Ph.D. Dissertation, ITC, Publication, [06] OUKSEL A-M. & Sheth A., Semantic Interoperability in Global Information Systems: A Brief Introduction to the Research Area and the Special Section, SIGMOD Record, [07]. SHETH A., Changing Focus on Interoperability in Information Systems: From 11 51

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