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2006, Electronic Notes in Theoretical Computer Science
The way developers define architecture, execute architectural strategy, and record the results make a critical difference in the ability to deal with information and knowledge. In this context, integrating databases is very important indeed, but the different semantics they possibly have usually complicates administration. Therefore, recovering information through a common semantics becomes crucial in order to realise the full knowledge contained in the databases. In this paper, we describe and illustrate a proposal on the use of layered architectures to integrate knowledge from heterogeneous sources. We illustrate how the process might be facilitated by applying ontologybased comparisons as part of the components' behaviour.
LADSEB-CNR Technical Report, 2002
More and more enterprises are currently undertaking projects to integrate their applications. They are finding that one of the more difficult tasks facing them is determining how the data from one application matches semantically with the data from the other applications. Currently there are few methodologies for undertaking this task – most commercial projects just rely on experience and intuition. Taking semantically heterogeneous databases as the prototypical situation, this paper describes how ontology (in the traditional metaphysical sense) can contribute to delivering a more efficient and effective process of matching by providing a framework for the analysis, and so the basis for a methodology. It delivers not only a better process for matching, but the process also gives a better result. This paper describes a couple of examples of this: how the analysis encourages a kind of generalisation that reduces complexity and how ontological relativity can be used to enhance this. Finally, it suggests that the benefits are not just restricted to individual integration projects: that the process produces models which can be used as to construct a universal reference ontology – for general use in a variety of types of projects.
Research in interoperability has been motivated by the growing heterogeneity of computing systems. Heterogeneity can occur in many levels and each level of heterogeneity requires an isolated or integrated approach for solution. In this paper, we propose the specification of a formal ontology for the information related to a specific domain of a database system, to work together with a global scheme, developed as software layer among the different databases under consideration. To test this approach we elaborated a case study, based upon hypothetical queries submitted to relational and heterogeneous databases, with data on soil domain, aiming at identifying the kinds of soil most appropriate to a certain culture. The case study demonstrated that the semantic conflicts were circumvented and the integration of the databases was easily reached.
International Web Rule Symposium (RuleML 2015), 2015
The progress of information and communication technologies has greatly increased the quantity of data to process. Thus, managing data heterogeneity is a problem nowadays. In the 1980s, the concept of a Federated Database Architecture (FDBA) was introduced as a collection of components to unite loosely coupled federation. Semantic web technologies mitigate the data heterogeneity problem, however due to the data structure heterogeneity the integration of several ontologies is still a complex task. For tackling this problem, we propose a loosely coupled federated ontology architecture (FOWLA). Our approach allows the coexistence of various ontologies sharing common data dynamically at query execution through logical rules. We have illustrated the advantages of adopting our approach through several examples and benchmarks. We also compare our approach with other existing initiatives.
Interoperability is a key point for integrating heterogeneous computing systems. A usual approach proposes the integration of the conceptual databases schemes into a global conceptual scheme, to resolve syntactic and structural heterogeneity. Moreover, semantic problems can remain. Ontologies have been largely designated to overcome semantic heterogeneity. We propose the specification of a formal ontology about the specific knowledge domain to be shared among several database systems build a posteriori of the ontology specification. We reach this integration through a global scheme, developed as a software layer among the databases under consideration. To test this approach we elaborated a case study, based upon hypothetical queries submitted to heterogeneous databases, with data on soil domain, to identify the soil most appropriate to a certain culture. The results are promising, but crucial, in our approach, is the acceptance for a given community of a common vocabulary and its relationships and that are captured by the ontology and transformed to the target conceptual models.
Open Computer Science, 2015
Integrating heterogeneous data in distributed databases has been a research issue for many years. In this paper, we discuss some of these problems and propose a solution using a semantic model. This semantic model is built upon the semantic relationships between existing data. Applying these semantics enables us to take into account different dimensions of user queries and find the best possible answer for them. The proposed approach leads us to introducing…
Information Technology & Management, 2005
This paper addresses the problem of handling semantic heterogeneity during database schema integration. We focus on the semantics of terms used as identifiers in schema definitions. Our solution does not rely on the names of the schema elements or the structure of the schemas. Instead, we utilize formal ontologies consisting of intensional definitions of terms represented in a logical language. The approach is based on similarity relations between intensional definitions in different ontologies. We present the definitions of similarity relations based on intensional definitions in formal ontologies. The extensional consequences of intensional relations are addressed. The paper shows how similarity relations are discovered by a reasoning system using a higher-level ontology. These similarity relations are then used to derive an integrated schema in two steps. First, we show how to use similarity relations to generate the class hierarchy of the global schema. Second, we explain how to enhance the class definitions with attributes. This approach reduces the cost of generating or re-generating global schemas for tightly-coupled federated databases.
2001
Interoperability and integration of data sources are becoming ever more important issues as both, the amount of data and the number of data producers are growing. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Semantics refer to the meaning of data in contrast to syntax, which only defines the structure of the schema items (e.g., classes and attributes). We focus on the part of semantics related to the meanings of the terms used as identifiers in schema definitions. This paper presents an approach to integrate schemas from different communities, where each such community is using its own ontology. The approach is based on merging ontologies based on similarity relations among concepts of different ontologies. We present formal definitions of similarity relations based on intensional definitions and conclude the extensional consequences. The process of merging ontologies based on the detected similarity relations is discussed. The merged ontology is finally used to derive an integrated schema. The resulting schema can be used as the global schema in a federated database system.
2007 International Conference on Computing: Theory and Applications (ICCTA'07), 2007
The Web is replete with databases, many of which are modeled on the relational paradigm. Currently, for the purpose of simultaneous querying data from multiple databases, the federated database technique is used extensively. However, the effectiveness of such a technique is suspect when it comes to querying heterogeneous databases. Therefore, it becomes imperative to develop an efficient methodology for the semantic integration of heterogeneous online databases. This may be realized by defining a mapping from a relational database to a description that utilises the Resource Description Framework (RDF). Such a representation would be machine processable and would make the semantics as expressed by databases more explicit and, thereby, facilitate their integration.
2003
One of today's hottest IT topics is integration, as bringing together information from different sources and structures is not completely solved. The approach outlined here wants to illustrate how ontologies [Gr93] could help to support the integration process. The main benefits for an ontology-based approach are the ability to picture all occurring data structures, for ontologies can be seen as nowadays most advanced knowledge representation model the combination of deduction and relational database systems, which extends the mapping and business logic capabilities a higher degree of abstraction, as the model is separated from the data storage its extendibility and reusability
1990
Abstract A knowledge-based architecture designed to connect and correlate autonomous disparate information sources is presented. The information sources being integrated come equipped with logical front-ends that build up only those parts of a virtual global schema which are needed to process local or global requests. The schema building and translation processes are driven by respective knowledge bases.
Revista de Informática Teórica e Aplicada, 2010
2002
More and more enterprises are currently undertaking projects to integrate their applications. They are finding that one of the more difficult tasks facing them is determining how the data from one application matches semantically with the data from the other applications. Currently there are few methodologies for undertaking this task – most commercial projects just rely on experience and intuition. Taking semantically heterogeneous databases as the prototypical situation, this paper describes how ontology (in the traditional metaphysical sense) can contribute to delivering a more efficient and effective process of matching by providing a framework for the analysis, and so the basis for a methodology. It delivers not only a better process for matching, but the process also gives a better result. This paper describes a couple of examples of this: how the analysis encourages a kind of generalisation that reduces complexity and how ontological relativity can be used to enhance this. Fi...
During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately -ontology and contextual information -in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.
2010
Abstract: Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Taking semantically heterogeneous databases as the prototypical situation, the presented work describes how ontology (in the traditional metaphysical sense) can contribute to delivering a more efficient and effective process of matching by providing a framework for the analysis, and so the basis for a methodology.
4th Linked Data in Architecture and Construction Workshop (LDAC'2016), 2016
Over the last few years, the benefits of applying ontologies (semantic graph modelling) for Architecture, Engineering, Construction and Facility Management (AEC/FM) industry have been recognized by several researchers and industry stakeholders. One of the main motivations is because it eases AEC data manipulation and representation. However, a research question that still remains open is how to take advantage of semantic web technologies to interoperate the AEC/FM and other ontologies in a flexible and dynamical way in order to solve data structure heterogeneity problem. Because of this, we propose in this paper to apply a rule-based federated architecture to answer this research question.
IEEE Access, 2020
Heterogeneous database integration is the study of integrating data from multiple databases. Integrating the heterogeneous database of the same domain has three main challenges that make the heterogeneity problem difficult to solve. The three problems are Semantic, Syntactic and Structural Heterogeneity. Conventional heterogeneous database integration schemes, like De-duplication Techniques, Data Warehouse, and Information Retrieval (IR) Search technique lack the capability to solve the integration of databases completely. The only reason is they cannot deal with Semantic heterogeneity problems efficiently. The semantic Web ontology model is experimented and discussed in the article, which is based on the query execution model. The ontology modeling is divided into two phases, initially to translate the database rules according to ontology rules to find an abstract ontology model. Secondly, to extend the abstract ontology model according to the databases. The method facilitates to apply similarly SPQRAL queries to search the data in the databases. Therefore, the Jena API is used to retrieve semantically similar records. The experiment is based on the two heterogeneous Universities Library Databases. The results show the effectiveness and scalability of the methodology.
2001
Interoperability and integration of data sources are becoming ever more important issues as both, the amount of data and the number of data producers are growing. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Semantics refer to the meaning of data in contrast to syntax, which only defines the structure of the schema items (e.g., classes and attributes). We focus on the part of semantics related to the meanings of the terms used as identifiers in schema definitions. This paper presents an approach to integrate schemas from different communities, where each such community is using its own ontology. The approach is based on merging ontologies based on similarity relations among concepts of different ontologies. We present formal definitions of similarity relations based on intensional definitions and conclude the extensional consequences. The process of merging ontologies based on the detected similarity relations is discussed. The merged ontology is finally used to derive an integrated schema. The resulting schema can be used as the global schema in a federated database system.
Journal of Computer Science and Technology - JCST
The term "Federated Databases" refers to the data integration of distributed, autonomous and heterogeneous databases. However, a federation can also include information systems, not only databases. At integrating data, several issues must be addressed. Here, we focus on the problem of heterogeneity, more specifically on semantic heterogeneity - that is, problems rela ted to semantically equivalent concepts or semantically related/unrelated concepts. In order to address this problem, we apply the idea of ontologies as a tool for data integration. In this paper, we explain this concept and we briefly describe a method for constructing an ontology by using a hybrid ontology approach.
Integrating data from a Federated System is a very complex process that involves a series of tasks. Characteristics such as autonomy of the information sources, their geographical distribution and heterogeneity are some of the main problems we face to perform the integration. In this paper we focus on the problem of heterogeneity, more specifically on semantic heterogeneity. The semantic heterogeneity makes the integration difficult because of its bearing problems on synonymous, generalization/specialization, etc. Here, we briefly explain our three level approach to solve these problems. Then we show the structure of software components used to implement our supporting tool.
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