Using Ontologies for Semantic Data Integration (original) (raw)
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Data Integration using Semantic Technology: A use case
2006 Second International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML'06), 2006
For the integration of data that resides in autonomous data sources Software AG uses ontologies. Data source ontologies describe the data sources themselves. Business ontologies provide an integrated view of the data. F-Logic rules are used to describe mappings between data objects in data source or business ontologies. Furthermore, F-Logic is used as the query language. F-Logic rules are perfectly suited to describe the mappings between objects and their properties. In a first project we integrated data that on one side resides in a support and on the other side in a customer information system.
A Novel Approach for Semantic Integration of Data using Ontology
Indian Journal of Science and Technology, 2016
Integration process of data is recently recognized like a significant visualization of the Semantic Web explore for which researchers focus on numerous areas, such as integration of information, ontologies and databases. Objectives: Users generally requires an incorporated analysis of information accessible from various data and it was proposed to grant users with this view of data. Methods: The Meta data is created from different data sets like excel data set, RDF data set and XML data set. Findings: In this work a survey was made for integrating databases using ontology and a new approach for integration of databases is devised for finding correspondence between ontologies. Applications: View and Search functionality was provided by filtering data from Meta data. This provides easy access to the integrated data.
Ontology-based integration of data sources
2007 10th International Conference on Information Fusion, 2007
Many applications, e.g., data/information fusion, data mining, and decision aids, need to access multiple heterogeneous data sources. These data sources may come from internal and external databases.
Semantic Integration in Big Data: State-of-the-Art
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Nowadays, web users and systems continually overload the web with an exponential generation of a massive amount of data. This leads to making big data more important in several domains such as social networks, internet of things, health care, E-commerce, aviation safety, etc. The use of big data has become increasingly crucial for companies due to the significant evolution of information providers and users on the web. However, big data remain meaningless without semantics. In order to get a good comprehension of big data, we raise questions about how big data and semantic are related to each other and how semantic may help. To overcome this problem, researchers devote considerable time to the integration of ontology in big data to ensure reliable interoperability between systems in order to make big data more useful, readable and exploitable. This technology can hide the heterogeneity of different data resources. Moreover, in given domains, users can exchange knowledge without cari...
SEMANTIC DATA INTEGRATION APPROACHES
airccse.org
Increased generation of data in the e-governance R&D process is required to generate the expected services in terms of enhanced e-services productivity and pipelines. The inability of existing integration strategies to organise and apply the available knowledge to the range of real scientific, business and governance issues is impacting on not only productivity but also transparency of information in crucial safety and regulatory applications. This requires focusing on normative models of e-governance that typically can assert horizontal (inter-agency) and vertical (inter-governmental) integration of data flows to represent the most sophisticated form of e-government delivering greatest payoff for both governments and users. The new range of semantic technologies based on ontology enable proper integration of knowledge in a way that is reusable by several applications across governance business from discovery to ministry affairs. The objective of this paper is to provide an insight on the necessary and sufficient knowledge base to deal with data integration using semantic web technologies applicable for e-governance based on exploratory research using literature survey. It assumes that reader has the capability of understanding some basic knowledge on E-governance, Relational Database Management, Ontology, and Service Oriented Architecture and Semantic Web Technology.
An Ontology Approach to Data Integration
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.
Journal on data …, 2008
Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources.
Ontology-Based Data Integration Methods: A Framework for Comparison
2005
A data integration system provides a uniform interface to distributed and heterogeneous sources. These sources can be databases as well as unstructured information such as files, HTML pages, etc. One of the most important problems within data integration is the semantic heterogeneity, which analyzes the meaning of terms included in the different information sources. This survey describes seven systems and