An Ontological Approach to Knowledge Building by Data Integration (original) (raw)

Using Ontologies for Semantic Data Integration

Studies in Big Data, 2017

While big data analytics is considered as one of the most important paths to competitive advantage of today's enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed.

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.

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.

Ontology-Based Information Integration: A Survey

2001

In the past a lot of approaches concerning the integration of heterogeneous information sources are developed. In the last years the semantics, which play an important role during the integration task, come into the focus leading to the so called ontology-based integration approaches. This paper provides a survey of most prominent ontologybased integration approaches. The approaches are evaluated according four criterions, i.e. the role and the representation of the ontologies, the mapping relating sources and ontologies, and their support for ontology engineering. The evaluation gives an impression, how which problems are solved, and shows the need for further research.

Integration of Heterogeneous Data Sources in an Ontological Knowledge Base

In this paper we present X2R, a system for integrating heterogeneous data sources in an ontological knowledge base. The main goal of the system is to create a unified view of information stored in relational, XML and LDAP data sources within an organization, expressed in RDF using a common ontology and valid according to a prescribed set of integrity constraints. X2R supports a wide range of source schemas and target ontologies by allowing the user to define potentially complex transformations of data between the original data source and the unified knowledge base. A rich set of integrity constraint primitives has been provided to ensure the quality of the unified data set. They are also leveraged in a novel approach towards semantic optimization of SPARQL queries.

The Role of Ontology in Semantic Integration

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 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. 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.

A Knowledge-Based Approach to Ontologies Data Integration

2004

Abstract This paper describes a proposal of multiple ontology data integration system for a question answering framework called AQUA. We propose an approach for mediating between a given query and a set of resources. This method is based on a Meta-ontology (which contains contents of each individual sources) and our similarity algorithm based on analysis of neighborhood of classes.

Information Integration Using Contextual Knowledge and Ontology Merging

2003

With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems.