Using Ontologies for Semantic Data Integration

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

The Form of Organization for Small Business

Matching and integrating ontologies has been a desirable technique in areas such as data fusion, knowledge integration, the Semantic Web and the development of advanced services in distributed system. Unfortunately, the heterogeneities of ontologies cause big obstacles in the development of this technique. This licentiate thesis describes an approach to tackle the problem of ontology integration using description logics and production rules, both on a syntactic level and on a semantic level. Concepts in ontologies are matched and integrated to generate ontology intersections. Context is extracted and rules for handling heterogeneous ontology reasoning with contexts are developed. Ontologies are integrated by two processes. The first integration is to generate an ontology intersection from two OWL ontologies. The result is an ontology intersection, which is an independent ontology containing non-contradictory assertions based on the original ontologies. The second integration is carried out by rules that extract context, such as ontology content and ontology description data, e.g. time and ontology creator. The integration is designed for conceptual ontology integration. The information of instances isn't considered, neither in the integrating process nor in the integrating results. An ontology reasoner is used in the integration process for non-violation check of two OWL ontologies and a rule engine for handling conflicts according to production rules. The ontology reasoner checks the satisfiability of concepts with the help of anchors, i.e. synonyms and string-identical entities; production rules are applied to integrate ontologies, with the constraint that the original ontologies should not be violated. The second integration process is carried out with production rules with context data of the ontologies. Ontology reasoning, in a repository, is conducted within the boundary of each ontology. Nonetheless, with context rules, reasoning is carried out across ontologies. The contents of an ontology provide context for its defined entities and are extracted to provide context with the help of an ontology reasoner. Metadata of ontologies are criteria that are useful for describing ontologies. Rules using context, also called context rules, are developed and in-built in the repository. New rules can also be added. The scientific contribution of the thesis is the suggested approach applying semantic based techniques to provide a complementary method for ontology matching and integrating semantically. With the illustration of the ontology integration process and the context rules and a few manually integrated ontology results, the approach shows the potential to help to develop advanced knowledge-based services.QC 20130201</p

A Data-Intensive Lightweight Semantic Wrapper Approach to Aid Information Integration

We argue for the flexible use of lightweight ontologies to aid information integration. Our proposed approach is grounded on the availability and exploitation of existing data sources in a networked environment such as the world wide web (instance data as it is commonly known in the description logic and ontology community). We have devised a mechanism using Semantic Web technologies that wraps each existing data source with semantic information, and we refer to this technique as SWEDER (Semantic Wrapping of Existing Data Sources with Embedded Rules). This technique provides representational homogeneity and a firm basis for information integration amongst these semantically enabled data sources. This technique also directly supports information integration though the use of context ontologies to align two or more semantically wrapped data sources and capture the rules that define these integrations. We have tested this proposed approach using a simple implementation in the domain of organisational and communication data and we speculate on the future directions for this lightweight approach to semantic enablement and contextual alignment of existing network-available data sources

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.publishedVersio

Applications and Uses of Dental Ontologies

The development of a number of large-scale semantically-rich ontologies for biomedicine attests to the interest of life science researchers and clinicians in Semantic Web technologies. To date, however, the dental profession has lagged behind other areas of biomedicine in developing a commonly accepted, standardized ontology to support the representation of dental knowledge and information. This paper attempts to identify some of the potential uses of dental ontologies as part of an effort to motivate the development of ontologies for the dental domain. The identified uses of dental ontologies include support for advanced data analysis and knowledge discovery capabilities, the implementation of novel education and training technologies, the development of information exchange and interoperability solutions, the better integration of scientific and clinical evidence into clinical decision-making, and the development of better clinical decision support systems. Some of the social issues raised by these uses include the ethics of using patient data without consent, the role played by ontologies in enforcing compliance with regulatory criteria and legislative constraints, and the extent to which the advent of the Semantic Web introduces new training requirements for dental students. Some of the technological issues relate to the need to extract information from a variety of resources (for example, natural language texts), the need to automatically annotate information resources with ontology elements, and the need to establish mappings between a variety of existing dental terminologies

An ontology-based approach for semantic level information exchange and integration in applications for product lifecycle management

During product lifecycle management (PLM), product information fromCAD/CAE applications regularly needs to be exchanged and shared between the variousapplications. However, these applications often have different product data semantics andcorresponding representations. The interoperability problem caused by the heterogeneoussemantics and data representation is critical and needs to be addressed and automated.Recent research has focused on integration frameworks for CAD/CAE applications inorder to improve interoperability. There are fundamental problems that still need to beaddressed.We identified the following important roadblocks and sought to address thesespecifically in our work: 1) The need for an adequate product knowledge representationof engineering design/analysis, which is easily expandable, and customizable fortraditional and non-traditional (e.g. virtual prototyping) design information systems thatalso allows the sharing of product data semantics across all these heterogeneous systemsto support distributed, collaborative engineering capabilities; 2) The need for a way togenerate product data semantics by using engineering design/analysis knowledge tointerpret actual product data 3) The need for a way to reconcile the differences in thedifferent product semantics by finding underlying similarities between differentknowledge representations that are from different viewports and reconcile, and use thesesimilarities to then translate product data semantics correctly.This dissertation proposes an ontology-based approach for a semantic levelexchange and integration to improve interoperability, which includes an ontologybuilding tool, ontology mapping tools and custom tools to associate ontologies to prductdata. For the purpose of semantic level integration, a way of representing engineeringdesign/analysis knowledge using an engineering ontology is proposed. A layeredstructure is used for building knowledge into engineering ontologies so as to improve thescalability and composition adaptivity. Based on the knowledge, a semantic layer is builtupon product data to use concepts/relations in ontologies to describe actual product data,which can be used to represent understandings about a product design from differentperspectives. To enable translating different understandings (product data semantics)using different ontologies, an ontology mapping method is proposed to find matchingconcepts between different ontologies, based on three basic relation types betweenconcepts: composition, inheritance and attribute.A scenario is explained to describe the working mechanism of the system and todemonstrate the concept of semantic level integration framework for a simple example. Asample assembly is designed and simulated in different software packages and anintegrated process is made to exchange information between them. The scenariosuccessfully demonstrates the ontology based approach

The MOUSE approach: Mapping Ontologies using UML for System Engineers

To address the problem of semantic heterogeneity, there has been a large body of research directed toward the study of semantic mapping technologies. Although various semantic mapping technologies have been investigated, &nbsp;facilitating the process for domain experts to perform a semantic data integration task is still not easy. This is because one is required not only to possess domain expertise but also to have a good understanding of knowledge engineering. This paper proposes an approach that automatically transforms an abstract semantic mapping syntax into a concrete executable mapping syntax, we call this approach MOUSE (Mapping Ontologies using UML for System Engineers). In order to evaluate MOUSE, an implementation of this approach for a semantic data integration use case has been developed (called SDI, Semantic Data Integration). The aim is to enable domain experts, particularly system engineers, to undertake mappings using a technology that they are familiar with (UML), while ensuring the created mappings are accurate and the approach is easy to use. The proposed UML-based abstract mapping syntax is evaluated through usability experiments conducted in a lab environment by participants who have skills equivalent to real life system engineers using the SDI tool. Results from the evaluations show that the participants could correctly undertake the semantic data integration task using the MOUSE approach while maintaining accuracy and usability (in terms of ease of use)

A community based approach for managing ontology alignments

The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefitfrom human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe a prototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies