Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

A comparative study for domain ontology guided feature extraction

Abstract: We introduced a novel method employing a hierarchical domain ontology structure to extract features representing documents in our previous publication (Wang 2002). All raw words in the training documents are mapped to concepts in a concept hierarchy derived from the domain ontology. Based on these concepts, a concept hierarchy is established for the training document space, using is-a relationships defined in the domain ontology. An optimum concept set may be obtained by searching the concept hierarchy with an appropriate heuristic function. This may be used as the feature space to represent the training dataset. The proposed method aims to solve some drawbacks suffered by text classification algorithms and feature selection algorithms. In this paper, we conducted a series of experiments to compare our approach with other comparable feature-selection and feature-extraction methods. The results indicated that our approach has advantages in many aspects.

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

Abstract: In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.

Semantic technologies: latest advances in agent-based manufacturing control systems

Abstract: The manuscript reports on the latest advancements in the field of multi-agent based industrial control systems. It is declared that this area is becoming strongly influenced by the recent massive proliferation of semantic technologies in various fields of software engineering. The application of ontologies for expressing semantics of data does not restrict any longer exclusively on semantic web or semantic web services. Ontologies are being applied for advanced handling, exchanging and reasoning about knowledge in the area of industrial automation, especially in the case of production systems based on multi-agent technology. The paper provides a comprehensive survey of the application of ontologies in agent-based control systems and discusses the common characteristics of such integration. The generic ontology designed for discrete manufacturing, which covers ordering, production planning and material handling aspects is presented. In addition, the framework for integration of this ontology in distributed...

A Data Quality Ontology for the Secondary Use of EHR Data.

Abstract: The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the important terms used to describe data quality concepts and harmonized into an ontology. Four high-level data quality dimensions ("correctness", "consistency", "completeness" and "currency") categorize 19 lower level measures. The ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases. A detailed example is presented to demonstrate its utility. The DQ ontology can make data validation more common and reproducible.

Supporting business intelligence by providing ontology-based end-user information self-service

Abstract: Business users need to analyse changing sets of information to effectively support their working tasks. Due to the complexity of enterprise systems and available tools, especially technically unskilled users face considerable challenges when trying to flexibly retrieve needed data in an ad-hoc manner. As a consequence, available data is limited to information artefacts like queries or reports which have been predefined for them by IT experts. To improve information self-service capabilities of business users, we present an ontology-based architecture and end-user tool, enabling easy data access and query creation for business users. Our approach is based on a semantic middleware integrating data from heterogeneous information systems and providing a comprehensible data model in the form of a business level ontology (BO). We show how our end-user tool Semantic Query Designer (SQD) enables convenient navigation and query building upon the BO, and illustrate its usage and the processing of data over all layers of our system architecture in detail, using a comprehensible use case example. As flexible query creation is a crucial precondition of leveraging the usage of enterprise data, we contribute to the enablement of business users of making better informed decisions, thus increasing effectiveness and efficiency of business processes.