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.

Rule-based ontological knowledge base for monitoring partners across supply networks

Abstract: This study demonstrates how ontology combined with semantic rules can be used for searching complete business partners in a supply network. Rather than being fully self-sufficient, individual enterprises are often part of a supply chain. Involving partners and understanding the importance of their activities is essential for enterprise agility. The question is how far-reaching and in what capacity a supply network is needed. Partner tracing becomes more difficult if search tasks involve potential partners or conforms to future production planning. This study utilized Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) technologies to develop supply network ontology and a problem-solving ontology, respectively. The three objectives of the study are creating a conceptual knowledge model for describing supply partners and their relationships, developing semantic rules as problem-solving methods for partner tracing in response to research questions and gathering experimental facts for evaluating knowledge-intensive designs. An example in the solar power industry is used to explain the ontological knowledge design and its uses. The experiments in this study showed that ontological approaches can effectively manage intricate and dynamic partnerships among supply networks. This design is also scalable in both domain ontology and task ontology for solving more complex problems.

Information system using healthcare ontology

Abstract: An information system using a healthcare ontology to provide a standardized representation for healthcare data is disclosed. One embodiment of the information system comprises a digital logic platform storing and using the healthcare ontology. The healthcare ontology describes concepts and relationships between the concepts derived from the corpus of domain specific knowledge and linking with standardized terminological systems.

Resolving inconsistencies in evolving ontologies

Abstract: Changing a consistent ontology may turn the ontology into an inconsistent state. It is the task of an approach supporting ontology evolution to ensure an ontology evolves from one consistent state into another consistent state. In this paper, we focus on checking consistency of OWL DL ontologies. While existing reasoners allow detecting inconsistencies, determining why the ontology is inconsistent and offering solutions for these inconsistencies is far from trivial. We therefore propose an algorithm to select the axioms from an ontology causing the inconsistency, as well as a set of rules that ontology engineers can use to resolve the detected inconsistency.

ontoX - a method for ontology-driven information extraction

Abstract: Information Extraction (IE) is an important research field within the Artificial Intelligence community, for it tries to extract relevant information out of vast amounts of data In this paper, we propose an extraction method that utilises the content and pre-defined semantics of ontologies formulated in the Web Ontology Language (OWL) to perform the extraction task We also propose our method to detect out-of-date constructs in the ontology to suggest changes to the user After stating the results of our evaluation, we conclude that the use of ontologies in conjunction with IESs can indeed yield feasible results and contribute to the better scalability and portability of the system

CORE: A tool for Collaborative Ontology Reuse and Evaluation

Abstract: Ontology evaluation can be defined as assessing the quality and the adequacy of an ontology for being used in a specific context, for a specific goal. In this work, a tool for Collaborative Ontology Reuse and Evaluation (CORE) is presented. The system receives an informal description of a semantic domain and determines which ontologies, from an ontology repository, are the most appropriate to describe the given domain. For this task, the environment is divided into three main modules. The first component receives the problem description represented as a set of terms and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository and determine which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques that combine the selected criteria. A third component of the system uses manual user evaluations of the ontologies in order to incorporate a human, collaborative assessment of the quality of ontologies.