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.

Collaborative ontology matching based on compact interactive evolutionary algorithm

Abstract: Ontology is the kernel technology of semantic web, which plays a prominent role for achieving inter-operability across heterogeneous systems and applications by formally describing the semantics of data that characterize a particular application domain However, different ontology engineers might have potentially opposing world views which could yield the different descriptions on the same ontology entity, raising so called ontology heterogeneous problem Ontology matching, which aims at identifying the correspondences between the entities of heterogeneous ontologies, is recognized as an effective technology to solve the ontology heterogeneous problem Due to the complexity of ontology matching process, ontology alignments generated by the automatic ontology matchers should be validated by the users to ensure their qualities, and the technology that makes multiple users collaborate with each other to help the automatic tool create high quality matchings in a reasonable amount of time is called collaborative ontology matching Such a collaborative ontology matching poses a new challenge of how to reduce users’ workload, but at the same time, increase their involvement’s value To address this challenge, in this paper, we propose a Compact Interactive Memetic Algorithm (CIMA) based collaborative ontology matching technology, which can reduce users’ workload by adaptively determining the time of getting users involved, presenting the most problematic correspondences for users and helping users to automatic validate multiple conflict mappings, and increase user involvement’s value by propagating the collaborative validation and decreasing the negative effect brought by the error user validations The experimental results show that our proposal is able to efficiently exploit the collaborative validation to improve its non-interactive version, and the runtime and alignment quality of our approach both outperform state-of-the-art interactive ontology matching systems under different user error rate cases

Development of a smart home ontology and the implementation of a semantic sensor network simulator: An Internet of Things approach

Abstract: Internet of Things (IoT) is a network that consists of embedded objects communicating with each other, sense their environment and interact with it. The number of connected devices is increasing day by day and it is expected to reach 26 billion by 2020. There is a huge potential for the development of many applications in IoT. Up to now, the communication of agents in IoT is solved in different ways: non-IP solutions, IP-based solutions and recently by high level, middleware solutions. The diversity of sensors and the complexity of data heterogeneity are solved by use of ontologies in recent works. In this paper, we present a smart home sensor ontology we developed that is a specialized ontology based on the Semantic Sensor Networks (SSN) ontology. We also present a simulation environment we developed for a smart home case using our ontology and present early performance results.

Domain Ontology Agents in Distributed Organizational Memories

Abstract: Ontologies facilitate access to, and reuse of knowledge in Organizational Memories. In distributed OMs — as the next evolution step for practical applications of OMs — the assumption of globally shared conceptualizations does not seem tenable. In order to retain the benefits of domain ontologies we propose to explicitly control the sharing scope of ontological knowledge. To this end we introduce ontology societies which are primarily defined by the rights and obligations of their members. The practical use of this concept is shown in the FRODO architecture for Distributed Organizational Memories.

A method for determining ontology-based semantic relevance

Abstract: The semantic web is based on ontologies and metadata that indexes resources using ontologies. This indexing is called annotation. Ontology based information retrieval is an operation that matches the relevance of an annotation or a user generated query against an ontology-based knowledge-base. Typically systems utilising ontology-based knowledge-bases are semantic portals that provide search facilities over the annotations. Handling large answer sets require effective methods to rank the search results based on relevance to the query or annotation. A method for determining such relevance is a pre-requisite for effective ontology-based information retrieval. This paper presents a method for determining relevance between two annotations. The method considers essential features of domain ontologies and RDF(S) languages to support determining this relevance. As a novel use case, the method was used to implement a knowledgebased recommendation system. A user study showing promising results was conducted.

Ontology-based integration of business intelligence

Abstract: The integration of Business Intelligence BI has been taken bybusiness decision-makers as an effective means to enhance enterprise "soft power" and added value in the reconstruction and revolution oftraditional industries. The existing solutions based on structuralintegration are to pack together data warehouse DW, OLAP, data miningDM and reporting systems from different vendors. BI system users arefinally delivered a reporting system in which reports, data models,dimensions and measures are predefined by system designers. As aresult of a survey in the US, 85% of DW projects based on the above solutions failed to meet their intended objectives. In this paper, wesummarize our investigation on the integration of BI on the basis ofsemantic integration and structural interaction. Ontology-basedintegration of BI is discussed for semantic interoperability inintegrating DW, OLAP and DM. A hybrid ontological structure isintroduced which includes conceptual view, analytical view and physicalview. These views are matched with user interfaces, DW and enterpriseinformation systems, respectively. Relevant ontological engineeringtechniques are developed for ontology namespace, semantic relationships,and ontological transformation, mapping and query in this ontologicalspace. The approach is promising for business-oriented, adaptive andautomatic integration of BI in the real world. Operational decisionmaking experiments within a telecom company have demonstrated that a BI system utilizing the proposed approach is more flexible.