Towards ensuring Satisfiability of Merged Ontology

AbstractThe last decade has seen researchers developing efficient algorithms for the mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. Our methodology detects inconsistencies based on structural mismatches that occur due to conflicts among the set of Generalized Concept Inclusions, and Disjoint Relations due to the differences between disjoint partitions in the local heterogeneous ontologies. We present novel methodologies to detect and repair semantic inconsistencies from the list of initial mappings. This results in global merged ontology free from ‘circulatory error in class/property hierarchy’, „common class/instance between disjoint classes error’, ‘redundancy of subclass/subproperty relations’, ‘redundancy of disjoint relations’ and other types of „semantic inconsistency’ errors. In this way, our methodology saves time and cost of traversing local ontologies for the validation of mappings, improves performance by producing only consistent accurate mappings, and reduces the user dependability for ensuring the satisfiability and consistency of merged ontology. The experiments show that the newer approach with automatic inconsistency detection yields a significantly higher precision

Ontology for continuous learning and support

Acquiring new knowledge and skills is the principal goal of any educational institution. Universities planned always to improve the learning methods to offer high academic competencies qualities for students. These competencies help students to respond to the needs of companies' missions during their internships. In some cases, some companies' missions required more competencies in a special domain activity. Therefore, we proposed our method based on a continuous learning ontology where a student can do his job with the support and assistance of a specific service or from his supervisor. This ontology will permit to take into consideration, in addition to competencies, the abilities of students to ensure interoperability between company and university by ensuring matching between the actual student skills and required mission skills. Thus, the proposed approach will establish an efficiency matching between the company needs and the students. Results show the benefits of such an approach to resolving the gap between industry needs and students' skills. Springer Nature Switzerland AG 2019.Acknowledgments This publication was made possible by NPRP grant\# NPRP 7-1883-5-289 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Towards Classification of Web Ontologies for the Emerging Semantic Web

The massive growth in ontology development has opened new research challenges such as ontology management, search and retrieval for the entire semantic web community. These results in many recent developments, like OntoKhoj, Swoogle, OntoSearch2, that facilitate tasks user have to perform. These semantic web portals mainly treat ontologies as plain texts and use the traditional text classification algorithms for classifying ontologies in directories and assigning predefined labels rather than using the semantic knowledge hidden within the ontologies. These approaches suffer from many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define an ontology classification problem and categorize it into many sub-problems. We present a new ontological methodology for the classification of web ontologies, which has been guided by the requirements of the emerging Semantic Web applications and by the lessons learnt from previous systems. The proposed framework, OntClassifire, is tested on 34 ontologies with a certain degree of overlapping domain, and effectiveness of the ontological mechanism is verified. It benefits the construction, maintenance or expansion of ontology directories on the semantic web that help to focus on the crawling and improving the quality of search for the software agents and people. We conclude that the use of a context specific knowledge hidden in the structure of ontologies gives more accurate results for the ontology classification

Ontology Based System to Guide Internship Assignment Process

Internship assignment is a complicated process for universities since it is necessary to take into account a multiplicity of variables to establish a compromise between companies' requirements and student competencies acquired during the university training. These variables build up a complex relations map that requires the formulation of an exhaustive and rigorous conceptual scheme. In this research a domain ontological model is presented as support to the student's decision making for opportunities of University studies level of the University Lumiere Lyon 2 (ULL) education system. The ontology is designed and created using methodological approach offering the possibility of improving the progressive creation, capture and knowledge articulation. In this paper, we draw a balance taking the demands of the companies across the capabilities of the students. This will be done through the establishment of an ontological model of an educational learners' profile and the internship postings which are written in a free text and using uncontrolled vocabulary. Furthermore, we outline the process of semantic matching which improves the quality of query results.This publication was made possible by NPRP grand # NPRP 7-1883-5-289 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Toward a Collaborative Sensor Network Integration for SMEs’ Zero-Defect Manufacturing

International audienceThe increasing challenges in industry paved the way towards the next generation factory model or namely “Industry 4.0” through the availability and development of recent technologies in ICT such as industrial internet of things (IIoT) and cyber-physical production systems (CPPS). One of the main pillars of this paradigm is Zero defect manufacturing (ZDM), which aims to get workpieces “right the first time”. However, this technological uplift can prove itself to be very challenging in an industrial environment especially when it comes to the choice of available sensors, the motivation behind that choice, and the insurance that they comply with different guidelines for further exploitation in decision support. This is even more relevant when addressing low-volume high-variety industrial entities such as make-to-order (MTO) SMEs, inherently characterized by limited resources and highly variable business processes collaborating to respond to the demands of an increasingly cutting-edged market. This paper presents a collaborative approach to devise a suitable sensor network in an industrial machining environment generally and in an MTO SME context specifically, based on a joint analysis of all business process data related to quality control issues. Furthermore, the paper showcases the benefits of the approach in a real-world case study involving a 3-axis universal machining center as early validation

Diagnosis of Uveitis with Artificial Intelligence: A Predictive Approach

International audienc