Evidence/Discovery-Based Evolving Ontology (EDBEO)

This paper presents a proposal for the development of an ontology evolution strategy which refines ontological relations in scientific ontologies. In addition to experts’ consensus, it is desirable to define ontological relations between any two concepts in a scientific ontology based on scientific evidence. To address this issue, we can relate ontological relations to different research results obtained from various studies. To implement this solution, our envisaged evidence/discovery-based methodology integrates a higher-level ontology (systematic review ontology) into a systematic review agent which employs a Fuzzy Inference System in order to automatically modifyontological relations of a domain ontology based on the evidence received from information resources. The evidence/discovery-based methodology will further use the domain ontology to discover novel connections between distinct literatures, thereby, enrich its conceptualization

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Semantic-Based Collaborative Decisional System Integrating Fuzzy Reasoning in an IoT Context

Technologies such as IoT and Big data use advanced representation models and methods to lead to coherent systems and softwares. Nevertheless, there is a substantial lack of approaches able to support uncertain data and fuzzy environment to build a bridge between physical objects, semantic real-world view and the systemic view. In this work, we propose a semantic driven approach to support a decisional system based on semantic representation and an accurate fuzzy reasoning using the Choquet Integral method. The proposed generic architecture takes into consideration the formal representation of the real world, users’ needs and processes. Further, we focus on the relation between them and the dependence between different criteria. A detailed case study in the agriculture domain is also presented to showcase the interest of our proposal

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision