Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community

Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper

Implementing sharing platform based on ontology using a sequential recommender system

While recommender systems have shown success in many fields, accurate recommendations in industrial settings remain challenging. In maintenance, existing techniques often struggle with the “cold start” problem and fail to consider differences in the target population's characteristics. To address this, additional user information can be incorporated into the recommendation process. This paper proposes a recommender system for recommending repair actions to technicians based on an ontology (knowledge base) and a sequential model. The approach utilizes two ontologies, one representing failure knowledge and the other representing asset attributes. The proposed method involves two steps: i) calculating score similarity based on ontology domain knowledge to make predictions for targeted failures and ii) generating Top-N repair actions through collaborative filtering recommendations for targeted failures. An additional module was implemented to evaluate the recommender system, and results showed improved performance

An approach of ontology and knowledge base for railway maintenance

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi

Knowledge-Enabled Building Information Modelling: A Framework for Improved Decision-Making

Purpose: The purpose of this study is to build an integrated framework that provides a methodical approach to managing the knowledge produced by Building Information Modelling (BIM) procedures.   Theoretical framework:  BIM has revolutionized the approach to building design and construction projects; nevertheless, managing the vast amounts of heterogeneous data produced by the various BIM processes is a very difficult task. The need of knowledge management (KM) for successful BIM implementation is becoming widely acknowledged as it will allow organisational stakeholders to make strategic decisions, reduce errors, and improve results.   Methodology: Both primary and secondary data were collected in this research. This data was utilized to develop an initial version of the KM-based BIM framework, which underwent a thorough two-phase expert evaluation process via interviews with industry experts to enhance the framework's applicability and relevance.   Findings:  The significant factors influencing KM in the context of BIM were identified. Moreover, through the expert evaluation process, it was determined that the proposed KM-based BIM framework provided valuable assistance in addressing these factors and in managing BIM organisational knowledge.   Research implications:  The research has implications for the field of building design and construction projects. By promoting the adoption of a KM-based BIM framework, it seeks to address the challenges associated with managing heterogeneous data and information silos. The framework has the potential to improve the decision making process in the context of BIM.   Originality/value:  This is the first research providing a methodical approach to managing BIM-related knowledge through KM, and that provides an expert-validated framework for improved decision making