Related papers: Knowledge Engineering Technique for Cluster Develo…

This paper discusses the important process of knowledge and its management, and differences between tacit and explicit knowledge and understanding the culture as a key issue for the successful implementation of knowledge management, in…

How can technical advice on operating experience feedback be managed efficiently in an organization that has never used knowledge engineering techniques and methods? This article explains how an industrial company in the nuclear and defense…

Production plants today are becoming more and more complicated through more automation and networking. It is becoming more difficult for humans to participate, due to higher speed and decreasing reaction time of these plants. Tendencies to…

Knowledge distillation (KD) transfers knowledge from large teacher models to compact student models, enabling efficient deployment on resource constrained devices. While diverse KD methods, including response based, feature based, and…

We propose a new approach -- called PK-clustering -- to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not…

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect…

In this paper we propose a novel approach aimed at building a new class of information system platforms which we call the "Knowledge-work Support Systems" or KwSS. KwSS can play a significant role in enhancing the IS support for knowledge…

Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their privacy concerns. Central to this paradigm is knowledge…

Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically,…

This article sets forth a review of knowledge distillation techniques with a focus on their applicability to retail banking contexts. Predictive machine learning algorithms used in banking environments, especially in risk and control…

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous…

The work presented in this paper focuses on the improvement of corporate knowledge management systems. For the implementation of such systems, companies deploy can important means for small gains. Indeed, management services often notice…

With the huge upsurge of information in day-to-days life, it has become difficult to assemble relevant information in nick of time. But people, always are in dearth of time, they need everything quick. Hence clustering was introduced to…

In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The…

To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test set. Many researchers and engineers are developing methods…

Designing Knowledge Management Systems (KMSs) for higher education requires addressing complex human-technology interactions, especially where staff turnover and changing roles create ongoing challenges for reusing knowledge. While advances…

Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI…

Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit…

Knowledge discovery is one of the main goals of Artificial Intelligence. This Knowledge is usually stored in databases spread in different environments, being a tedious (or impossible) task to access and extract data from them. To this…

KG (Knowledge Generation) and understanding have traditionally been a Human-centric activity. KE (Knowledge Engineering) and KM (Knowledge Management) have tried to augment human knowledge on two separate planes: the first deals with…