Procedural knowledge management in Industry 5.0: Challenges and opportunities for knowledge graphs

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics Pub Date : 2025-01-01 DOI:10.1016/j.websem.2024.100850

Irene Celino, Valentina Anita Carriero, Antonia Azzini, Ilaria Baroni, Mario Scrocca

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引用次数: 0

Abstract

With digital transformation, industrial companies today are facing the challenges to change and innovate their business, by leveraging digital technologies and tools to support their processes and their operations. One of their main challenges is the management of the company knowledge, especially when tacit and owned by industry workers. In this paper, we illustrate how knowledge graphs can be the turning point to allow industry workers digitize and exploit the knowledge about the “what”, the “how” and the “why” of their everyday activities.

In particular, we focus on the “how” by illustrating the challenges related to procedural knowledge management, i.e., the knowledge about processes and workflows that employees need to follow, and comply with, to correctly execute their tasks, in order to improve efficiency and effectiveness, to reduce risks and human errors and to optimize operations. We also explain the relationship in this context between knowledge graphs and sub-symbolic AI approaches.

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工业5.0中的程序性知识管理：知识图谱的挑战与机遇

随着数字化转型，今天的工业公司正面临着改变和创新业务的挑战，通过利用数字技术和工具来支持他们的流程和运营。他们面临的主要挑战之一是对公司知识的管理，特别是当隐性知识由行业工人拥有时。在本文中，我们说明了知识图谱如何成为一个转折点，使行业工作者能够数字化并利用关于他们日常活动的“什么”、“如何”和“为什么”的知识。特别是，我们通过说明与程序性知识管理相关的挑战来关注“如何”，即员工需要遵循和遵守的流程和工作流程的知识，以正确执行任务，以提高效率和有效性，减少风险和人为错误，并优化操作。在这种情况下，我们还解释了知识图和亚符号人工智能方法之间的关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Web Semantics 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.00%

发文量

审稿时长

14.6 weeks

期刊介绍： The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.