Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.

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.

The ability to extract valuable information from documents and convert it into knowledge is crucial for driving technological innovation across industries. While adding metadata to manuals enhances their searchability, the real knowledge is still hidden in the procedural information they contain, which offers vital guidance for operators. Therefore, the approach of extracting and transforming unstructured human-readable information into machine-interpretable data is fundamental for establishing cutting-edge digital knowledge-based platforms. This paper presents a methodology tailored to the specific requirements of users who are seeking support in extracting and representing procedural knowledge from documents. We introduce a tool designed to support users in manually annotating procedures within PDF documents and generating a corresponding procedural knowledge graph. We assess the tool in real-world scenarios, aimed at evaluating its effectiveness in accomplishing various tasks. Finally, we generate a procedural knowledge graph that can facilitate knowledge discovery.

How to take multiple factors into account when evaluating a Game with a Purpose? How is player behaviour or participation influenced by different incentives? How does player engagement impact their accuracy in solving tasks? In this paper, we present a detailed investigation of multiple factors affecting the evaluation of a GWAP and we show how they impact on the achieved results. We inform our study with the experimental assessment of a GWAP designed to solve a multinomial classification task.