SCOPE AND MOTIVATION

In an era where data (either unstructured, semi-structured or structured) is not just abundant but also the bedrock of innovation, knowledge graphs stand out as pivotal tools for structuring this data, making it interpretable and actionable by AI systems. They enable the representation of data in a form that mirrors human understanding, facilitating more natural interaction between humans and machines, and enhancing AI’s ability to generate insights that are both deep and relevant. The construction of knowledge graphs embodies the synthesis of various AI disciplines, including natural language processing, machine learning, and semantic web technologies. This workshop aims to foster a collaborative environment where experts and enthusiasts across these fields can converge to share insights, challenges, and breakthroughs. This convergence is crucial for pushing the boundaries of what AI can achieve, making it more adaptable, intuitive, and capable of handling the nuances of real-world information. By bridging the gap between theoretical research and practical applications, the objective is to accelerate the integration of Knowledge Graphs into diverse domains, from healthcare to cybersecurity, enhancing decision-making processes and creating more intelligent, autonomous systems.

This workshop is supported by the ENCRYPT project (https://encrypt-project.eu/) which aims to tackle privacy and security challenges in critical sectors such as healthcare, finance, and entertainment, driven by the expansion in digital data. In this context, Knowledge Graphs are used to introduce a semantic layer on top of the available data relevant to the use cases, with the aim of interlinking and contextually enriching the schemata and data in an interoperable manner.