Vision of Knowledge Graph Lifecycle Management within Hybrid Artificial Intelligence Solutions
conference paper
Knowledge Graphs (KGs) are essential components in AI systems, providing structured and interpretable data representations. However, managing the lifecycle of KGs poses significant challenges due to their dynamic nature, requiring continuous updates, validation, and maintenance. This vision paper addresses the critical need for innovative lifecycle management practices for hybrid AI solutions, KGs being part of them. Given advances in software engineering and software lifecycle, we need to learn from their past and investigate their practices to be applied to hybrid AI. This can be best done in collaboration with industry, such as small to middle-sized companies (SMEs). Our work aims to advance the scientific understanding of KG lifecycle management, offering practical tools and methodologies that benefit various industries, including healthcare, finance, and manufacturing. The implementation of such practices will enhance the overall quality and trustworthiness of AI systems, contributing to broader societal acceptance and integration of AI technologies in the future.
TNO Identifier
1003417
ISSN
16130073
Publisher
CEUR-WS
Source title
CEUR Workshop Proceedings