Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs

This work delves into the critical intersection of provenance and data science, exploring the ways in which knowledge graphs can be enhanced to reflect contextual information. It emphasizes the necessity of sophisticated formalisms that transcend traditional RDF structures, allowing for richer representation and understanding of data. The authors, hailing from diverse backgrounds, bring a wealth of experience and insight as they guide the reader through the complexities of creating context-aware knowledge graphs.

Through a series of well-crafted examples and theoretical insights, the text illuminates the importance of provenance in the realm of data interpretation and usage. It serves as a valuable resource for researchers and practitioners alike, offering innovative approaches that empower readers to navigate the evolving landscape of data science with a nuanced perspective on knowledge representation. Each chapter builds upon foundational concepts, ultimately leading to a comprehensive understanding of how data can be leveraged more effectively in various applications.