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.

The fast-growing complexity of decentralized infrastructures outpaces end-users from acquiring the required knowledge to operate them. A possible remedy for this ever-aggravating problem might be the integration of natural-language-driven interfaces that allow end-users to interact with those systems. This work focuses on a Distributed Analytics (DA) use case, which includes the challenge above. We propose a DA-tailored and module-based monitoring and transaction bot called HAICO, which assists users in keeping track of their activities. Our user study (n=50) shows that our approach indeed contributes to increased transparency and accessibility of digital assets in these ecosystems. The results show that integrating automated agents can lower the barriers for non-technology-versed users to foster efficiency and adoption time.