This paper provides an overview on graph databases for the retrieval and the integration of knowledge originating from textual data, attempting to bring together different bricks that are usually addressed separately. It explores concepts and insights that result from the scientific activities promoted by the GDR MADICS DOING Action (Intelligent Data: turning information into knowledge). The action promoted scientific discussion on the challenges, current findings, and open issues in converting textual data into information and, ultimately, knowledge. This topic has been investigated within a multidisciplinary context, involving specialists in Databases (DB), Natural Language Processing (NLP), Artificial Intelligence (AI), and professionals in various application domains.