This paper presents significant enhancements to the frances platform, incorporating the Heritage Textual Ontology (HTO), advanced knowledge graphs, and sophisticated search capabilities, along with innovative data visualization methods. The HTO integrates diverse historical collections and unifies various sources. Leveraging this ontology, the new knowledge graphs connect data across different sources and editions, linking to external resources like Wikipedia and Dbpedia to enrich semantic relationships. We employed deep-learning-based spell correction for OCR error correction. Enhanced search functionalities, powered by Elasticsearch and semantic technologies, enable precise retrieval and analysis. Additionally, new data visualization approaches offer multifaceted interpretations of search results. A case study tracking slavery references in historical editions of the Encyclopaedia Britannica 1768-1860 demonstrates the platform’s effectiveness in analyzing historical text and validating frances’s capabilities.

Frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques. It has been specifically designed to unlock the full potential of historical digital textual collections, such as those from the National Library of Scotland, offering cloud-based capabilities and extended support for complex NLP analyses and data visualizations. frances enables realtime recurrent operational text mining and provides robust capabilities for temporal analysis, accompanied by automatic visualizations for easy result inspection. In this paper, we present the motivation behind the development of frances, emphasizing its innovative design and novel implementation aspects. We also outline future development directions, and we evaluate the platform through two comprehensive case studies in history and publishing history.