HONto: A Bottom-Up Knowledge Base from Textbooks for Recommending Contextually Relevant Documents

This research presents a recommender system designed on the basis of a bottom-up knowledge base from textbooks. While other ontologies that are usually applied to such tasks are hand-crafted, our automated approach is a possible answer to the knowledge acquisition bottleneck. We extract concept hierarchies from section titles and use co-occurrences in book sections as evidence for possible contextual relationships between the therein mentioned entities. Motivated by a legal use case of recommending upcoming changes in law, the design is targeting three major challenges: different abstraction levels between entities of legal documents and the parliament protocols announcing norm changes, as well as engineering an explainable retrieval mechanism using the knowledge base which can additionally offer decent usability despite a high-recall requirement. Although the system is developed for a specific legal use case, there are many aspects of general applicability in the fields of recommender systems, information retrieval and information extraction, entity resolution, explainable artificial intelligence and usability. We validate selected parts of the system design also on other applications, such as educational media research.