  Knowledge management systems (KMS) are in high demand for industrial
researchers, chemical or research enterprises, or evidence-based decision
making. However, existing systems have limitations in categorizing and
organizing paper insights or relationships. Traditional databases are usually
disjoint with logging systems, which limit its utility in generating concise,
collated overviews. In this work, we briefly survey existing approaches of this
problem space and propose a unified framework that utilizes relational
databases to log hierarchical information to facilitate the research and
writing process, or generate useful knowledge from references or insights from
connected concepts. Our framework of bidirectional knowledge management system
(BKMS) enables novel functionalities encompassing improved hierarchical
note-taking, AI-assisted brainstorming, and multi-directional relationships.
Potential applications include managing inventories and changes for manufacture
or research enterprises, or generating analytic reports with evidence-based
decision making.
