Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook
This work addresses knowledge management challenges for industrial researchers and enterprises, but it appears incremental as it builds on existing approaches with a new framework.
The paper surveys existing knowledge management systems and proposes a unified framework called bidirectional knowledge management system (BKMS) to address limitations in categorizing and organizing paper insights, enabling improved hierarchical note-taking and AI-assisted brainstorming.
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.