Publication
Plan Generation from Unstructured Documents Through Transformer-Based Extraction of Knowledge Graphs
Wolfgang Maaß; Cicy Agnes; Amin Harig
In: Thirty-Second European Conference on Information Systems (ECIS 2024). European Conference on Information Systems (ECIS-2024), June 13-19, Paphos, Cyprus, Association for Information Systems, 4/2024.
Abstract
Planning for complex tasks is a key task for knowledge workers that is often time-consuming and depends on the manual extraction of knowledge from documents. In this research, we propose an end-to-end method, called PlanKG, that: (1) extracts knowledge graphs from full-text plan descriptions (FTPD); and (2) generates novel FTPD according to plan requirements and context information provided by users. From the knowledge graphs, activity sequences are obtained and projected into embedding spaces. We show that compressed activity sequences are sufficient for the search and generation of plan descriptions. The PlanKG method uses a pipeline consisting of decoder-only transformer models and encoder-only transformer models. To evaluate the PlanKG method, we conducted an experimental study for movie plot descriptions and compared our method with original FTPDs and FTPD summarizations. The results of this research has significant potential for enhancing efficiency and precision when searching and generating plans