Meta-PKE: Memory-Enhanced Task-Adaptive Personal Knowledge Extraction in Daily Life

Yijie Zhong, Feifan Wu, Mengying Guo, Xiaolian Zhang, Meng Wang, Haofen Wang

Abstract: In this paper, we propose the task of personal knowledge extraction to get structured knowledge from personal data in daily life. The existing information extraction methods struggle to handle this task due to the personal data’s multi-source, fine-grained, dynamic, and personalized nature. They fail to select necessary extraction tasks adaptively, cope with diverse scenarios in daily life, and overlook the assistance of historical personal data for the extraction task. Thus, we propose a novel Memory-Enhanced Task-Adaptive Personal Knowledge Extraction method called Meta-PKE. We introduce a task selection module to select the necessary extraction tasks without manual specification according to input personal data. When executing the selected extraction tasks, we record the historical data as the memory and design a memory-enhanced progressive extraction module. Structured personal knowledge is extracted in a coarse-to-fine manner aided by the optimal historical data from a carefully designed memory selection strategy. In addition, we propose a knowledge re-identification module to ensure the completeness of the extracted personal knowledge while avoiding the hallucinations engendered by the large language models. Extensive experiments reflect that, only utilizing the model with a small number of parameters (7B v.s. ><math><mo is="true">&gt;</mo></math>100B), Meta-PKE outperforms the state-of-the-art methods by near 15%, 20%, and 10% on 3 datasets, which cover not only daily but also non-daily scenarios more efficiently.