PROM: Personal Knowledge Graph Construction with Large Language Models

Aishan Maoliniyazi, Chaohong Ma, Xiaofeng Meng, Bingbing Xu

Abstract: The growing volume of digital information requires effective Personal Knowledge Management. Personal Knowledge Graphs (PKGs), which model knowledge as connected entities and relationships, show potential. Chats or natural voice conversations contain abundant context information about users’ thoughts and preferences, which is beneficial for constructing PKGs. However, constructing PKGs from unstructured natural conversations is still challenging. The main obstacle comes from two aspects: inherently complex and context-dependent conversations. In this paper, we present PROM, a novel framework of personal knowledge graph construction with LLMs. PROM effectively constructs PKGs from natural conversations. Particularly, PROM constructs PKGs with rich knowledge information, preserves context information for knowledge provenance, and fuses different kinds of contexts for structural and semantic coherence. Specifically, PROM constructs knowledge triples (subject, predicate, object) from conv

External IDs:dblp:conf/ic3k/MaoliniyaziM0025