PROMPT-ASSISTED RELATION FUSION IN KNOWLEDGE GRAPH ACQUISITION

Knowledge Base (KB) systems have been studied for decades. Various approaches have been explored in acquiring accurate and scalable KBs. Recently, many studies focus on Knowledge Graphs (KG) which uses a simple triple representation. A triple consists of a head entity, a predicate, and a tail entity. The head entity and the tail entity are connected by the predicate which indicates a certain relation between them. Three main research fields can be identified in KG acquisition. First, relation extraction aims at extracting the triples from the raw data. Second, entity linking addresses mapping the same entity together. Last, knowledge fusion integrates heterogeneous sources into one. This dissertation focuses on relation fusion, which is a sub-process of knowledge fusion. More specifically, this dissertation aims to investigate if the concurrently popular prompt-based learning method can assist with relation fusion. A framework to acquire a KG is proposed to work with a real world dataset. The framework contains a Preprocessing module which annotates raw sentences and links known entities to the triples; a Prompting module, which generates and processes prompts for prediction with Pretrained Language Models (PLMs); and a Relation Fusion module, which creates predicate representations, clusters embeddings, and derives cluster labels. A series of experiments with comparison prompting groups are conducted. The results indicate that prompt-based learning, if applied appropriately, can help with grouping similar predicates. The framework proposed in this dissertation can be used eectively for assisting human experts with the creation of relation types during knowledge acquisition.