  Knowledge graphs (KGs) are crucial in the field of artificial intelligence
and are widely applied in downstream tasks, such as enhancing Question
Answering (QA) systems. The construction of KGs typically requires significant
effort from domain experts. Recently, Large Language Models (LLMs) have been
used for knowledge graph construction (KGC), however, most existing approaches
focus on a local perspective, extracting knowledge triplets from individual
sentences or documents. In this work, we introduce Graphusion, a zero-shot KGC
framework from free text. The core fusion module provides a global view of
triplets, incorporating entity merging, conflict resolution, and novel triplet
discovery. We showcase how Graphusion could be applied to the natural language
processing (NLP) domain and validate it in the educational scenario.
Specifically, we introduce TutorQA, a new expert-verified benchmark for graph
reasoning and QA, comprising six tasks and a total of 1,200 QA pairs. Our
evaluation demonstrates that Graphusion surpasses supervised baselines by up to
10% in accuracy on link prediction. Additionally, it achieves average scores of
2.92 and 2.37 out of 3 in human evaluations for concept entity extraction and
relation recognition, respectively.
