OwnThink stands as the most extensive Chinese open-domain knowledge graph
introduced in recent times. Despite prior attempts in question answering over
OwnThink (OQA), existing studies have faced limitations in model representation
capabilities, posing challenges in further enhancing overall accuracy in
question answering. In this paper, we introduce UniOQA, a unified framework
that integrates two complementary parallel workflows. Unlike conventional
approaches, UniOQA harnesses large language models (LLMs) for precise question
answering and incorporates a direct-answer-prediction process as a
cost-effective complement. Initially, to bolster representation capacity, we
fine-tune an LLM to translate questions into the Cypher query language (CQL),
tackling issues associated with restricted semantic understanding and
hallucinations. Subsequently, we introduce the Entity and Relation Replacement
algorithm to ensure the executability of the generated CQL. Concurrently, to
augment overall accuracy in question answering, we further adapt the
Retrieval-Augmented Generation (RAG) process to the knowledge graph.
Ultimately, we optimize answer accuracy through a dynamic decision algorithm.
Experimental findings illustrate that UniOQA notably advances SpCQL Logical
Accuracy to 21.2% and Execution Accuracy to 54.9%, achieving the new
state-of-the-art results on this benchmark. Through ablation experiments, we
delve into the superior representation capacity of UniOQA and quantify its
performance breakthrough.

UniOQA 是一个统一框架，通过整合两种互补的并行工作流程，利用大语言模型精确地回答问题，并采用直接回答预测过程作为一种成本效益的补充，通过改进问题表示能力和优化知识图谱，显著提升了 SpCQL 逻辑准确性达到 21.2% 和执行准确性达到 54.9%。