Abstract: Knowledge base question answering aims to use pre-constructed knowledge bases to answer questions raised by users. Existing knowledge base question answering research mainly sorts candidate entities and relationship paths, and finally returns the tail entity of the triple as the answer. After the questions given by the user pass through the entity recognition model and the entity disambiguation model, they can be linked to candidate entities related to the answers in the knowledge base. Using the generation ability of the language model, the answer can be expanded into a sentence and returned, which is more user-friendly. In order to improve the generalization ability of the model and make up for the difference between question text and structured knowledge, candidate entities and their one-hop relationship subgraphs are organized and input into the generation model through prompt template, and a popular and fluent text is generated under the guidance of the answer template. Experimental results on the NLPCC 2016 CKBQA and KgCLUE Chinese datasets indicate that, on average, the proposed method outperformed the BART-large model by 2.8%, 2.3%, and 1.5% on the BLEU, METEOR, and ROUGE series metrics, respectively. On the Perplexity metric, the method performed comparably to ChatGPT's responses.

摘要： 知识库问答旨在利用事先构建好的知识库来回答用户提出的问题。现有的知识库问答研究主要通过对候选实体和关系路径进行排序，最后将三元组的尾实体作为答案返回。用户给出的问题经过实体识别模型和实体消歧模型之后，可以链接到知识库中与答案相关的候选实体。利用语言模型的生成能力，可以将答案拓展为一句话并返回，这对用户而言是更加友好的。为了提高模型的泛化能力和弥补问题文本与结构化知识之间的差别，将候选实体及其一跳关系子图通过提示模板进行组织输入到生成模型中并在回答模板的引导下生成通俗流畅的回答。在NLPCC 2016 CKBQA和KgCLUE两个中文数据集上的实验结果表明，该方法在BLEU、METEOR和ROUGE系列三类指标上分别平均比BART-large模型提高了2.8%、2.3%和1.5%。在Perplexity指标上，该方法与ChatGPT的回答表现相当。