Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information easily and effectively. Conversational AI simulates conversations with humans; however, it is limited by the data captured in the training datasets. In contrast, QASs retrieve the most recent information from a KG by understanding and translating the natural language question into a formal query supported by the database engine. In this paper, we present a comprehensive study of the characteristics of the existing alternatives towards combining both worlds into novel KG chatbots. Our framework compares two representative conversational models, ChatGPT and Galactica, against KGQAN, the current state-of-the-art QAS. We conduct a thorough evaluation using four real KGs across various application domains to identify the current limitations of each category of systems. Based on our findings, we propose open research opportunities to empower QASs with chatbot capabilities for KGs. All benchmarks and all raw results are available1 for further analysis.

翻译：对话式AI和知识图谱的问答系统都是新兴的研究领域：它们为用户提供自然语言接口，以便轻松有效地提取信息。对话式AI模拟与人类的对话，但受限于训练数据集中包含的数据。相比之下，问答系统通过理解自然语言问题并将其转换为数据库引擎支持的形式化查询，从知识图谱中检索最新信息。本文全面研究了现有方案的特征，旨在将两者融合为新型知识图谱聊天机器人。我们的框架将两个代表性对话模型——ChatGPT和Galactica，与当前最先进的问答系统KGQAN进行了比较。我们使用四个跨不同应用领域的真实知识图谱进行了全面评估，以识别每类系统当前的局限性。基于研究结果，我们提出了公开的研究机会，旨在赋予问答系统以知识图谱的聊天机器人能力。所有基准测试和原始结果均可供进一步分析。