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：用于知识图的相互交流的AI和问题解答系统(QAS)都是新出现的研究领域:它们赋予用户以自然语言界面的功能,以方便和有效地提取信息;相互交流的AI模拟与人类的对话;然而,它受培训数据集所采集的数据的限制;相比之下,质量AS通过理解和将自然语言问题转化为数据库引擎支持的正式查询,从一个知识图(KG)中检索最新信息。我们在本文件中全面研究了将两个世界合并成新型KG聊天机器人的现有替代方法的特点。我们的框架比较了两种有代表性的谈话模型,即ChatGPT和Galactica, 与目前最先进的QAS的KGQAN。我们利用不同应用领域的4个真正的KGs进行彻底评估,以确定每一类系统目前的局限性。根据我们的调查结果,我们建议开放研究机会,使QAS具备KGs的聊天博特能力。所有基准和所有原始结果都可供进一步分析。