Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day-to-day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work.

中文翻译：

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个人知识图谱综合考察

能够概括一个人的日常生活及其不同方面的信息提供了有洞察力的知识。事实证明，这些知识比一般知识更有助于改进个性化任务。在存储此类知识时，个人知识图（PKG）成为方便的救星。PKG 是知识图谱，它存储与用户相关的详细信息，但通常对其他人没有用处。会话代理可以访问这些 PKG 来回答与用户日常生活相关的查询，而推荐系统可以利用 PKG 中存储的知识来提出个性化建议。尽管 PKG 具有巨大的适用性，但该领域尚未开展大量研究。我们对 PKG 进行了广泛的审查。我们根据它们最相关的领域对它们进行分类；我们特别强调 PKG 在医学、金融、教育和研究中的使用。我们还根据构建 PKG 所需的数据源对构建 PKG 的不同方法进行了分类。此外，我们讨论了 PKG 的局限性并提出了未来工作的方向。