Personal knowledge graphs (PKGs) offer individuals a way to store and
consolidate their fragmented personal data in a central place, improving
service personalization while maintaining full user control. Despite their
potential, practical PKG implementations with user-friendly interfaces remain
scarce. This work addresses this gap by proposing a complete solution to
represent, manage, and interface with PKGs. Our approach includes (1) a
user-facing PKG Client, enabling end-users to administer their personal data
easily via natural language statements, and (2) a service-oriented PKG API. To
tackle the complexity of representing these statements within a PKG, we present
an RDF-based PKG vocabulary that supports this, along with properties for
access rights and provenance.

个人知识图谱（PKG）是一种为个人提供将其碎片化个人数据存储和 consololidate 到中心位置的方法，既提高了服务个性化，又保持了完全用户控制。本文通过提出一个完整的解决方案来解决实用的具有用户友好界面的 PKG 实现的缺失。我们的方法包括（1）面向用户的 PKG 客户端，使终端用户可以通过自然语言语句轻松管理其个人数据，并且（2）面向服务的 PKG API。为了解决在 PKG 中表示这些语句的复杂性，我们提出了一种基于 RDF 的 PKG 词汇表，并支持访问权限和来源的属性。

PKG API：个人知识图谱管理工具

PKG API: A Tool for Personal Knowledge Graph Management

Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.

实体消解是确定两个实体是否指向相同基础实体的问题。本章将讨论在个人知识图谱（PKG）环境下的命名实体消解问题，包括问题的形式化定义、进行高质量和高效实体消解所需的组件、面临的挑战、现有技术在 PKG 中的应用、一些应用案例以及未来研究的方向。

个人知识图谱中的命名实体解析

Named Entity Resolution in Personal Knowledge Graphs

This paper presents an ecosystem for personal knowledge graphs (PKG),
commonly defined as resources of structured information about entities related
to an individual, their attributes, and the relations between them. PKGs are a
key enabler of secure and sophisticated personal data management and
personalized services. However, there are challenges that need to be addressed
before PKGs can achieve widespread adoption. One of the fundamental challenges
is the very definition of what constitutes a PKG, as there are multiple
interpretations of the term. We propose our own definition of a PKG,
emphasizing the aspects of (1) data ownership by a single individual and (2)
the delivery of personalized services as the primary purpose. We further argue
that a holistic view of PKGs is needed to unlock their full potential, and
propose a unified framework for PKGs, where the PKG is a part of a larger
ecosystem with clear interfaces towards data services and data sources. A
comprehensive survey and synthesis of existing work is conducted, with a
mapping of the surveyed work into the proposed unified ecosystem. Finally, we
identify open challenges and research opportunities for the ecosystem as a
whole, as well as for the specific aspects of PKGs, which include population,
representation and management, and utilization.

本文提出了一个面向个人知识图谱 (PKG) 的生态系统，旨在解决 PKG 的普及化挑战，设计了一个统一的 PKG 框架，并综合了现有工作，分析其在该框架下的地位及挑战与机遇。