Knowledge Graphs (KGs) are a major asset for companies thanks to their great
flexibility in data representation and their numerous applications, e.g.,
vocabulary sharing, Q/A or recommendation systems. To build a KG it is a common
practice to rely on automatic methods for extracting knowledge from various
heterogeneous sources. But in a noisy and uncertain world, knowledge may not be
reliable and conflicts between data sources may occur. Integrating unreliable
data would directly impact the use of the KG, therefore such conflicts must be
resolved. This could be done manually by selecting the best data to integrate.
This first approach is highly accurate, but costly and time-consuming. That is
why recent efforts focus on automatic approaches, which represents a
challenging task since it requires handling the uncertainty of extracted
knowledge throughout its integration into the KG. We survey state-of-the-art
approaches in this direction and present constructions of both open and
enterprise KGs and how their quality is maintained. We then describe different
knowledge extraction methods, introducing additional uncertainty. We also
discuss downstream tasks after knowledge acquisition, including KG completion
using embedding models, knowledge alignment, and knowledge fusion in order to
address the problem of knowledge uncertainty in KG construction. We conclude
with a discussion on the remaining challenges and perspectives when
constructing a KG taking into account uncertainty.

建立知识图谱是个具有挑战性的任务，因为需要处理提取知识中的不确定性，并且需要解决数据的可靠性和冲突问题，同时还需要确保知识图谱的质量。

知识图谱构建中的不确定性管理：一项调查

Uncertainty Management in the Construction of Knowledge Graphs: a Survey

Knowledge Graphs (KGs) have made a qualitative leap and effected a real
revolution in knowledge representation. This is leveraged by the underlying
structure of the KG which underpins a better comprehension, reasoning and
interpretation of knowledge for both human and machine. Therefore, KGs continue
to be used as the main means of tackling a plethora of real-life problems in
various domains. However, there is no consensus in regard to a plausible and
inclusive definition of a domain-specific KG. Further, in conjunction with
several limitations and deficiencies, various domain-specific KG construction
approaches are far from perfect. This survey is the first to offer a
comprehensive definition of a domain-specific KG. Also, the paper presents a
thorough review of the state-of-the-art approaches drawn from academic works
relevant to seven domains of knowledge. An examination of current approaches
reveals a range of limitations and deficiencies. At the same time, uncharted
territories on the research map are highlighted to tackle extant issues in the
literature and point to directions for future research.

本文首次提出了领域特定知识图谱的综合定义，并针对七个领域的相关学术作品，提出了最先进的知识图谱构建方法进行了全面的回顾。审视当前方法时，揭示了一系列的局限和不足，同时还强调指出了文献中现存问题和未知的研究方向。