This paper studies a new problem setting of entity alignment for knowledge
graphs (KGs). Since KGs possess different sets of entities, there could be
entities that cannot find alignment across them, leading to the problem of
dangling entities. As the first attempt to this problem, we construct a new
dataset and design a multi-task learning framework for both entity alignment
and dangling entity detection. The framework can opt to abstain from predicting
alignment for the detected dangling entities. We propose three techniques for
dangling entity detection that are based on the distribution of
nearest-neighbor distances, i.e., nearest neighbor classification, marginal
ranking and background ranking. After detecting and removing dangling entities,
an incorporated entity alignment model in our framework can provide more robust
alignment for remaining entities. Comprehensive experiments and analyses
demonstrate the effectiveness of our framework. We further discover that the
dangling entity detection module can, in turn, improve alignment learning and
the final performance. The contributed resource is publicly available to foster
further research.

本文研究了知识图谱实体对齐的新问题设置，提出了一种多任务学习框架，通过检测悬空实体并采取相应技术，可以提高实体对齐的效率和准确性。

了解无匹配：带有悬空情况的实体对齐

Knowing the No-match: Entity Alignment with Dangling Cases

We study the problem of learning local metrics for nearest neighbor
classification. Most previous works on local metric learning learn a number of
local unrelated metrics. While this "independence" approach delivers an
increased flexibility its downside is the considerable risk of overfitting. We
present a new parametric local metric learning method in which we learn a
smooth metric matrix function over the data manifold. Using an approximation
error bound of the metric matrix function we learn local metrics as linear
combinations of basis metrics defined on anchor points over different regions
of the instance space. We constrain the metric matrix function by imposing on
the linear combinations manifold regularization which makes the learned metric
matrix function vary smoothly along the geodesics of the data manifold. Our
metric learning method has excellent performance both in terms of predictive
power and scalability. We experimented with several large-scale classification
problems, tens of thousands of instances, and compared it with several state of
the art metric learning methods, both global and local, as well as to SVM with
automatic kernel selection, all of which it outperforms in a significant
manner.

本文介绍一种新的参数化定向 metric 学习方法，通过学习数据流形上的平滑 metric 矩阵函数，将本地度量作为区域不同点的锚定点上定义的基本度量的线性组合来学习，并应用 manifold 正则化来使学习到的 metric 矩阵函数沿着数据流形的测地线平滑变化，表现出极好的预测能力和可扩展性，并优于多种全局和局部的最先进的度量学习方法以及具有自动内核选择的支持向量机 (SVM)。