  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.
