Developing link prediction models to automatically complete knowledge graphs
has recently been the focus of significant research interest. The current
methods for the link prediction taskhavetwonaturalproblems:1)the relation
distributions in KGs are usually unbalanced, and 2) there are many unseen
relations that occur in practical situations. These two problems limit the
training effectiveness and practical applications of the existing link
prediction models. We advocate a holistic understanding of KGs and we propose
in this work a unified Generalized Relation Learning framework GRL to address
the above two problems, which can be plugged into existing link prediction
models. GRL conducts a generalized relation learning, which is aware of
semantic correlations between relations that serve as a bridge to connect
semantically similar relations. After training with GRL, the closeness of
semantically similar relations in vector space and the discrimination of
dissimilar relations are improved. We perform comprehensive experiments on six
benchmarks to demonstrate the superior capability of GRL in the link prediction
task. In particular, GRL is found to enhance the existing link prediction
models making them insensitive to unbalanced relation distributions and capable
of learning unseen relations.

该研究提出了一种统一的关系学习框架（GRL），该框架可以插入现有的链接预测模型中，能够增强现有的链接预测模型，使其对于不平衡的关系分布和学习未见关系具有不敏感性，并能够联接语义相似的关系。

具有语义相关性感知的广义关系学习用于链接预测

Generalized Relation Learning with Semantic Correlation Awareness for  Link Prediction

In this paper we introduce the notion of Demand-Weighted Completeness,
allowing estimation of the completeness of a knowledge base with respect to how
it is used. Defining an entity by its classes, we employ usage data to predict
the distribution over relations for that entity. For example, instances of
person in a knowledge base may require a birth date, name and nationality to be
considered complete. These predicted relation distributions enable detection of
important gaps in the knowledge base, and define the required facts for unseen
entities. Such characterisation of the knowledge base can also quantify how
usage and completeness change over time. We demonstrate a method to measure
Demand-Weighted Completeness, and show that a simple neural network model
performs well at this prediction task.

本文提出了 “权重完备性” 的概念，该概念可以评估知识库对其实际使用的完备性。本研究利用实体的类别和使用数据来预测实体之间关系的概率分布，从而检测知识库中的重要空缺并确定未曾收录实体的相关信息。同时，本论文还展示了一个用于测量权重完备性的简单神经网络模型，取得了不错的结果。