Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional and continuous vectors. This enables KG machine learning models to be easily adapted for KG reasoning, completion, and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to a method that learns compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring the Hamming distance instead of Euclidean distance. Alternatively, a dot-product is used in a continuous vector space. Unfortunately, most learning-to-hash methods cannot be directly applied to KG structure encoding because they focus on similarity preservation between images. In this paper, we introduce a novel end-to-end learning-to-hash framework for encoding incomplete KGs and graph queries in a Hamming space. To preserve KG structure information, from embeddings to hash codes, and address the ill-posed gradient issue in the optimization, we utilize a continuation method (with convergence guarantees) to jointly encode queries and KG entities using geometric operations. The hashed embedding of a query can be utilized to discover target entities from incomplete KGs whilst the efficiency has been greatly improved. To evaluate the proposed framework, we have compared our model to state-of-the-art methods commonly used in real-world KGs. Extensive experimental results show that our framework not only significantly speeds up the search process, but also provides good results when unanswerable queries are caused by incomplete information.¹

知识图（KG）嵌入技术将实体和关系表示为低维和连续向量。这使KG机器学习模型可以轻松地适应KG推理，完成和查询任务。但是，学习到的密集向量对于大规模相似度计算效率不高。哈希学习是一种从高维输入数据中学习紧凑二进制代码的方法，它通过测量汉明距离而不是欧氏距离，提供了一种有希望的方式来提高效率。或者，在连续向量空间中使用点积。不幸的是，大多数哈希学习方法无法直接应用于KG结构编码，因为它们专注于图像之间的相似性保留。在本文中，我们介绍了一种新颖的端到端“学习哈希”框架，用于在汉明空间中对不完整的KG和图查询进行编码。为了保留从嵌入到哈希码的KG结构信息，并解决优化过程中不适定的梯度问题，我们使用一种延续方法（具有收敛性保证）来使用几何运算对查询和KG实体进行联合编码。查询的散列嵌入可用于从不完整的KG中发现目标实体，同时效率得到了极大的提高。为了评估提议的框架，我们将模型与现实世界中的KG中常用的最新方法进行了比较。大量的实验结果表明，我们的框架不仅可以大大加快搜索过程，^1个