论文标题
知识图的物理嵌入模型
A Physical Embedding Model for Knowledge Graphs
论文作者
论文摘要
知识图嵌入方法学习知识图中实体的连续矢量表示，并已成功地用于大量应用中。我们提出了一种用于计算知识图嵌入的新颖且可扩展的范式，我们将其配置为Pyke。我们的方法将基于胡克定律的物理模型及其与模拟退火的想法相结合，到有效地计算知识图的嵌入。我们证明Pyke达到了线性空间的复杂性。虽然我们方法初始化的时间复杂性是二次的，但其每个迭代的时间复杂性在输入知识图的大小上是线性的。因此，Pyke的总运行时接近线性。因此，我们的方法很容易扩展到包含数百万三分之二的知识图。我们在两个系列实验中对药物库和DBPEDIA数据集的六种最新嵌入方法评估了我们的方法。第一个系列表明，皮克（Pyke）实现的群集纯度比最先进的纯度高26％（绝对）。此外，在最好的情况下，Pyke的速度比现有嵌入解决方案快22倍以上。我们的第二系列实验的结果表明，在维持其出色的可扩展性的同时，在类型预测的任务上，Pyke的最高比最高23％（绝对）（绝对）。我们的实施和结果是开源的，可在http://github.com/dice-group/pyke上找到。
Knowledge graph embedding methods learn continuous vector representations for entities in knowledge graphs and have been used successfully in a large number of applications. We present a novel and scalable paradigm for the computation of knowledge graph embeddings, which we dub PYKE . Our approach combines a physical model based on Hooke's law and its inverse with ideas from simulated annealing to compute embeddings for knowledge graphs efficiently. We prove that PYKE achieves a linear space complexity. While the time complexity for the initialization of our approach is quadratic, the time complexity of each of its iterations is linear in the size of the input knowledge graph. Hence, PYKE's overall runtime is close to linear. Consequently, our approach easily scales up to knowledge graphs containing millions of triples. We evaluate our approach against six state-of-the-art embedding approaches on the DrugBank and DBpedia datasets in two series of experiments. The first series shows that the cluster purity achieved by PYKE is up to 26% (absolute) better than that of the state of art. In addition, PYKE is more than 22 times faster than existing embedding solutions in the best case. The results of our second series of experiments show that PYKE is up to 23% (absolute) better than the state of art on the task of type prediction while maintaining its superior scalability. Our implementation and results are open-source and are available at http://github.com/dice-group/PYKE.
