Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and industrial projects for, e.g., the design of spacecraft. A prominent method for dealing with relational data are knowledge graph embedding algorithms, where entities and relations of a knowledge graph are mapped to a low-dimensional vector space while preserving its semantic structure. Recently, a graph embedding method has been proposed that maps graph elements to the temporal domain of spiking neural networks. However, it relies on encoding graph elements through populations of neurons that only spike once. Here, we present a model that allows us to learn spike train-based embeddings of knowledge graphs, requiring only one neuron per graph element by fully utilizing the temporal domain of spike patterns. This coding scheme can be implemented with arbitrary spiking neuron models as long as gradients with respect to spike times can be calculated, which we demonstrate for the integrate-and-fire neuron model. In general, the presented results show how relational knowledge can be integrated into spike-based systems, opening up the possibility of merging event-based computing and relational data to build powerful and energy efficient artificial intelligence applications and reasoning systems.
翻译:最近,由于在模拟各种系统方面的灵活性,例如交互式粒子、材料和工业项目,例如航天器的设计,关系代表学习最近得到越来越多的兴趣,这些系统包括航天器的设计。处理关系数据的一个突出的方法是知识图形嵌入算法,其中将一个知识图形的实体和关系映射到低维矢量空间,同时保留其语义结构。最近,有人提议了一个图嵌入方法,将图形元素映射到神经神经网络的时空域。然而,它依赖通过神经组群编码图形元素的编码,而神经组群只猛增过一次。在这里,我们提出了一个模型,使我们能够学习以火车为基础的知识图形嵌入,只要求通过充分利用峰值模式的时空域,每个图形只要求一个神经元元素。这一编码方案可以用任意的螺旋型神经模型实施,只要能够计算到与峰值时间有关的梯度,我们就为集成和发泡神经模型演示。一般而言,介绍的结果显示,如何将神经组群集的图形元素群集成成成以钉状为基础的系统,从而有可能将事件高效的计算和人工关系数据应用与人工智能系统合并。