Abstract

We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.

中文翻译：

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合理的嵌入：使用可转移神经推理器学习概念嵌入

摘要

我们提出了一种学习嵌入的新颖方法ALC知识库概念。嵌入反映了概念的语义，使得可以通过使用适当的神经构造函数从其各部分的嵌入来计算复杂概念的嵌入。不同知识库的嵌入是共享向量空间中的向量，其形状使得可以通过相同的神经网络（称为推理器头）对所有知识库完成任意复杂概念的近似包含检查。为了强调直接在嵌入上进行推理的独特属性，我们将其称为合理嵌入。我们报告的实验评估结果表明，为每个本体训练单独的推理器头和使用共享推理器头之间的推理性能差异可以忽略不计。