知识库嵌入模型的教学学解释
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
摘要：
知识库被用于从自然语言处理到语义网络搜索的各种应用中;唉，在实践中，它们的不完整性损害了它们的有用性。嵌入模型在知识库完成方面达到了最先进的精度，但它们的预测是出了名的难以解释。在本文中，我们适应“教学方法”（从神经网络的文献），以便解释嵌入模型提取加权霍恩规则。我们展示了如何教学方法必须适应采取大规模的关系方面的知识基础，并通过实验显示其优势和劣势。
英文摘要：
Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
