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首页> 外文期刊>Journal of Intelligent Information Systems >AWML: adaptive weighted margin learning for knowledge graph embedding
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AWML: adaptive weighted margin learning for knowledge graph embedding

机译：AWML：知识图形嵌入的自适应加权保证金学习

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摘要

Knowledge representation learning (KRL), exploited by various applications such as question answering and information retrieval, aims to embed the entities and relations contained by the knowledge graph into points of a vector space such that the semantic and structure information of the graph is well preserved in the representing space. However, the previous works mainly learned the embedding representations by treating each entity and relation equally which tends to ignore the inherent imbalance and heterogeneous properties existing in knowledge graph. By visualizing the representation results obtained from classic algorithm TransE in detail, we reveal the disadvantages caused by this homogeneous learning strategy and gain insight of designing policy for the homogeneous representation learning. In this paper, we propose a novel margin-based pairwise representation learning framework to be incorporated into many KRL approaches, with the method of introducing adaptivity according to the degree of knowledge heterogeneity. More specially, an adaptive margin appropriate to separate the real samples from fake samples in the embedding space is first proposed based on the sample's distribution density, and then an adaptive weight is suggested to explicitly address the trade-off between the different contributions coming from the real and fake samples respectively. The experiments show that our Adaptive Weighted Margin Learning (AWML) framework can help the previous work achieve a better performance on real-world Knowledge Graphs Freebase and WordNet in the tasks of both link prediction and triplet classification.
机译：知识表示学习（KRL）由诸如问题回答和信息检索的各种应用程序开发，旨在将知识图所含的实体和关系嵌入到矢量空间的点，使得图形的语义和结构信息得到保存良好在代表的空间。然而，以前的作品主要通过治疗每个实体和同样的关系来学习嵌入表示，这倾向于忽略知识图中存在的固有不平衡和异构性质。通过详细可视化从经典算法宁静获得的表示结果，我们揭示了这种同质学习策略引起的缺点，并增强了对同一性代表学习的设计政策的洞察力。在本文中，我们提出了一种新的基于边缘的成对的成对表示学习框架，该框架被纳入许多KRL方法，其根据知识异质程度引入适应性的方法。更特别地，首先基于样本的分布密度提出适当的自适应边缘，以便将实际样本从嵌入空间中的假样品分离，然后建议自适应重量明确地解决来自的不同贡献之间的权衡实际和假样本。实验表明，我们的自适应加权保证金学习（AWML）框架可以帮助以前的工作在Real-World知识图中实现更好的性能，以便在链路预测和三联分类的任务中获得更好的性能。

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