在 ICML 2022 被接收的论文中,《HousE: Knowledge Graph Embedding with Householder Parameterization》提出了一种具有更加强大且更加全面建模能力的KGE模型,命名为HousE。 知识图谱表示学习(Knowledge Graph Embedding,KGE) KGE模型旨在学习KG中实体和关系的表示,并定义一个评分函数来衡量三元组的合理性,用于预测缺失的链...
知识图谱嵌入(Knowledge Graph Embedding,KGE)可以帮助推荐系统。KGE在推荐系统中的应用主要是基于知识图谱的推荐任务,即利用知识图谱中的实体和关系信息来进行个性化推荐。 KGE模型可以学习实体和关系的向量表示,并将它们映射到低维向量空间中。通过这些向量表示,KGE模型可以计算用户和物品之间的关系向量,从而进行个性化推荐。
KEQA是一种基于Knowledge Embedding的问答框架, 能从繁杂的自然语言中直接抽取出谓词表示和实体表示, 缓解了自然语言的模糊性和歧义性问题. 并通过头实体检测模型过滤掉非常多的候选实体三元组, 缩小搜索范围. 同时, 作者充分结合了KGE能够保存关系信息的特性, 提出了联合距离度量....
Due to the incompleteness of knowledge graph, knowledge graph embedding(KGE) has become a key technique for automatically predict missing facts in knowledge graph. KGE aims to learn low dimensional representations for both relations and entities. Despite existing KGE models achieved state-of-the-art(...
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogonalization with restricted dimension and homogeneous geometry, ...
Therefore, effectively encoding the multimodal data into the learning of knowledge graph embedding provides new clue to improve KGE. Figure 1. An example of knowledge graph with associated multimodal content. DownLoad: Full-Size Img PowerPoint There are some works that attempt to improve the ...
知识图谱词嵌入(Knowledge Graph Embedding,KGE)模型:如图6的右侧部分,将知识图谱三元组中的前2个(电影ID和关系实体)作为输入,预测出第3个(目标实体)。 图6 MKR框架 在3个子模型中,最关键的是交叉压缩单元模型。下面就先从该模型开始一步一步地实现MKR框架。
论文笔记丨Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network,程序员大本营,技术文章内容聚合第一站。
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space Introduction This is the PyTorch implementation of the RotatE model for knowledge graph embedding (KGE). We provide a toolkit that gives state-of-the-art performance of several popular KGE models. The toolkit is quite efficien...
文章进一步提出了一个简单而有效的强基线:Knowledge Graph Embeddings Editor(KGEditor),它可以通过编辑额外的参数层来有效操作嵌入中的知识。实验中表明,KGEditor可以在修改不正确的知识或增加新的知识,并同时保持其他的知识稳定。本文的贡献总结如下: 提出了了一个新的任务:编辑基于语言模型的KGE,这个任务和提供的数据...