We present MMKG, a collection of three knowledge graphs that contain both
numerical features and (links to) images for all entities as well as entity
alignments between pairs of KGs. Therefore, multi-relational link prediction
and entity matching communities can benefit from this resource. We believe this
data set has the potential to facilitate the development of novel multi-modal
learning approaches for knowledge graphs.We validate the utility ofMMKG in the
sameAs link prediction task with an extensive set of experiments. These
experiments show that the task at hand benefits from learning of multiple
feature types.

MMKG 是一个包含数字特征和图像链接的三个知识图谱集合，可以帮助多重关系链接预测和实体匹配社区，并且具有潜力促进知识图谱的新型多模态学习方法的发展。我们通过大量实验验证了 MMKG 在同 As 链接预测任务中的效用，并显示出多元特征类型学习对这一任务的益处。

多模态知识图谱

MMKG: Multi-Modal Knowledge Graphs

We propose the Relational Tucker3 (RT) decomposition for multi-relational
link prediction in knowledge graphs. We show that many existing knowledge graph
embedding models are special cases of the RT decomposition with certain
predefined sparsity patterns in its components. In contrast to these prior
models, RT decouples the sizes of entity and relation embeddings, allows
parameter sharing across relations, and does not make use of a predefined
sparsity pattern. We use the RT decomposition as a tool to explore whether it
is possible and beneficial to automatically learn sparsity patterns, and
whether dense models can outperform sparse models (using the same number of
parameters). Our experiments indicate that---depending on the dataset--both
questions can be answered affirmatively.

本研究基于关系型 Tucker3 分解，探究了多关系链接预测中的知识图谱嵌入模型，使用 RT 分解作为工具来探讨自动学习稀疏模式是否可行并且是否可以超越使用相同数量参数的稀疏模型。

用于多关系链接预测的关系 Tucker 分解

A Relational Tucker Decomposition for Multi-Relational Link Prediction

We study bilinear embedding models for the task of multi-relational link
prediction and knowledge graph completion. Bilinear models belong to the most
basic models for this task, they are comparably efficient to train and use, and
they can provide good prediction performance. The main goal of this paper is to
explore the expressiveness of and the connections between various bilinear
models proposed in the literature. In particular, a substantial number of
models can be represented as bilinear models with certain additional
constraints enforced on the embeddings. We explore whether or not these
constraints lead to universal models, which can in principle represent every
set of relations, and whether or not there are subsumption relationships
between various models. We report results of an independent experimental study
that evaluates recent bilinear models in a common experimental setup. Finally,
we provide evidence that relation-level ensembles of multiple bilinear models
can achieve state-of-the art prediction performance.

本文研究了多关系链接预测和知识图谱完成的双线性嵌入模型，探讨了文献中各种双线性模型的表达能力和相互关系，证明了多个嵌入约束条件的双线性模型可以表示每个关系集合，并提出了多个嵌入关系级联模型达到最先进的预测性能的证据。