Numeric values associated to edges of a knowledge graph have been used to
represent uncertainty, edge importance, and even out-of-band knowledge in a
growing number of scenarios, ranging from genetic data to social networks.
Nevertheless, traditional knowledge graph embedding models are not designed to
capture such information, to the detriment of predictive power. We propose a
novel method that injects numeric edge attributes into the scoring layer of a
traditional knowledge graph embedding architecture. Experiments with publicly
available numeric-enriched knowledge graphs show that our method outperforms
traditional numeric-unaware baselines as well as the recent UKGE model.

本文提出了一种将数字边属性注入传统的知识图谱嵌入架构的评分层的新方法，研究结果表明，这种方法优于传统的缺乏数字边知识和最近的 UKGE 模型。

从包含数值边属性的知识图谱中学习嵌入

Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes

Embedding models for deterministic Knowledge Graphs (KG) have been
extensively studied, with the purpose of capturing latent semantic relations
between entities and incorporating the structured knowledge into machine
learning. However, there are many KGs that model uncertain knowledge, which
typically model the inherent uncertainty of relations facts with a confidence
score, and embedding such uncertain knowledge represents an unresolved
challenge. The capturing of uncertain knowledge will benefit many
knowledge-driven applications such as question answering and semantic search by
providing more natural characterization of the knowledge. In this paper, we
propose a novel uncertain KG embedding model UKGE, which aims to preserve both
structural and uncertainty information of relation facts in the embedding
space. Unlike previous models that characterize relation facts with binary
classification techniques, UKGE learns embeddings according to the confidence
scores of uncertain relation facts. To further enhance the precision of UKGE,
we also introduce probabilistic soft logic to infer confidence scores for
unseen relation facts during training. We propose and evaluate two variants of
UKGE based on different learning objectives. Experiments are conducted on three
real-world uncertain KGs via three tasks, i.e. confidence prediction, relation
fact ranking, and relation fact classification. UKGE shows effectiveness in
capturing uncertain knowledge by achieving promising results on these tasks,
and consistently outperforms baselines on these tasks.

本文提出了一种新颖的不确定性知识图谱嵌入模型 UKGE，其目的是在嵌入空间中保留结构和不确定性信息，该模型通过学习不确定关系事实的置信度得出嵌入，并引入概率软逻辑来推断训练期间未见关系事实的置信度，实验表明 UKGE 在不确定知识的捕捉上非常有效，并在置信度预测，关系事实排名和关系事实分类等任务中表现出色。