Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE)
tasks. While most previous RE methods focus on leveraging deterministic KGs,
uncertain KGs, which assign a confidence score for each relation instance, can
provide prior probability distributions of relational facts as valuable
external knowledge for RE models. This paper proposes to exploit uncertain
knowledge to improve relation extraction. Specifically, we introduce ProBase,
an uncertain KG that indicates to what extent a target entity belongs to a
concept, into our RE architecture. We then design a novel multi-view inference
framework to systematically integrate local context and global knowledge across
three views: mention-, entity- and concept-view. The experimental results show
that our model achieves competitive performances on both sentence- and
document-level relation extraction, which verifies the effectiveness of
introducing uncertain knowledge and the multi-view inference framework that we
design.

本文提出了一种基于不确定知识图谱的关系抽取模型，该模型采用多视角推理框架来整合本地上下文和全局知识。实验结果表明，引入不确定知识和多视角推理框架的模型在句子和文档级别关系抽取上都具有竞争性能。

不确定知识下的关系抽取多视图推理

Multi-view Inference for Relation Extraction with Uncertain Knowledge

Text representations using neural word embeddings have proven effective in
many NLP applications. Recent researches adapt the traditional word embedding
models to learn vectors of multiword expressions (concepts/entities). However,
these methods are limited to textual knowledge bases (e.g., Wikipedia). In this
paper, we propose a novel and simple technique for integrating the knowledge
about concepts from two large scale knowledge bases of different structure
(Wikipedia and Probase) in order to learn concept representations. We adapt the
efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia
text and Probase concept graph. We evaluate our concept embedding models on two
tasks: (1) analogical reasoning, where we achieve a state-of-the-art
performance of 91% on semantic analogies, (2) concept categorization, where we
achieve a state-of-the-art performance on two benchmark datasets achieving
categorization accuracy of 100% on one and 98% on the other. Additionally, we
present a case study to evaluate our model on unsupervised argument type
identification for neural semantic parsing. We demonstrate the competitive
accuracy of our unsupervised method and its ability to better generalize to out
of vocabulary entity mentions compared to the tedious and error prone methods
which depend on gazetteers and regular expressions.

本文提出了一种新颖而简单的技术，将来自不同结构的两个大型知识库中的概念知识（Wikipedia 和 Probase）整合起来，学习概念表示，并在两个任务上评估了概念嵌入模型：类比推理和概念分类，并展示了无监督环境中用于神经语义分析的参数识别的案例研究，相对于 gazetteer 和正则表达式等繁琐易出错的方法，我们的无监督方法具有更好的泛化能力。