When it comes to comprehending and analyzing multi-relational data, the
semantics of relations are crucial. Polysemous relations between different
types of entities, that represent multiple semantics, are common in real-world
relational datasets represented by knowledge graphs. For numerous use cases,
such as entity type classification, question answering and knowledge graph
completion, the correct semantic interpretation of these relations is
necessary. In this work, we provide a strategy for discovering the different
semantics associated with abstract relations and deriving many sub-relations
with fine-grained meaning. To do this, we leverage the types of the entities
associated with the relations and cluster the vector representations of
entities and relations. The suggested method is able to automatically discover
the best number of sub-relations for a polysemous relation and determine their
semantic interpretation, according to our empirical evaluation.

本文提出了一种发现抽象关系中不同语义和获取细致意义的子关系的策略，其方法基于关系涉及的实体类型和向量表示的聚类，能够自动发现多义关系的最佳子关系及其语义解释。

在知识图谱关系中发现细粒度语义

Discovering Fine-Grained Semantics in Knowledge Graph Relations

In this work, we investigate several neural network architectures for
fine-grained entity type classification. Particularly, we consider extensions
to a recently proposed attentive neural architecture and make three key
contributions. Previous work on attentive neural architectures do not consider
hand-crafted features, we combine learnt and hand-crafted features and observe
that they complement each other. Additionally, through quantitative analysis we
establish that the attention mechanism is capable of learning to attend over
syntactic heads and the phrase containing the mention, where both are known
strong hand-crafted features for our task. We enable parameter sharing through
a hierarchical label encoding method, that in low-dimensional projections show
clear clusters for each type hierarchy. Lastly, despite using the same
evaluation dataset, the literature frequently compare models trained using
different data. We establish that the choice of training data has a drastic
impact on performance, with decreases by as much as 9.85% loose micro F1 score
for a previously proposed method. Despite this, our best model achieves
state-of-the-art results with 75.36% loose micro F1 score on the well-
established FIGER (GOLD) dataset.

本文采用几种神经网络结构进行细粒度实体类型分类研究，并考虑了对最近提出的注意力神经结构的扩展，得出了三个关键贡献：结合学习和手工特征，同时观察它们互补作用；定量分析表明该注意力机制能够学习关注句法头和包含提及的短语，在我们的任务中，这些都是强大的手工特征；通过分层标签编码方法实现参数共享，在低维投影中显示出每种类型层次的明显聚类；虽然使用相同的评估数据集，但文献经常比较使用不同数据训练的模型，我们发现训练数据的选择对性能有显着影响，甚至会降低先前提出的方法的松散微 F1 得分高达 9.85％，但我们的最佳模型在广为人知的 FIGER（GOLD）数据集上实现了最新成果，松弛的微 F1 得分为 75.36％。