Sparsity of formal knowledge and roughness of non-ontological construction
make sparsity problem particularly prominent in Open Knowledge Graphs
(OpenKGs). Due to sparse links, learning effective representation for few-shot
entities becomes difficult. We hypothesize that by introducing negative
samples, a contrastive learning (CL) formulation could be beneficial in such
scenarios. However, existing CL methods model KG triplets as binary objects of
entities ignoring the relation-guided ternary propagation patterns and they are
too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that
appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based
on ternary propagation patterns among head, relation and tail. TernaryCL
designs Contrastive Entity and Contrastive Relation to mine ternary
discriminative features with both negative entities and relations, introduces
Contrastive Self to help zero- and few-shot entities learn discriminative
features, Contrastive Synonym to model synonymous entities, and Contrastive
Fusion to aggregate graph features from multiple paths. Extensive experiments
on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art
models.

本文提出了一种基于三元传播模式的对比学习框架 TernaryCL，该框架结合了多种方法以解决在 OpenKGs 中出现的零样本、少样本和同义词问题，并证明了其在基准测试中的优越性。

使用三元对比学习缓解开放知识图谱的稀疏性

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning

Open Knowledge Graphs (OpenKG) refer to a set of (head noun phrase, relation
phrase, tail noun phrase) triples such as (tesla, return to, new york)
extracted from a corpus using OpenIE tools. While OpenKGs are easy to bootstrap
for a domain, they are very sparse and far from being directly usable in an end
task. Therefore, the task of predicting new facts, i.e., link prediction,
becomes an important step while using these graphs in downstream tasks such as
text comprehension, question answering, and web search query recommendation.
Learning embeddings for OpenKGs is one approach for link prediction that has
received some attention lately. However, on careful examination, we found that
current OpenKG link prediction algorithms often predict noun phrases (NPs) with
incompatible types for given noun and relation phrases. We address this problem
in this work and propose OKGIT that improves OpenKG link prediction using novel
type compatibility score and type regularization. With extensive experiments on
multiple datasets, we show that the proposed method achieves state-of-the-art
performance while producing type compatible NPs in the link prediction task.

本文提出了一种新的方法 OKGIT 用于改善 Open Knowledge Graphs 中链接预测，并提出了一种新的类型兼容性得分和类型正则化，实验证明该方法可以产生与类型兼容的名词短语，从而达到了最好的性能。

OKGIT: 基于隐式类型的开放知识图谱链接预测

OKGIT: Open Knowledge Graph Link Prediction with Implicit Types

Noun phrases and Relation phrases in open knowledge graphs are not
canonicalized, leading to an explosion of redundant and ambiguous
subject-relation-object triples. Existing approaches to solve this problem take
a two-step approach. First, they generate embedding representations for both
noun and relation phrases, then a clustering algorithm is used to group them
using the embeddings as features. In this work, we propose Canonicalizing Using
Variational Autoencoders (CUVA), a joint model to learn both embeddings and
cluster assignments in an end-to-end approach, which leads to a better vector
representation for the noun and relation phrases. Our evaluation over multiple
benchmarks shows that CUVA outperforms the existing state-of-the-art
approaches. Moreover, we introduce CanonicNell, a novel dataset to evaluate
entity canonicalization systems.

本文提出一种名为 CUVA 的联合模型，使用变分自编码器来进行实体和关系短语的嵌入式特征提取与聚类，改善了 open knowledge graphs 中实体重复和模糊性的问题，并在多个基准测试中证明了其优于现有最先进技术的表现。同时为此也构建了一个新的数据集 CanonicNell 用于评估实体归一化系统。