Temporal relation prediction in incomplete temporal knowledge graphs (TKGs)
is a popular temporal knowledge graph completion (TKGC) problem in both
transductive and inductive settings. Traditional embedding-based TKGC models
(TKGE) rely on structured connections and can only handle a fixed set of
entities, i.e., the transductive setting. In the inductive setting where test
TKGs contain emerging entities, the latest methods are based on symbolic rules
or pre-trained language models (PLMs). However, they suffer from being
inflexible and not time-specific, respectively. In this work, we extend the
fully-inductive setting, where entities in the training and test sets are
totally disjoint, into TKGs and take a further step towards a more flexible and
time-sensitive temporal relation prediction approach SST-BERT, incorporating
Structured Sentences with Time-enhanced BERT. Our model can obtain the entity
history and implicitly learn rules in the semantic space by encoding structured
sentences, solving the problem of inflexibility. We propose to use a time
masking MLM task to pre-train BERT in a corpus rich in temporal tokens
specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To
compute the probability of occurrence of a target quadruple, we aggregate all
its structured sentences from both temporal and semantic perspectives into a
score. Experiments on the transductive datasets and newly generated
fully-inductive benchmarks show that SST-BERT successfully improves over
state-of-the-art baselines.

本文提出了一种基于 SST-BERT 的方法，结合时间增强技术和结构句子编码，用于解决在不完整的时间知识图谱中进行时关系预测的问题。该模型包含时间掩码 MLM 任务，用于在 TKGs 中生成丰富的时间令牌，提高时间敏感性，并在转导和归纳测试数据上有效改善了现有模型。

使用增强时间 BERT 结合结构化语句进行全感知时态关系预测

Incorporating Structured Sentences with Time-enhanced BERT for  Fully-inductive Temporal Relation Prediction

We present LOME, a system for performing multilingual information extraction.
Given a text document as input, our core system identifies spans of textual
entity and event mentions with a FrameNet (Baker et al., 1998) parser. It
subsequently performs coreference resolution, fine-grained entity typing, and
temporal relation prediction between events. By doing so, the system constructs
an event and entity focused knowledge graph. We can further apply third-party
modules for other types of annotation, like relation extraction. Our
(multilingual) first-party modules either outperform or are competitive with
the (monolingual) state-of-the-art. We achieve this through the use of
multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging
multilingual training data. LOME is available as a Docker container on Docker
Hub. In addition, a lightweight version of the system is accessible as a web
demo.

本文介绍了 LOME 系统，用于执行多语言信息提取，通过 FrameNet 解析器标识文本实体和事件提及的跨度，执行指代消解、细粒度实体类型和事件之间的时间关系预测，构建事件和实体关注的知识图谱。LOME 可用作 Docker 容器和网络演示。