Abstract Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

The unprecedented performance of large language models (LLMs) necessitates
improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we …

Most existing methods for relation extraction tasks depend heavily on large-scale annotated
data; they cannot learn from existing knowledge and have low generalization ability. It is …

Existing technologies expand BERT from different perspectives, eg designing different pre-
training tasks, different semantic granularities, and different model architectures. Few …

Deep text understanding, which requires the connections between a given document and
prior knowledge beyond its text, has been highlighted by many benchmarks in recent years …

The prosperity of massive open online courses provides fodder for plentiful research efforts
on adaptive learning. However, current open-access educational datasets are still far from …

Cross-lingual entity alignment has attracted considerable attention in recent years. Past
studies using conventional approaches to match entities share the common problem of …

Large-scale pre-trained language models (PLMs) have shown promising advances on
various downstream tasks, among which dialogue is one of the most concerned. However …

Big earth data is a cross‐domain of geoscience and information science, which provides a
novel perspective for solving geoscience problems. Most contemporary research is driven …

Complex question-answering (CQA) involves answering complex natural-language
questions on a knowledge base (KB). However, the conventional neural program induction …