Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

In addressing the imbalanced issue of data within the realm of Natural Language
Processing, text data augmentation methods have emerged as pivotal solutions. This data …

Geoscience knowledge graph (GKG) can organize various geoscience knowledge into a
machine understandable and computable semantic network and is an effective way to …

Large language models (LLMs) have demonstrated remarkable advancements, primarily
due to their capabilities in modeling the hidden relationships within text sequences. This …

Automatic disease diagnosis from symptoms has attracted much attention in medical
practices. It can assist doctors and medical practitioners in narrowing down disease …

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a
non-stationary stream of data. Since the introduction of new relational tasks can overshadow …

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a
challenge for knowledge engineering to construct a high-quality domain knowledge graph …

摘要学科交叉的研究是促进各种复杂科学问题解决的重要途径. 本文利用国家自然科学基金所
资助项目中人工智能学科与信息管理学科关键词之间的共现关系, 通过word2vec 相关模型 …

We construct a spacecraft performance-fault relationship graph of the control system, which
can help space robots locate and repair spacecraft faults quickly. In order to improve the …

A knowledge graph is a visual method that can display the information contained in the
knowledge points, core structure, and comprehensive knowledge structure technology. In …