24 citations · 46 across the 17 of their papers we have counts for
6 papers · 1 filter
Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation
Zhoutian Shao, Yuanning Cui, Wei Hu
Knowledge graphs (KGs) are widely acknowledged as incomplete, and new entities are constantly emerging in the real world. Inductive KG reasoning aims to predict missing facts for t…
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs
Zequn Sun, Jiacheng Huang, Jinghao Lin +3
In this paper, we present the ``joint pre-training and local re-training'' framework for learning and applying multi-source knowledge graph (KG) embeddings. We are motivated by the…
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction
Xinyi Wang, Zitao Wang, Wei Hu
Continual few-shot relation extraction (RE) aims to continuously train a model for new relations with few labeled training data, of which the major challenges are the catastrophic…
: A Library for Multi-source Knowledge Graph Embeddings and Applications
Xindi Luo, Zequn Sun, Wei Hu
This paper presents , an open-source Python library for representation learning over knowledge graphs. supports joint representation learning over multi-so…
Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs
Yuxin Wang, Yuanning Cui, Wenqiang Liu +4
Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static,…
Enhancing Document-level Relation Extraction by Entity Knowledge Injection
Xinyi Wang, Zitao Wang, Weijian Sun +1
Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowl…