2 citations · 10 across the 9 of their papers we have counts for
9 papers
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…
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
Zequn Sun, Jiacheng Huang, Xiaozhou Xu +3
Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a crit…
Deep Active Alignment of Knowledge Graph Entities and Schemata
Jiacheng Huang, Zequn Sun, Qijin Chen +3
Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and cl…
Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
Jiacheng Huang, Yao Zhao, Wei Hu +5
Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this…
Data Augmentation for Text Generation Without Any Augmented Data
Wei Bi, Huayang Li, Jiacheng Huang
Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper d…
Rule-Guided Graph Neural Networks for Recommender Systems
Xinze Lyu, Guangyao Li, Jiacheng Huang +1
To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resource…
TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
Zequn Sun, Jiacheng Huang, Wei Hu +3
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping fu…
Crowdsourced Collective Entity Resolution with Relational Match Propagation
Jiacheng Huang, Wei Hu, Zhifeng Bao +1
Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent stu…
Open Knowledge Enrichment for Long-tail Entities
Ermei Cao, Difeng Wang, Jiacheng Huang +1
Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especiall…