5 citations · 21 across the 10 of their papers we have counts for
10 papers
Knowledge-augmented Few-shot Visual Relation Detection
Tianyu Yu, Yangning Li, Jiaoyan Chen +8
Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relati…
Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs
Yuanning Cui, Yuxin Wang, Zequn Sun +4
Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the dev…
Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities
Yuanning Cui, Yuxin Wang, Zequn Sun +4
Over the years, reasoning over knowledge graphs (KGs), which aims to infer new conclusions from known facts, has mostly focused on static KGs. The unceasing growth of knowledge in…
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,…
Retrieve Synonymous keywords for Frequent Queries in Sponsored Search in a Data Augmentation Way
Yijiang Lian, Zhenjun You, Fan Wu +2
In sponsored search, retrieving synonymous keywords is of great importance for accurately targeted advertising. The semantic gap between queries and keywords and the extremely high…
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
Wenqiang Liu, Hongyun Cai, Xu Cheng +3
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the ben…
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Meng Wang, Jiaheng Zhang, Jun Liu +4
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers ma…
Exploiting Source-Object Network to Resolve Object Conflicts in Linked Data
Wenqiang Liu, Jun Liu, Haimeng Duan +2
Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple dif…
TruthDiscover: Resolving Object Conflicts on Massive Linked Data
Wenqiang Liu, Jun Liu, Jian Zhang +2
Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-struct…
Truth Discovery to Resolve Object Conflicts in Linked Data
Wenqiang Liu
In the community of Linked Data, anyone can publish their data as Linked Data on the web because of the openness of the Semantic Web. As such, RDF (Resource Description Framework)…