8 citations · 36 across the 9 of their papers we have counts for
9 papers
Logic and Commonsense-Guided Temporal Knowledge Graph Completion
Guanglin Niu, Bo Li
A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Be…
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
Guanglin Niu, Bo Li, Yongfei Zhang +1
Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links bet…
Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
Guanglin Niu, Bo Li, Yongfei Zhang +1
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based mode…
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
Guanglin Niu, Yang Li, Chengguang Tang +6
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer. Recent approaches attempt to…
Entity Concept-enhanced Few-shot Relation Extraction
Shan Yang, Yongfei Zhang, Guanglin Niu +2
Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fai…
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Guanglin Niu, Yang Li, Chengguang Tang +7
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing mo…
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Guanglin Niu, Bo Li, Yongfei Zhang +4
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information ov…
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding
Guanglin Niu, Bo Li, Yongfei Zhang +2
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type…
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Guanglin Niu, Yongfei Zhang, Bo Li +4
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay atte…