Search Results for author:
Found 9 papers, 5 papers with code
Logic and Commonsense-Guided Temporal Knowledge Graph Completion
To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense.
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance.
Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
Entity Concept-enhanced Few-shot Relation Extraction
Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.
