CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
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 between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.
Code (1)
Tasks
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsLink PredictionSimilar Papers 제목 키워드 기반
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models cannot make a proper trade-off between t…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLanguage Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained question generation (QG) systems on readin…
Question AnsweringQuestion GenerationQuestion-GenerationReading Comprehension+1CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they struggle to generalize these updates to mult…
knowledge editingCAKE: Real-time Action Detection via Motion Distillation and Background-aware Contrastive Learning
Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of discriminative temporal dynamics against background motion. Adding optical flow could provides stron…
Online Action DetectionContrastive LearningAdaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Tradit…
Hyperparameter Optimization