Knowledge graph-enhanced molecular contrastive learning with functional prompt

Text-Enhanced Representation Learning for Knowledge Graph

Learning the representations of a knowledge graph has attracted significant research interest in the field of intelligent Web. By regarding each relation as one translation from head entity to tail entity, translation-based methods including TransE, TransH and TransR are simple, effective and achieving the state-of-the-art performance. However, they still suffer the following issues: (i) low pe...

Personal Knowledge/Learning Graph

Educational data mining and learning analytics have to date largely focused on specific research questions that provide insight into granular interactions. These insights have been abstracted to include the development of predictive models, intelligent tutors, and adaptive learning. While there are several domains where holistic or systems models have provided additional explanatory power, work...

9. Contrastive Knowledge

Contrastive Knowledge Surveyed

Suppose that Ann says, “Keith knows that the bank will be open tomorrow.” Her audience may well agree. Her knowledge ascription may seem true. But now suppose that Ben—in a different context—also says “Keith knows that the bank will be open tomorrow.” His audience may well disagree. His knowledge ascription may seem false. Indeed, a number of philosophers have claimed that people’s intuitions a...

Hierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents

This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...