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Found 7 papers, 3 papers with code
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference
Knowledge graph inference has been studied extensively due to its wide applications.
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text
Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.
Neural Compositional Rule Learning for Knowledge Graph Reasoning
NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head.
NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
Feature Selection: A Data Perspective
To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).
