Knowledge Graphs
4개 벤치마크 · 논문 3,741편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Modeling Relational Data with Graph Convolutional Networks
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Embedding Logical Queries on Knowledge Graphs
Inductive Relation Prediction by Subgraph Reasoning
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Papers
The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs
A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model what to do with them. We vary all four ov…
Graph Question AnsweringKnowledge GraphsFrom State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins
As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities i…
Semantic CommunicationKnowledge GraphsCommonsense Reasoning in Computer Vision: Foundations, Recent Advancements, and Future Directions
Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial for enhancing AI's understanding of everyday scenarios. This understanding not only improves machine learning…
Object RecognitionKnowledge GraphsContinual Graph Memory for Adaptive Recommendation under Intent Drift
This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome can reveal whether the relational evidence used for ranking is useful, missing, or misleading. While Knowledg…
Knowledge GraphsPyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionAdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two distinct challenges that general-domain methods are not designed for: (…
Knowledge Graphs