Knowledge Graphs
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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
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost. This cost is incurred during indexing time, through the use of expensive Knowledg…
Multi-hop Question AnsweringRepresentation LearningKnowledge GraphsExtracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models
The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. However, essential data necessary for these models and simulations ar…
Knowledge GraphsSymbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics
Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and tool-using agents stay unreliable: even frontier models answer littl…
Knowledge GraphsThe 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 Graphs