Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:52:40.078129Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2411.14858.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:52:40.078129Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
40 of 40 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann
Reference 1
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Factorizing Y AGO: scalable machine learning for linked data
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Comprehensive analysis of negative sampling in knowledge graph representation learning
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Knowledge graph embedding: A survey from the perspective of representation spaces
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Investigations on knowledge base embedding for relation prediction and extraction, 2018
Reference 11
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Pytorch-biggraph: A large scale graph embedding system
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models A novel negative sample generating method for knowledge graph embedding
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models A novel negative sampling based on frequency of relational association entities for knowledge graph embedding
Reference 16
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Enhancing knowledge graph embedding with probabilistic negative sampling
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Observation 3aec8c6e-e3f4-4fe8-9ae5-19078878b00e · outbound
Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
Reference 18
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings
Reference 19
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Reference 20
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Incorporating domain and range of relations for knowledge graph completion
Reference 21
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Conditional constraints for knowledge graph embeddings
Reference 22
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Rotate: Knowledge graph em- bedding by relational rotation in complex space
Reference 23
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Confidence-aware negative sampling method for noisy knowledge graph embedding
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Nscaching: Simple and efficient negative sampling for knowledge graph embedding
Reference 25
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Adversarial knowledge representation learning without external model
Reference 26
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C
Reference 27
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Reconciling competing sampling strategies of network embedding
Reference 28
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Observed versus latent features for knowledge base and text inference
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Reference 30
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Reference 31
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Adam: A Method for Stochastic Optimization
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Embedding Entities and Relations for Learning and Inference in Knowledge Bases
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Complex embeddings for simple link prediction
Reference 34
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Tucker: Tensor factorization for knowledge graph completion
Reference 36
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
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Observation 5d5731fe-3ac1-4dd5-a7d4-b634b2a4c2dd · outbound
Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models AmpliGraph: a Library for Representation Learning on Knowledge Graphs, March 2019
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models doi: 10.18653/V1/P17-1088
Reference 962
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Unresolved cited work
Reference 2013
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No inbound Pith citation observations are available.