Paper Citation Record · LEDGER

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models

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.

pith.paper-citation-record.v1
2411.14858 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:52:40.078129Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact4
verified fuzzy13
unresolved20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5cb633e4-becd-44f6-88e6-4346c6d2ac8a · outbound

This paper cites Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann.

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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Source-reported events for the cited work

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Observation c5d76941-20e2-4d0c-b1c7-187f5d5b4546 · outbound

This paper cites Knowledge vault: a web-scale approach to probabilis- tic knowledge fusion.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Knowledge vault: a web-scale approach to probabilis- tic knowledge fusion

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:39.969014Z

Source-reported events for the cited work

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Observation 3e84b6f3-e19d-4393-b724-3d2a680b97fe · outbound

This paper cites Translating embeddings for modeling multi-relational data.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Translating embeddings for modeling multi-relational data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.804064Z

Source-reported events for the cited work

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Observation b914d3b8-0f49-46dd-80b8-9ccbaa310564 · outbound

This paper cites Factorizing Y AGO: scalable machine learning for linked data.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Factorizing Y AGO: scalable machine learning for linked data

Reference 4

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Source-reported events for the cited work

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Observation 5ceb0ea3-0e53-4d41-9110-daa8db889856 · outbound

This paper cites Comprehensive analysis of negative sampling in knowledge graph representation learning.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Comprehensive analysis of negative sampling in knowledge graph representation learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.784347Z

Source-reported events for the cited work

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Observation ce882abc-d145-4670-a007-f6bbd617147d · outbound

This paper cites Negative Sampling in Knowledge Graph Representation Learning: A Review.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Negative Sampling in Knowledge Graph Representation Learning: A Review

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:39.984338Z

Source-reported events for the cited work

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Observation b1cc0783-7dcd-4dc8-ab43-a22ef123bd9f · outbound

This paper cites Type-constrained representation learning in knowledge graphs.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Type-constrained representation learning in knowledge graphs

Reference 7

Resolution: verified exact; doi, observed 2026-08-12T14:52:40.775579Z

Source-reported events for the cited work

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Observation 27f8f36b-7edd-42d3-b13c-0b1cc8a2edf5 · outbound

This paper cites You CAN teach an old dog new tricks! on training knowledge graph embeddings.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models You CAN teach an old dog new tricks! on training knowledge graph embeddings

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.765939Z

Source-reported events for the cited work

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Observation 631836d4-d14a-4feb-8f58-6599b5091260 · outbound

This paper cites Convolutional 2d knowledge graph embeddings.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Convolutional 2d knowledge graph embeddings

Reference 9

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Source-reported events for the cited work

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Observation 94e2e4cd-dc13-4625-ae7e-ec50b4b8369b · outbound

This paper cites Knowledge graph embedding: A survey from the perspective of representation spaces.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Knowledge graph embedding: A survey from the perspective of representation spaces

Reference 10

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Source-reported events for the cited work

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Observation 9344acb1-27ed-4e45-8322-820e358a0eca · outbound

This paper cites Investigations on knowledge base embedding for relation prediction and extraction, 2018.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.751220Z

Source-reported events for the cited work

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Observation dbdc6802-e367-408d-a142-78750b86e1a0 · outbound

This paper cites Pytorch-biggraph: A large scale graph embedding system.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Pytorch-biggraph: A large scale graph embedding system

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.742724Z

Source-reported events for the cited work

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Observation adb901ae-9f8d-40b4-bc2e-81ffbc32e9ff · outbound

This paper cites Knowledge graph embedding by translating on hyperplanes.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Knowledge graph embedding by translating on hyperplanes

Reference 13

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Source-reported events for the cited work

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Observation ecae6a88-5c54-4e0a-ae72-127c3306cbc6 · outbound

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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation eab78df0-30db-4d41-9ee2-7e4b0c6c8122 · outbound

This paper cites A novel negative sample generating method for knowledge graph embedding.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models A novel negative sample generating method for knowledge graph embedding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.725389Z

Source-reported events for the cited work

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Observation 6baef5ea-3001-48f8-ad14-8dcc81c13659 · outbound

This paper cites A novel negative sampling based on frequency of relational association entities for knowledge graph embedding.

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

Resolution: verified exact; raw_fallback, observed 2026-08-12T14:52:40.543455Z

Source-reported events for the cited work

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Observation 813ae72f-5ed7-41d5-bd31-ad0391b1d0e5 · outbound

This paper cites Enhancing knowledge graph embedding with probabilistic negative sampling.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Enhancing knowledge graph embedding with probabilistic negative sampling

Reference 17

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Source-reported events for the cited work

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Observation 3aec8c6e-e3f4-4fe8-9ae5-19078878b00e · outbound

This paper cites Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs.

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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Source-reported events for the cited work

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Observation 41c184fa-0a0c-4739-af6d-03f171296150 · outbound

This paper cites LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings.

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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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Unresolved cited work

Reference 20

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Observation 28130e6e-8a38-49b5-9946-71b9172023ec · outbound

This paper cites Incorporating domain and range of relations for knowledge graph completion.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Incorporating domain and range of relations for knowledge graph completion

Reference 21

Resolution: verified exact; doi, observed 2026-08-12T14:52:40.118405Z

Source-reported events for the cited work

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Observation 75f118e9-1fb5-46e8-8535-6440e13a9b97 · outbound

This paper cites Conditional constraints for knowledge graph embeddings.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Conditional constraints for knowledge graph embeddings

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.716566Z

Source-reported events for the cited work

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Observation 059177b4-1ccb-43d0-9bc5-f512160dae2b · outbound

This paper cites Rotate: Knowledge graph em- bedding by relational rotation in complex space.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.707529Z

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Observation 34aa2bb3-d2fc-4a01-83d2-809b4b928d3c · outbound

This paper cites Confidence-aware negative sampling method for noisy knowledge graph embedding.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Confidence-aware negative sampling method for noisy knowledge graph embedding

Reference 24

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Observation 13183617-f1c7-4f72-b5c9-c29a4db41ea2 · outbound

This paper cites Nscaching: Simple and efficient negative sampling for knowledge graph embedding.

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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Source-reported events for the cited work

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Observation 465e2512-3ff0-4177-a8d2-b0cce99adbab · outbound

This paper cites Adversarial knowledge representation learning without external model.

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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Source-reported events for the cited work

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This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.698221Z

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Observation 131f6c17-3fed-4316-bd81-c43b439a0049 · outbound

This paper cites Reconciling competing sampling strategies of network embedding.

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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Observation e3694f62-d94e-4d13-a835-9c2c029dd219 · outbound

This paper cites Observed versus latent features for knowledge base and text inference.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Observed versus latent features for knowledge base and text inference

Reference 29

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Source-reported events for the cited work

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Observation 2eb7215c-7937-47d7-840d-cc67c0dfe955 · outbound

This paper cites Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

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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Observation 8e7ec21c-051d-4a2e-93f8-d1ce6462c7a9 · outbound

This paper cites Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery.

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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Observation 014d9907-9d5f-4852-a038-0e67d06daf2f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Adam: A Method for Stochastic Optimization

Reference 32

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Observation c4125100-6937-455e-b895-926244745ef8 · outbound

This paper cites Embedding Entities and Relations for Learning and Inference in Knowledge Bases.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:40.063647Z

Source-reported events for the cited work

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Observation 63fdf4b1-ddfb-42a3-9e5c-ff58afcac8e2 · outbound

This paper cites Complex embeddings for simple link prediction.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Complex embeddings for simple link prediction

Reference 34

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Observation 87a23c46-9e42-4342-abbf-d0aff3cd64ab · outbound

This paper cites Canonical tensor decomposition for knowledge base completion.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Canonical tensor decomposition for knowledge base completion

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.671708Z

Source-reported events for the cited work

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Observation 0ee7e238-73b7-407c-a792-bec642c6624e · outbound

This paper cites Tucker: Tensor factorization for knowledge graph completion.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Tucker: Tensor factorization for knowledge graph completion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:40.662114Z

Source-reported events for the cited work

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Observation fe79c214-26e8-44b0-9e0e-5821f57cd15e · outbound

This paper cites A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:40.075098Z

Source-reported events for the cited work

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Observation 5d5731fe-3ac1-4dd5-a7d4-b634b2a4c2dd · outbound

This paper cites AmpliGraph: a Library for Representation Learning on Knowledge Graphs, March 2019.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models AmpliGraph: a Library for Representation Learning on Knowledge Graphs, March 2019

Reference 38

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Observation 8eb555ef-be5f-4109-91cd-b7ae21b599c2 · outbound

This paper cites doi: 10.18653/V1/P17-1088.

Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models doi: 10.18653/V1/P17-1088

Reference 962

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Observation 7f8f6e8b-2ecf-4acd-ac4b-5c26e29a8c86 · outbound

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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models Unresolved cited work

Reference 2013

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:52:40.794156Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.