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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 2026-08-12T06:34:41.77262+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

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  • 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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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

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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

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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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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

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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

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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

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verified exact
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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

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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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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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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

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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

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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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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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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

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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

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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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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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Observation 330cecef-22ef-4853-b687-4a6518794717 · outbound

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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

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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

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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

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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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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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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

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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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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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Source-reported events for the cited work

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

Reference 32

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Source-reported events for the cited work

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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

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

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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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Source-reported events for the cited work

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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

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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

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:52:40.072263Z digest=sha256:deec53317ee06d119a35156bdad661e28c2c951dbc5aa94a37780114fd65194b

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:52:40.075098Z digest=sha256:67037aa27269bddb2124c7b17e93b15964308297e457d10a7971d860f91a9ef8

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

Resolution
malformed identifier
no resolver link, observed 2026-08-12T14:52:40.078129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:52:40.078129Z digest=sha256:024010b12be1c68e594b36f588c556fe737660ec8400b0f00e1dd49dfe98f12c

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

Resolution
unresolved
no resolver link, observed 2026-08-12T14:52:40.009613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:52:40.009613Z digest=sha256:e699cb04f5463ae86b08f5ce1f36d1f8b849f183bdf70f251cf65eeb8de93d7a

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

This paper cites an unresolved cited work.

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

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:52:39.975152Z digest=sha256:2ee420129b3aa6a89849268418aea0a41717de9f243a4ff93246f0bd29011c52

Pith citing papers

No inbound Pith citation observations are available.