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Paper Citation Record · LEDGER

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift

As of 9 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:2507.05110.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.05110 v3

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:39:46.493578Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

74 of 74 outbound references displayed

  • verified exact1
  • verified fuzzy62
  • unresolved10
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ee7aefa-e604-4b28-8133-dd19da9242a5 · outbound

This paper cites Dbpedia: A nucleus for a web of open data,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Dbpedia: A nucleus for a web of open data,

Reference 1

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Source-reported events for the cited work

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Observation dffad088-4618-4210-a3ad-f10e46907acf · outbound

This paper cites Yago: a core of semantic knowledge,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Yago: a core of semantic knowledge,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d66d0c0b-b075-4c6d-bb2e-42ac7315f296 · outbound

This paper cites Hkgb: an inclusive, extensible, intelligent, semi- auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Hkgb: an inclusive, extensible, intelligent, semi- auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated,

Reference 3

Resolution
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Source-reported events for the cited work

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Observation c9bd7697-a2ec-47ee-9dd6-66af16acc7d3 · outbound

This paper cites Kgat: Knowledge graph attention network for recommendation,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Kgat: Knowledge graph attention network for recommendation,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0ff36f2c-e858-4ee8-8fc2-2392bca09656 · outbound

This paper cites Relational learning analysis of social politics using knowledge graph embedding,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Relational learning analysis of social politics using knowledge graph embedding,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b1ae3cb2-3d48-46d2-8e8b-ce9b23016219 · outbound

This paper cites A comprehensive overview of knowledge graph completion,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A comprehensive overview of knowledge graph completion,

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9c7133c8-2804-488d-bc9d-1ec8c3986130 · outbound

This paper cites A review: Knowledge reasoning over knowledge graph,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A review: Knowledge reasoning over knowledge graph,

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2192f226-938a-4736-820b-694caa6f6d99 · outbound

This paper cites A survey on knowl- edge graph embedding: Approaches, applications and bench- marks,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A survey on knowl- edge graph embedding: Approaches, applications and bench- marks,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 61ec0607-e2cb-4cc8-973c-f37759d5415a · outbound

This paper cites Differentiable learning of logical rules for knowledge base reasoning,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Differentiable learning of logical rules for knowledge base reasoning,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T19:39:55.832925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation cf39f5ee-d766-43f6-9faa-71b57794cb1d · outbound

This paper cites Sparsity and noise: Where knowledge graph embeddings fall short,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Sparsity and noise: Where knowledge graph embeddings fall short,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2a2946c5-6f60-40f5-a799-be6025232133 · outbound

This paper cites From local structures to size generalization in graph neural networks,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift From local structures to size generalization in graph neural networks,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 083b2ede-d9e3-4800-9aa6-353a33984bd6 · outbound

This paper cites Size-invariant graph representations for graph classification extrapolations,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Size-invariant graph representations for graph classification extrapolations,

Reference 12

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 7cc31c97-169a-40c7-b534-905a35d8ba84 · outbound

This paper cites Logical rule learning,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Logical rule learning,

Reference 13

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5aa6c712-bab8-449f-9052-00f24c9f3469 · outbound

This paper cites Ood-gnn: Out-of- distribution generalized graph neural network,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Ood-gnn: Out-of- distribution generalized graph neural network,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0374e134-a2ea-4748-a021-96f71487f888 · outbound

This paper cites Rnnlogic: Learning logic rules for reasoning on knowledge graphs,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Rnnlogic: Learning logic rules for reasoning on knowledge graphs,

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-06T19:39:54.693784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ffea885d-e642-4e71-bb27-c1eedb11d429 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 16

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no resolver link, observed 2026-08-06T19:39:41.474214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 88b84502-c750-4994-82a1-0dbcf7e58bc5 · outbound

This paper cites Pretrained Transformers Improve Out-of-Distribution Robustness.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Pretrained Transformers Improve Out-of-Distribution Robustness

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ce0029f8-b1e9-4cd9-9d2d-24424667c354 · outbound

This paper cites Deep stable learning for out-of-distribution generalization,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Deep stable learning for out-of-distribution generalization,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 89e20911-dcad-4861-8153-5406a362e8ba · outbound

This paper cites Rlogic: Recursive logical rule learning from knowledge graphs,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Rlogic: Recursive logical rule learning from knowledge graphs,

Reference 19

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e4f29279-3657-47ea-b7b6-5e503efab8ce · outbound

This paper cites The mean and variance of the distribution of shortest path lengths of random regular graphs,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift The mean and variance of the distribution of shortest path lengths of random regular graphs,

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation a708f85f-3254-4a6d-a280-c99b900a3f69 · outbound

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

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Translating embeddings for modeling multi- relational data,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 513eb61f-a40e-4da9-a4c2-bc6fb649c557 · outbound

This paper cites Knowledge graph embedding by translating on hyperplanes,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Knowledge graph embedding by translating on hyperplanes,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e2e7b553-0f91-4d84-a83f-e5a7183d022c · outbound

This paper cites Learning entity and relation embeddings for knowledge graph completion,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Learning entity and relation embeddings for knowledge graph completion,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 565d3b62-d47e-45e5-92ba-7dedd69c6d21 · outbound

This paper cites RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 023807c5-6d42-468a-b11b-31d7edc05e4b · outbound

This paper cites A three-way model for collective learning on multi-relational data.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A three-way model for collective learning on multi-relational data

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c52286ae-0cba-4b68-806b-9548922eae48 · outbound

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

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T19:39:42.394591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6559f0a5-8ccc-40a3-a4e6-1677d81fda62 · outbound

This paper cites Complex embeddings for simple link prediction,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Complex embeddings for simple link prediction,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.693269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f65556de-5274-45e3-a978-3a78e9b7b404 · outbound

This paper cites Modeling relational data with graph convolu- tional networks,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Modeling relational data with graph convolu- tional networks,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.555328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d23ab742-6c4b-4afa-8f95-64b3564d50ae · outbound

This paper cites Robust embedding with multi-level structures for link prediction.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Robust embedding with multi-level structures for link prediction

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.465503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 81c21e55-7a58-431c-abd9-61ed47e93532 · outbound

This paper cites Composition- based multi-relational graph convolutional networks,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Composition- based multi-relational graph convolutional networks,

Reference 30

Resolution
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raw_fallback, observed 2026-08-06T19:39:53.337612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 48042004-3c39-4093-8266-28a7f567e705 · outbound

This paper cites Inductive relation prediction by subgraph reasoning,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Inductive relation prediction by subgraph reasoning,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.241407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation a999e956-a45e-4c57-9015-22a54f3338eb · outbound

This paper cites Neural bellman- ford networks: A general graph neural network framework for link prediction,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Neural bellman- ford networks: A general graph neural network framework for link prediction,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.127266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:43.071892Z digest=sha256:add01bfc5651e81214c451d1bd3971d7a913c2e87cc9e9e7a68ded4c74c5c5eb

Observation 52ba68b8-6f3b-46c4-86f0-085ce4cfa4f1 · outbound

This paper cites A* net: A scalable path-based reasoning approach for knowledge graphs,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A* net: A scalable path-based reasoning approach for knowledge graphs,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:53.010360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:43.150183Z digest=sha256:a6358f66d5a7f4ea018c9b7da51ecf35c65fad62faee4f2d93e47f1b08583a00

Observation bcca59a7-c98b-473f-9e0d-294be23e86b9 · outbound

This paper cites Knowledge graph reasoning with relational digraph,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Knowledge graph reasoning with relational digraph,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:52.861094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:43.228568Z digest=sha256:76016364032ebea12708e5650c2f59ab2bf2d2e9978723a7fe705c8a9ab22462

Observation 63bf1d0b-5e77-4aa9-a8cc-8b956b6f70b6 · outbound

This paper cites Inductive logic programming: Theory and methods,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Inductive logic programming: Theory and methods,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:52.706046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 90729474-da9e-42cc-83ae-1fbcd9db6e66 · outbound

This paper cites an unresolved cited work.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:39:52.602865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5e6dd53a-8ef1-4ed0-bfa7-124ac59f2b19 · outbound

This paper cites End-to-end differentiable proving,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift End-to-end differentiable proving,

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c01d00bd-a01e-4fad-9777-8f7294d9c13c · outbound

This paper cites Drum: End-to-end differentiable rule mining on knowledge graphs,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Drum: End-to-end differentiable rule mining on knowledge graphs,

Reference 38

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0d20323e-cbbe-4212-af75-86ee12b9705a · outbound

This paper cites Neural compositional rule learning for knowledge graph reasoning,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Neural compositional rule learning for knowledge graph reasoning,

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 15d04a10-244c-47b0-abf0-20d6e35d875e · outbound

This paper cites Learning to gener- alize: Meta-learning for domain generalization,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Learning to gener- alize: Meta-learning for domain generalization,

Reference 40

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2f123251-e70e-4a6a-ad7c-a844cd3f6978 · outbound

This paper cites Domain generalization with adversarial feature learning,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Domain generalization with adversarial feature learning,

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4ada9ba5-5ae1-4e75-bb2d-cf74fcd71f25 · outbound

This paper cites Domain generalization via model-agnostic learning of semantic features,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Domain generalization via model-agnostic learning of semantic features,

Reference 42

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c54b6da8-9ad2-427d-b70d-dcf310020087 · outbound

This paper cites Domain generalization via multidomain discriminant analysis,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Domain generalization via multidomain discriminant analysis,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:51.458021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f2fd0c35-95a6-4976-84a7-915abaa7d82e · outbound

This paper cites Efficient domain gener- alization via common-specific low-rank decomposition,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Efficient domain gener- alization via common-specific low-rank decomposition,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:51.247571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4bc74d27-12c5-4d7c-8a67-891adb5607b4 · outbound

This paper cites Learning to op- timize domain specific normalization for domain generalization,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Learning to op- timize domain specific normalization for domain generalization,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:51.080361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 49d1a1b3-9732-4be5-b7fb-ab277aeedc9e · outbound

This paper cites Domain generalization by solving jigsaw puzzles,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Domain generalization by solving jigsaw puzzles,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:50.934130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0b391a25-fedf-40c4-b0af-7d836fcd27ed · outbound

This paper cites Generalizing Across Domains via Cross-Gradient Training.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Generalizing Across Domains via Cross-Gradient Training

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T19:39:44.364610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:39:44.364610Z digest=sha256:44bc04e63e133503c18e2052a1651eb4dde5a8e359f8b1837288ce82311dc1c5

Observation acaeab69-d8a8-4ba0-be08-485ec20d0c1c · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Generalizing to unseen domains via adversarial data augmentation,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:50.742615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:44.508932Z digest=sha256:f2a7b6bdcdf0cd17ec3cd1adb9b4302b8a63b7a82e6af95448ac3ea8f4a7b49c

Observation 18c94653-38d1-4bd0-aa0c-8003821099df · outbound

This paper cites Episodic training for domain generalization,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Episodic training for domain generalization,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:50.469301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b910051e-54ff-4348-ac93-0a02f1a7640a · outbound

This paper cites Invariant Risk Minimization.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Invariant Risk Minimization

Reference 50

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:39:44.682478Z digest=sha256:3e260b4b5f38df042c17ec3972d4e2ed497b61aceabb7666952e2182f0dc8155

Observation 7b6262af-8492-4b4d-a689-b724ec2f686b · outbound

This paper cites Stable prediction with model misspecification and agnostic distribution shift,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Stable prediction with model misspecification and agnostic distribution shift,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:50.290035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:44.763871Z digest=sha256:7f8a0e0f2b4fc852a50586c3a2dcfa56e1716e4dc3aa3ab3a84bf9039c7f177d

Observation 0235fd4e-0e84-45d2-af0e-2306325c1f54 · outbound

This paper cites Stable learning via sample reweighting,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Stable learning via sample reweighting,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:50.136391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 814da830-cfba-4866-89ef-0d59a9e8b388 · outbound

This paper cites A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Reference 53

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c0441bad-1bae-4e3d-80a3-986e9cb7130a · outbound

This paper cites Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective

Reference 54

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 56fbcf61-4b56-47e8-be5a-8ef9a9c31e6a · outbound

This paper cites Sound and complete forward and backward chainings of graph rules,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Sound and complete forward and backward chainings of graph rules,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.984320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.026172Z digest=sha256:aa043b0e5089c549959fcfd190150151b0f8e2d53fdc5678ee9a2406bace4d75

Observation 8d7fd6de-f94e-410c-8968-f5c6c3a1741b · outbound

This paper cites Amie: association rule mining under incomplete evidence in ontological knowledge bases,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Amie: association rule mining under incomplete evidence in ontological knowledge bases,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.817566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 23bd9473-2de0-40fa-8d32-ed8cab1a5923 · outbound

This paper cites Robust covariate shift regression,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Robust covariate shift regression,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.651950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 327c6e0f-e4af-4a38-8abc-1a6533074e5b · outbound

This paper cites Learning under nonstationarity: covariate shift and class-balance change,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Learning under nonstationarity: covariate shift and class-balance change,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.459716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.220350Z digest=sha256:1f76960833f9cd3e0e55d57cdb05b90ddc1970d236fff75b79cb7d9e07b6b331

Observation 30d62e01-715c-471a-ba67-d3498bcbf010 · outbound

This paper cites A theoretical anal- ysis on independence-driven importance weighting for covariate- shift generalization,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift A theoretical anal- ysis on independence-driven importance weighting for covariate- shift generalization,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.317976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 95eed147-61c4-40d7-a767-1db42c6da750 · outbound

This paper cites An empirical study on robustness to spurious correlations using pre-trained language models,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift An empirical study on robustness to spurious correlations using pre-trained language models,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:49.135317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 7fb754a4-f9ba-40ff-882a-fdef33efb373 · outbound

This paper cites When does e (xk· yl)= e (xk)· e (yl) imply independence?.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift When does e (xk· yl)= e (xk)· e (yl) imply independence?

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.929508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.411112Z digest=sha256:5bc8d734b9aff2fcfba1f69f72dd41ccc2823344fee2dcd73e1ebd3799375c67

Observation 1e22d6e5-8340-400a-8022-466a3ac89e9b · outbound

This paper cites Approximate residual balancing: debiased inference of average treatment effects in high dimensions,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Approximate residual balancing: debiased inference of average treatment effects in high dimensions,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.754232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.476996Z digest=sha256:322846bf15f9dc21c20753153bec69584f9e08e324c2b5b08f0850b1a9323309

Observation c043b3c1-0c24-4fe1-92b6-0dfb09dd0f56 · outbound

This paper cites Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.565042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.546990Z digest=sha256:6ae378f83ed59fc2ed45b8e86900d4cfa7a21de804ff98aeb7ad2b872492c5f5

Observation e76471af-6a10-48ac-a118-6b63758c6256 · outbound

This paper cites Hollander, D.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Hollander, D

Reference 64

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:39:45.617092Z digest=sha256:8e29cf2f7d229a44eedc5cacb0b97416bc98a9ea3edf6fda509e2b251e0363b8

Observation 66404a46-aeab-4fa7-a4e2-565cad9a11e5 · outbound

This paper cites Spitzer, Principles of random walk.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Spitzer, Principles of random walk

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.373247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.717446Z digest=sha256:619733e9d2eaa947cf17fad5c9fdb1e3b42bb2d33c1cd59b5296cd9acd48ce1d

Observation 3a31785a-32b4-4a0e-b246-2547e3e79fe1 · outbound

This paper cites Learning distributed representations of con- cepts,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Learning distributed representations of con- cepts,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.179055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.798382Z digest=sha256:a2374042fb66de7d29885080394f8bb327e62d413da64fe77cf3d996f5774bd5

Observation 57aeb332-b1a7-43db-a857-36c256802ba2 · outbound

This paper cites Statistical predicate invention,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Statistical predicate invention,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:48.009884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:45.908351Z digest=sha256:19d8f6496b72912f2bcbbe98d54c496e3af680f6fd020b26edbf577b88227a68

Observation 739d4d69-6e43-4bd7-926a-98dd9b8e5066 · outbound

This paper cites Convo- lutional 2d knowledge graph embeddings,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Convo- lutional 2d knowledge graph embeddings,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:47.815070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.025350Z digest=sha256:7ff9646b37d07944e35d4b24ef98ac6f36ce29037fbeada8c1219a804650d953

Observation eef193c0-103b-4132-bf76-0b07e28dd8a7 · outbound

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

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Observed versus latent features for knowledge base and text inference,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:47.570461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.111617Z digest=sha256:2cc289ea9fff9caa0a968158ceb3327acffe494192c7a266c699437ef486521f

Observation e767d618-fe5d-4205-9264-4a14530cae85 · outbound

This paper cites Arnetminer: extraction and mining of academic social networks,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Arnetminer: extraction and mining of academic social networks,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:47.393724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.190740Z digest=sha256:8c3206c00cc03ec868167d269c00679754c7fbdee9694f2f87e03c88091d6577

Observation ddbe615e-ce41-44b2-8b80-e055de93010a · outbound

This paper cites Geotext: an intelligent dynamic geometry textbook,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Geotext: an intelligent dynamic geometry textbook,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:47.201143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.267949Z digest=sha256:e47bfb0f3c34b517912a4a26927b25bcd681a674d212dbddccf7455f0667316a

Observation f1be99e8-8c73-4fd7-b568-7ade5f8ac4f9 · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Pytorch: An im- perative style, high-performance deep learning library,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:47.071276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.334042Z digest=sha256:3ba453c3ef4afa263b764587b447b8538891079554f7f54a971b9735691c45cb

Observation bf32587e-590d-41d7-ab70-2f84f803ba2a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Adam: A Method for Stochastic Optimization

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T19:39:46.431360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:39:46.431360Z digest=sha256:d44848dfcb7f67b5c42bca6ef017d9c3d0f7cf89b705d60f92bfae59df7c6a9b

Observation b6c95a57-11dc-4f38-923d-d40adde9dee4 · outbound

This paper cites Heterogeneous information networks: the past, the present, and the future,.

Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift Heterogeneous information networks: the past, the present, and the future,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:39:46.922795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:39:46.493578Z digest=sha256:4e63b80986d2b74a360ae6112aec381e762cbc57ba4b6dd07e44e58ff443f22b

Pith citing papers

No inbound Pith citation observations are available.