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Paper Citation Record · LEDGER

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering

As of 7 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2506.09645.

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

pith.paper-citation-record.v1
2506.09645 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:47:40.429016Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:35:49.207472Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:06:26.153057Z

Reference resolution

70 of 70 outbound references displayed

  • verified exact0
  • verified fuzzy28
  • unresolved42
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b60b0f9-7b27-411b-ae0c-e5daefc97c73 · outbound

This paper cites GPT-4 Technical Report.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation dc76a1ee-eef8-46bb-b9f0-e2010a6a24b0 · outbound

This paper cites Semantic parsing on freebase from question-answer pairs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Semantic parsing on freebase from question-answer pairs

Reference 2

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source=pdf_text observed=2026-08-07T04:47:35.562957Z digest=sha256:0f10cb728e532df64ce743441315d110a39d3da941ba01d9e06e324b6ba7aa24

Observation 28080855-5c2d-449d-88ab-a365843f1703 · outbound

This paper cites Freebase: a collaboratively created graph database for structuring human knowledge.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Freebase: a collaboratively created graph database for structuring human knowledge

Reference 3

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source=pdf_text observed=2026-08-07T04:47:35.626404Z digest=sha256:bef78e42f572045ec7a4dba69c9aa649b591b0a631eb0f4a82f55f6a5b94b24e

Observation add3c118-c2d1-4643-9d8a-ec44a67bd922 · outbound

This paper cites Language models are few-shot learners.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Language models are few-shot learners

Reference 4

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source=pdf_text observed=2026-08-07T04:47:35.675026Z digest=sha256:0d07cdd994ed20ae686bb0792cc0bf689b434dce00fda8a186ffdafae863b639

Observation 680c345c-f67e-4f2a-abfa-0092744f2d03 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

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source=pdf_text observed=2026-08-07T04:47:35.720901Z digest=sha256:348ee66328cb5a2f010beb6bab60b3f894ad8fb461a53486734b91ece946042f

Observation 6a15196b-26f1-42f7-8fdc-5b2bc0ed08ed · outbound

This paper cites Springer Science & Business Media, 2008.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Springer Science & Business Media, 2008

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.233744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:35.761632Z digest=sha256:f865943bc3e02a0d5281d82b923171a607f0c59f04eecfafd4046cec65ced12d

Observation 8bb14fe6-fe07-4925-9c4e-0de11bd8ffd2 · outbound

This paper cites Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

Reference 7

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source=pdf_text observed=2026-08-07T04:47:35.794225Z digest=sha256:a27738ec5be218aa5c156cf19177df7ef9e328700489abcfc1b141620639ddd3

Observation 83f98206-911a-4cc9-a6fe-1baf6a50941e · outbound

This paper cites EWEK-QA : Enhanced web and efficient knowledge graph retrieval for citation-based question answering systems.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering EWEK-QA : Enhanced web and efficient knowledge graph retrieval for citation-based question answering systems

Reference 8

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raw_fallback, observed 2026-08-07T04:47:41.223211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:35.818932Z digest=sha256:f3ad1b400632e0a8ba71111d1bd24d1667a8d3b14f1f044da514ef6f9db87ffc

Observation 696d674e-afaa-4c44-a338-b84f3db386be · outbound

This paper cites HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics

Reference 9

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:47:35.900857Z digest=sha256:83ada58cf2a531b55b001ef8a3ec096f26ff89ca64c2728d203effcbe61757a5

Observation 84bf3b83-011f-414b-8f8a-b3bb52a083b6 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Fast Graph Representation Learning with PyTorch Geometric

Reference 10

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source=pdf_text observed=2026-08-07T04:47:35.961960Z digest=sha256:330bae5a52e4c6cc2b808ecf39e520eb66843f143be3abbdbd85c79eaf4a9fd5

Observation 2e504b99-2af7-4fb9-9937-15b8e5a6bc03 · outbound

This paper cites Towards Foundation Models for Knowledge Graph Reasoning.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Towards Foundation Models for Knowledge Graph Reasoning

Reference 11

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source=pdf_text observed=2026-08-07T04:47:36.017211Z digest=sha256:02e54566afd4261822a59abe472de1867d486f451542a661c11878f7f2907a07

Observation 14e9c7be-2a72-4b2d-a555-2a288d1bec29 · outbound

This paper cites Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types

Reference 12

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source=pdf_text observed=2026-08-07T04:47:36.115558Z digest=sha256:69de1d261878a25a800627fd0eee56c8ec1746cab9a89148ea8946c0206b680c

Observation b02ba82e-1a16-403a-8c94-54a7aec3dddf · outbound

This paper cites Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

Reference 13

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:47:36.176716Z digest=sha256:0346d6fa4114f083e5b268371311dc7feb6aec7d188a6bdb5d34062f601bd829

Observation b3080ddd-96ad-4e88-96f5-54bf4d932816 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:36.267679Z digest=sha256:7e73a23688666b2ce26b90376fce4eaad9ce414a873b70c5769885627d61819e

Observation 60435a6d-03ad-4e17-8bcd-c6738d72e2ed · outbound

This paper cites Rela- tional message passing for fully inductive knowledge graph completion.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Rela- tional message passing for fully inductive knowledge graph completion

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.211642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:36.324084Z digest=sha256:e8b65767fd906c46bc302a19a9c5e60829bf98a0fb506d93031f71937ba04f1d

Observation b3090267-438c-43c5-9817-66a22fac3270 · outbound

This paper cites Exploring network structure, dynamics, and function using networkx.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Exploring network structure, dynamics, and function using networkx

Reference 16

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source=pdf_text observed=2026-08-07T04:47:36.420767Z digest=sha256:6366a807c3179b8dea3fbee1055b71deaddb13cd6a654f24f086e75cc7660544

Observation 7a7fd096-0af0-4fb0-b4f8-de98bb3fe960 · outbound

This paper cites Inductive representation learning on large graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Inductive representation learning on large graphs

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:36.470357Z digest=sha256:312e95b690ea791b520342f6a5cd20832bc3349b1ff23f0e502dc60b8a014b5a

Observation 5181610a-b387-47db-84bc-e212cbe4b65f · outbound

This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024

Reference 18

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source=pdf_text observed=2026-08-07T04:47:36.580762Z digest=sha256:09ce04e47502f0d56bc3b56a210067ba7a140f9554c48f6029442e1cf4585daa

Observation 657094fa-72c3-41cd-af46-29b58b16db0f · outbound

This paper cites Knowledge graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Knowledge graphs

Reference 19

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source=pdf_text observed=2026-08-07T04:47:36.629133Z digest=sha256:5329bd3845c862c5c27a9c867b1e7a71f78e71a8246b8865b541dbb75f42d9b3

Observation 0e971bff-4b9b-4c5c-ba35-c5e5e0ec0384 · outbound

This paper cites Towards reasoning in large language models: A survey.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Towards reasoning in large language models: A survey

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.174449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:36.687834Z digest=sha256:7510ead63ac7be0c08e3808a24b4fba2853058220e32213d384b547a4717715f

Observation 2f0f03c4-991b-4237-a49f-d908954ddb13 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 21

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source=pdf_text observed=2026-08-07T04:47:36.770322Z digest=sha256:55de80a7adb9d5433a0f0a2765a9d41ce192caaab35af61d2cb38022fdf8a275

Observation d22116a6-75aa-4ad8-871a-3f2cb06818b2 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 22

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source=pdf_text observed=2026-08-07T04:47:36.839879Z digest=sha256:eb3499a05c362c19f99817bb5afcb3f7bfc0e6499eff123cbaef887e0c62c668

Observation 2f0ec49d-10ee-40cf-97df-55b7ee95dd32 · outbound

This paper cites Survey of hallucination in natural language generation.ACM Computing Surveys, 55(12), 2023.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Survey of hallucination in natural language generation.ACM Computing Surveys, 55(12), 2023

Reference 23

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source=pdf_text observed=2026-08-07T04:47:36.908428Z digest=sha256:bd19ff5be1e17016663e7158fd5573c3e6024619993023b4628aad0b02406764

Observation 814650eb-78cb-4a58-9e8c-50f253b7efcf · outbound

This paper cites Structgpt: A general framework for large language model to reason over structured data.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Structgpt: A general framework for large language model to reason over structured data

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.149443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:36.964423Z digest=sha256:bb0bb318c1212270ff58ba2e8d8ba815486bf1512b314b92f773bec3495d48f3

Observation 58aba20a-676a-4785-a2c5-51308776fa3e · outbound

This paper cites KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph

Reference 25

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source=pdf_text observed=2026-08-07T04:47:37.028487Z digest=sha256:51d5e723ff8a790db48b31e6c2742052b4722a092e9e33014277a452d3131eaa

Observation 00f4ebb6-c97f-4b38-8179-9b96929f4969 · outbound

This paper cites Unikgqa: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Unikgqa: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.138520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.092663Z digest=sha256:dc5c9b009d64f9b7654aadae1ea7a54e1ee9c7ba4afe49720afc6cca8cb94f68

Observation e66e7667-9ad3-455a-8e30-50cffcc77095 · outbound

This paper cites Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.174141Z digest=sha256:4832f03e03711a50be80a3a24faf04506b3632879e2e81b00314ef893f0be61f

Observation ac24dc96-bcc9-4128-a7f9-9136e2d4cd59 · outbound

This paper cites Smith, Yejin Choi, and Kentaro Inui.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Smith, Yejin Choi, and Kentaro Inui

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.128715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.247319Z digest=sha256:bfb3c087a226b964bcae038f7f0a23f0702f07b984e63b175388d42234463415

Observation 308e4f23-1db1-49b1-bf6e-b71e6be288d3 · outbound

This paper cites KG-GPT: A general framework for reasoning on knowledge graphs using large language models.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering KG-GPT: A general framework for reasoning on knowledge graphs using large language models

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.116803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.302694Z digest=sha256:16b12b609dd8f9a90e02882d9158a9b97329e3cf35e13eb0475ca3059bf2eccc

Observation a750e4ad-3d8d-4d3e-987d-c8055f873e4a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Adam: A Method for Stochastic Optimization

Reference 30

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source=pdf_text observed=2026-08-07T04:47:37.354968Z digest=sha256:c660a85838a0324028568c8c09b6849c3940d8eb6b42cbf33379895fbb7be7e3

Observation 7baff500-c81c-4aa7-aafc-31c624088a1c · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Semi-Supervised Classification with Graph Convolutional Networks

Reference 31

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source=pdf_text observed=2026-08-07T04:47:37.399355Z digest=sha256:b5ec8a6b3808408dfd56dcd9f1f3c95830e86f51b3e608709afedcd374e5c27f

Observation 54872069-8d31-40c5-ba5c-665e1974d6df · outbound

This paper cites Ingram: Inductive knowledge graph embedding via relation graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Ingram: Inductive knowledge graph embedding via relation graphs

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.105979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.444395Z digest=sha256:c0f9a399a164f18ff890dc5c251ed0d3624273b45f15e68b2de6865aaee4e029

Observation 03af9d0a-7014-4ae6-b89d-9f36c2c30804 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.530892Z digest=sha256:a8652fa63f855626364ccd5f45cfec863eb5f7c3ac6530b69eccdc97592bf05b

Observation 8542ce97-933d-42d5-8941-52302f466351 · outbound

This paper cites Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.567324Z digest=sha256:983d1165f463b6575ec6d37ad96ae14d1a884559d5294709b974dbf9f8a27bfd

Observation 05fea252-2a91-4388-a488-079ccbeb8aa2 · outbound

This paper cites Distance encoding: Design provably more powerful neural networks for graph representation learning.Advances in Neural Information Processing Systems, 33:4465–4478, 2020.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Distance encoding: Design provably more powerful neural networks for graph representation learning.Advances in Neural Information Processing Systems, 33:4465–4478, 2020

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.089143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.627052Z digest=sha256:45066699732938fd4ce338c70d0bb5a6350e9fbc3c0c27d94729d5ccfe67cae0

Observation b1524cd9-3362-45ec-9a64-cb34902f4257 · outbound

This paper cites Graph reasoning for question answering with triplet retrieval.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Graph reasoning for question answering with triplet retrieval

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.078918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.682852Z digest=sha256:08dd2846cd042bdbe9bc46a3fe106c9d841e1947145df7f3efc00ae70ac48e36

Observation 78483f80-88b2-42a0-a649-9758f2f054c1 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 37

Resolution
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no resolver link, observed 2026-08-07T04:47:37.719430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.719430Z digest=sha256:e9d3693a2f046b637751478eee82cb4b65003bb5da581b51509b3ff63f1203e4

Observation 0b8bfeaf-185f-41d6-869d-3fc239f650d2 · outbound

This paper cites Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.757564Z digest=sha256:70ae5722885e3506d91bbc9248fbf7959971fd12767004fde34662a0e0e911bf

Observation a8487c9b-58da-4910-a855-a6b710b74c4b · outbound

This paper cites Dual reasoning: A gnn-llm collaborative framework for knowledge graph question answering, 2025.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Dual reasoning: A gnn-llm collaborative framework for knowledge graph question answering, 2025

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.068430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.834534Z digest=sha256:75c54a85d9e7acf91dde1ee892e5a5906c0900faf3892c1da226053ccf23f959

Observation 5f546da4-17aa-4019-ae9e-017d78aec195 · outbound

This paper cites Reasoning on graphs: Faithful and interpretable large language model reasoning.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Reasoning on graphs: Faithful and interpretable large language model reasoning

Reference 40

Resolution
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no resolver link, observed 2026-08-07T04:47:37.902338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:37.902338Z digest=sha256:786e5ef3ebf7c5213b3f08d75adac96760c12fe2292a17d69259338746b00d47

Observation 9e8d211e-c3e1-441d-b8af-6102d0a87968 · outbound

This paper cites Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval.arXiv e-prints, pages arXiv–2407, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval.arXiv e-prints, pages arXiv–2407, 2024

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.052356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:37.974407Z digest=sha256:99afa7afd6d2ab2824356e8db853a610b5fdcecdc14e3dfe9e7304fac4299e83

Observation 90775d33-dd47-4f9d-9b23-b7b07ee0f784 · outbound

This paper cites Automated social science: Language models as scientist and subjects.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Automated social science: Language models as scientist and subjects

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.023777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.023777Z digest=sha256:36d63b6705dbd719023b838dc6250f0ff8506498049d2b0f675f13b312b3f06e

Observation b6c9d1b5-aeb5-42a6-8fa2-4c14e20aa59d · outbound

This paper cites Rearev: Adaptive reasoning for question answering over knowledge graphs.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Rearev: Adaptive reasoning for question answering over knowledge graphs

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.034355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.106561Z digest=sha256:35b3093689b7a968d7f07cf58ae23fdc97404a7323f3c3068364ca50b30d9d44

Observation 6a6b8ed1-b82d-45e4-82a5-f078aa5553cc · outbound

This paper cites GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Reference 44

Resolution
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no resolver link, observed 2026-08-07T04:47:38.174391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.174391Z digest=sha256:9b55a3fc35bc3169173af5adb0b3c06cfcd1991893fe38e46e1b0aee3c13b80f

Observation cd52d773-5628-4b3a-b793-4ed06891fdfe · outbound

This paper cites Build the future of ai with meta llama 3, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Build the future of ai with meta llama 3, 2024

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.023784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.226132Z digest=sha256:fa9a24df174f49ddf0bc2acbee69ad8e98af8128587f9c4d3923a0f60ab9ba95

Observation 59b96e11-5e18-4a1f-b930-dd18df9e4b98 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Efficient Estimation of Word Representations in Vector Space

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.294523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.294523Z digest=sha256:1f44fe087cb46d869e8b21e6dbe579d729ec0cb17e85709e7c98fe1ed175c79e

Observation 48866ae5-4633-4ddb-9d3f-2048c29ee9c9 · outbound

This paper cites Introducing chatgpt, 2022.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Introducing chatgpt, 2022

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:41.013626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.354887Z digest=sha256:65beae440f194d3ed22e8c89e4028e286b1fea6d77643233fbcaa4fdf24c0d58

Observation c7e3a015-d613-4303-a41b-dd05586bd6e7 · outbound

This paper cites Hello gpt-4o, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Hello gpt-4o, 2024

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.407792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.407792Z digest=sha256:75eb54b0f42a5c56a11131e333312e9aa53c887bf6229501d61c21e168ac284c

Observation 9be7f244-d084-41c8-890b-df29cc419106 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.476718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.476718Z digest=sha256:605c8dbb0d105aca60f22fade953a31c9260dbb1d4e677ad4399224f8741719e

Observation bd18f6f3-c8b2-4edc-9c49-1e1a477ecef5 · outbound

This paper cites O’Reilly Media, Inc.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering O’Reilly Media, Inc

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.991049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.513260Z digest=sha256:93d539b68b26d673adfd04bc9f9b93cb77035db1920597ee6a11b42a50b8cee8

Observation ac51d714-839d-4423-9012-14a5813f6419 · outbound

This paper cites Retrieval augmentation reduces hallucination in conversation.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Retrieval augmentation reduces hallucination in conversation

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.979838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.562263Z digest=sha256:140d7884ef38eae93efdf4d46a83f6e9475e7c47780306a9f1c4e7aa36f43e90

Observation 8dc5cdf7-85c9-41a2-8f26-f3ee101d2dee · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

Reference 52

Resolution
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no resolver link, observed 2026-08-07T04:47:38.630413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.630413Z digest=sha256:e8dd328652fec9758b247f855c424b4449b1e9fb2e9a291d134d01a3c9dae376

Observation 02901886-9147-4606-b60d-f0c075a6dfa8 · outbound

This paper cites Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.962486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.689683Z digest=sha256:4405519d38086de0b0ca613c8f38f10fdba4e341b7a501295db5ee61ca2cafb8

Observation e004cb1e-5fb5-44e6-96ac-056696b01d77 · outbound

This paper cites The web as a knowledge-base for answering complex questions.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering The web as a knowledge-base for answering complex questions

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.951943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:38.759559Z digest=sha256:27926ff9615642183d8f827f11ad005ad2b117bfa2d606a62dbc29c3c51cb79c

Observation 0c8449b2-26e0-4756-aa9f-2c279be8634e · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.793227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.793227Z digest=sha256:72507f205ed91eecc3ae670d3152a37dd4b4f189a16d7c51dd56d39d359888d8

Observation 11c09014-839f-430b-98c1-eac2bc1f3b84 · outbound

This paper cites Graph Attention Networks.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Graph Attention Networks

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.850519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.850519Z digest=sha256:15483a969472eaf191e8e948cdff632394a1923c06b52cec8f867dc5cacf785c

Observation cc124f8c-8d79-4762-8468-2b9b1d29e030 · outbound

This paper cites Graph attention networks.stat, 1050(20):10–48550, 2017.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Graph attention networks.stat, 1050(20):10–48550, 2017

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:38.924630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:38.924630Z digest=sha256:dd33a32a4f4a65865f698daf2d21cdc1c8e8088a194eabde18583b736848fcf7

Observation aecae7b9-be3a-47a6-8bbe-57cd60633558 · outbound

This paper cites Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:39.004807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:39.004807Z digest=sha256:51924b57bc22c8091f948440f42e1e41baf30689bac55c15f35990ff98e00539

Observation 87abafd6-a0d9-42c2-b44d-1a542c426d42 · outbound

This paper cites Knowledge graph prompting for multi-document question answering.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Knowledge graph prompting for multi-document question answering

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.935333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:39.118607Z digest=sha256:a48c9a2f1e2d2763d64bcbdb2d97e5d0c3e94357080217bbc6bf7a875a9ef172

Observation 94b044d8-65e3-4c2c-93a8-2fe146060367 · outbound

This paper cites Chi, Quoc V Le, and Denny Zhou.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Chi, Quoc V Le, and Denny Zhou

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.925781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:39.250700Z digest=sha256:667aa1d1ffa51a71c64488e57917704c97c48712130117882981ae353097458c

Observation 35d93351-ee93-438e-b484-dea0c61bee54 · outbound

This paper cites MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:39.345994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:39.345994Z digest=sha256:1cd56f99d9f304792c3be34e41067928084ad5148319a0bf2880ae7042f2d5d7

Observation 41a7c75b-4527-43a9-b6ad-25a2121ae32b · outbound

This paper cites A survey on large language models for recommendation.World Wide Web, 27(5):60, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering A survey on large language models for recommendation.World Wide Web, 27(5):60, 2024

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.915854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:39.435851Z digest=sha256:ecf165105e3efc4ac15d6f258a44cafe8ec47d311ee12e410d92ed6e3169615d

Observation ba57b682-8da1-4d02-b952-19fcd31e2a16 · outbound

This paper cites Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering, 2023.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering, 2023

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.906714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:39.614841Z digest=sha256:e4635aac0426de4890ef51b7df8a56c0cab1111e66d0a5a0fa840f0c9697de92

Observation 37aafdf4-ff76-4d13-8d0c-3f52feb8b494 · outbound

This paper cites How powerful are graph neural networks? InInternational Conference on Learning Representations, 2019.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering How powerful are graph neural networks? InInternational Conference on Learning Representations, 2019

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:39.715576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:39.715576Z digest=sha256:2d4f7b7d5f1a5ffb55316b9ea1d01acb8058aa6d9808bd55283d4f3e50c5bce4

Observation b4614909-b869-4f82-82e3-514f981458f4 · outbound

This paper cites Qwen2 Technical Report.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Qwen2 Technical Report

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:39.818522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:39.818522Z digest=sha256:cc190ff21260a28b47278378ac594d79cf75fdf75b874512303af6489ffbb7c6

Observation 835a9359-650b-4fc6-bcee-ca18a88750f0 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36, 2024.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36, 2024

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.891139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:39.987320Z digest=sha256:adea71fd12b88359f821d11cc587930f0ece7e877c17fe8a83867416876f1e96

Observation 3a85d529-0906-4a42-840f-67c48160cf37 · outbound

This paper cites The value of semantic parse labeling for knowledge base question answering.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering The value of semantic parse labeling for knowledge base question answering

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.881183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:40.097440Z digest=sha256:dd2e92be31790f738a1aaf406fc70c7ec9024a258e1c5c0389d4f45ae2f4c9b4

Observation 7c8e05b6-ba32-49e1-a427-edc98711ca4a · outbound

This paper cites Subgraph retrieval enhanced model for multi-hop knowledge base question answering.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Subgraph retrieval enhanced model for multi-hop knowledge base question answering

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:47:40.823166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:47:40.225987Z digest=sha256:f93be32c3e80f66db4cd45cd51e0c07370ada98da1fb3a3a20e3f3c953b3b099

Observation c0e7ccde-1205-4b1f-a6a9-cc049a1f1bd6 · outbound

This paper cites Labeling trick: A theory of using graph neural networks for multi-node representation learning.Advances in Neural Information Processing Systems, 34:9061–9073, 2021.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering Labeling trick: A theory of using graph neural networks for multi-node representation learning.Advances in Neural Information Processing Systems, 34:9061–9073, 2021

Reference 69

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no resolver link, observed 2026-08-07T04:47:40.327799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:40.327799Z digest=sha256:eb4b810116ed28c55d0ff2b12e135902753ddece146a2655dc66ecf1f3d51839

Observation d646f850-f168-4085-9803-f0ed851c6c27 · outbound

This paper cites A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes.

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes

Reference 70

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no resolver link, observed 2026-08-07T04:47:40.429016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:40.429016Z digest=sha256:906aa28ae5aef518d0e5b42189b24ae22a547e05d2def8287d6b425bf8ee4983

Pith citing papers

Observation 5987d127-5c6d-424c-a47f-834f4bdf4e1d · inbound

TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering cites this paper.

TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:51:03.038886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:17:59.538800Z digest=sha256:1580be0899760a5bef4b09e19143c3553f4666bf5e6513668f3a301693cf0d86

Observation 7ec901b1-96bf-48c5-bdc0-dc02a9199505 · inbound

PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering cites this paper.

PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:06:26.161434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T04:35:49.207472Z digest=sha256:6c371d3b86786a112878ceb15989326b0b39bd64b69ec0ccc94aa3f7b0d4ca9b