Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution | Paper Citation Record

Observation 686ca77e-156d-4729-b7ea-7ac54f16e264 · outbound

This paper cites Gazetteer matching for natural features in switzerland.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Gazetteer matching for natural features in switzerland

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.911166Z

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Observation caae631f-312f-4a6a-bb2f-eb0ab6d7a9ca · outbound

This paper cites Machine learning for cross-gazetteer matching of natural features.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Machine learning for cross-gazetteer matching of natural features

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.905204Z

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Observation 372a9f1e-19ff-4500-9b4f-c2d27656974d · outbound

This paper cites GPT-4 Technical Report.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution GPT-4 Technical Report

Reference 3

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Observation 8cef86cf-2b22-47f9-8338-07b76fb59357 · outbound

This paper cites Assessment of the accuracy of geonames gazetteer data.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Assessment of the accuracy of geonames gazetteer data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.899350Z

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Observation 263d8d37-847b-4009-b46d-4f96a0851941 · outbound

This paper cites Geospatial entity resolution.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Geospatial entity resolution

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.893707Z

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Observation b769f0e2-f8c5-476c-8556-39a50c10f4e9 · outbound

This paper cites Mining geospatial relationships from text.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Mining geospatial relationships from text

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.888120Z

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Observation c10a9a27-0e62-4846-849a-0a9756f2096a · outbound

This paper cites Enriching word vectors with subword information.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Enriching word vectors with subword information

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.882195Z

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Observation efab962d-cd64-43c4-9363-cb1b0e62822a · outbound

This paper cites Geometric deep learning: going beyond euclidean data.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Geometric deep learning: going beyond euclidean data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.876396Z

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Observation 60f78beb-13a2-4371-a00e-9b353412ad53 · outbound

This paper cites and Stockinger, K., 2020.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution and Stockinger, K., 2020

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.870692Z

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Observation c9b8ba27-91a4-4fa0-b10f-575a605eb28c · outbound

This paper cites Palm: Scaling language modeling with pathways.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Palm: Scaling language modeling with pathways

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.865141Z

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Observation 06c1bd50-47f7-48a8-9b09-4f2c03fd89ab · outbound

This paper cites An overview of end-to-end entity resolution for big data.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution An overview of end-to-end entity resolution for big data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.859409Z

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Observation f6475533-1181-4d11-ade5-e3f929a21a67 · outbound

This paper cites Geo-aware networks for fine-grained recognition.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Geo-aware networks for fine-grained recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.853430Z

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Observation 00ad1c60-5a0b-4044-9da8-180f30808c32 · outbound

This paper cites and Barbosa, L., 2021.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution and Barbosa, L., 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.847599Z

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Observation 9c2e012a-bbec-4b14-9e09-c9ae87e8ffda · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Qlora: Efficient finetuning of quantized llms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.841839Z

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Observation 6f35579e-0235-43f4-8ac1-69de104fe252 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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Observation fe69252b-fa8b-4203-902b-5226afb83f87 · outbound

This paper cites A survey on in-context learning.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A survey on in-context learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.836192Z

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Observation 54ee023d-b1e6-4f04-a73c-f8aa4e9c50bf · outbound

This paper cites and Peucker, T.K., 1973.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution and Peucker, T.K., 1973

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.830473Z

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Observation 4f2416b1-ca78-4646-ae1b-fb1bad4a6d72 · outbound

This paper cites Cost-effective in-context learning for entity resolution: A design space exploration.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Cost-effective in-context learning for entity resolution: A design space exploration

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.824386Z

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Observation 4b260dd7-fb2e-4194-b211-ef5999f911b3 · outbound

This paper cites Constructing gazetteers from volunteered big geo-data based on hadoop.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Constructing gazetteers from volunteered big geo-data based on hadoop

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.818133Z

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Observation de205c46-6c91-4d87-b944-7c39347e8f1b · outbound

This paper cites Automated conflation of digital gazetteer data.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Automated conflation of digital gazetteer data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.811758Z

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Observation 3b66a98e-2c9e-43ee-8ce2-f99030d87396 · outbound

This paper cites Deep residual learning for image recognition.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Deep residual learning for image recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.805803Z

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Observation 2c5ebcab-9ecb-442e-9b17-58a23221732f · outbound

This paper cites and Papadakis, G., 2024.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution and Papadakis, G., 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.799739Z

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Observation a689256f-e33b-452d-b5f0-374f50d2f113 · outbound

This paper cites Large language models are zero-shot reasoners.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Large language models are zero-shot reasoners

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.793960Z

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Observation 80a4b2e7-c1d6-424d-966b-e5df6b746951 · outbound

This paper cites Evaluation of entity resolution approaches on real-world match problems.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Evaluation of entity resolution approaches on real-world match problems

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:05.999700Z

Source-reported events for the cited work

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Observation ca487ec1-41ef-4cc5-ade0-049df4625e9b · outbound

This paper cites Geographic ontologies, gazetteers and multilingualism.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Geographic ontologies, gazetteers and multilingualism

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.788229Z

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Observation 1bffb3e8-b7ac-464a-9b0f-0e3c32d2303b · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:06.143371Z

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Observation c8aeaf50-38d9-42d8-a32f-137ede8ee583 · outbound

This paper cites Booster: Leveraging large language models for enhancing entity resolution.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Booster: Leveraging large language models for enhancing entity resolution

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.782738Z

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Observation 3e448863-038d-4dbb-a462-2f0181c8f51e · outbound

This paper cites Pointcnn: Convolution on x-transformed points.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Pointcnn: Convolution on x-transformed points

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.776775Z

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Observation 836f9773-25e9-44f3-9e70-085872721cda · outbound

This paper cites Deep entity matching with pre-trained language models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Deep entity matching with pre-trained language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.770983Z

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Observation 14c6769f-2286-404d-a47d-0d94a70b0cc8 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:06.598438Z

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Observation a84799a1-5314-41cb-958e-f7f9205a78e0 · outbound

This paper cites Presence-only geographical priors for fine-grained image classification.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Presence-only geographical priors for fine-grained image classification

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.764931Z

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Observation 44de9e16-95a8-4e4b-a04d-32a8c72cecf1 · outbound

This paper cites On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:06.785792Z

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Observation ae9006b1-3776-4afe-823c-f2674ca6a60f · outbound

This paper cites A review of location encoding for geoai: methods and applications.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A review of location encoding for geoai: methods and applications

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.758710Z

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Observation 8887bbdf-de1f-4f94-a11e-2e6a738c84be · outbound

This paper cites Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:06.902734Z

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Observation 77cd138b-dfe9-4038-bf51-dad8d963a5ff · outbound

This paper cites Towards general-purpose representation learning of polygonal geometries.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Towards general-purpose representation learning of polygonal geometries

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.753012Z

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Observation a6d9b1ed-b8d3-4af7-9a7a-abe78a26bffa · outbound

This paper cites A supervised machine learning approach for duplicate detection over gazetteer records.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A supervised machine learning approach for duplicate detection over gazetteer records

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.747181Z

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Observation dd22a4a2-4936-4f4d-b452-0ff2bc1110ac · outbound

This paper cites Weighted multi-attribute matching of user-generated points of interest.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Weighted multi-attribute matching of user-generated points of interest

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.740856Z

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Observation 9cad051f-9bdc-4d7d-ab9d-209a21755197 · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Distributed representations of words and phrases and their compositionality

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.735072Z

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Observation 0b2f6ae9-7523-4966-99aa-569cb2669182 · outbound

This paper cites Can foundation models wrangle your data? Proceedings of the VLDB Endowment, 16 (4), 738--746.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Can foundation models wrangle your data? Proceedings of the VLDB Endowment, 16 (4), 738--746

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.728766Z

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Observation 4dd33211-4e4b-4c8f-91d5-57b497d8bacf · outbound

This paper cites A multi-facet analysis of bert-based entity matching models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A multi-facet analysis of bert-based entity matching models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.722921Z

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Observation 241cae04-57bb-4221-911b-5202737c7636 · outbound

This paper cites and Bizer, C., 2021.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution and Bizer, C., 2021

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.716752Z

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Observation 0fc7f2d1-8055-47c8-a2f9-cc3b952511ee · outbound

This paper cites Entity Matching using Large Language Models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Entity Matching using Large Language Models

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:04:11.029413Z

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Observation 8073ac94-2ee4-4dd9-b656-3f71e1ba3043 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.709808Z

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Observation 11d74ad2-1215-44a8-9594-6751285dfcf0 · outbound

This paper cites A critical evaluation of location based services and their potential.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A critical evaluation of location based services and their potential

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.703809Z

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Observation 09e51b81-0287-488b-921b-f0ce00c239c5 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 45

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Source-reported events for the cited work

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Observation 1876e397-d669-4d30-b5fc-37e77ef990f7 · outbound

This paper cites Toponym matching through deep neural networks.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Toponym matching through deep neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.698150Z

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Observation 1949aa76-ad96-4473-a700-a77ebc4f1de7 · outbound

This paper cites Entity resolution in geospatial data integration.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Entity resolution in geospatial data integration

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.692111Z

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Observation 224fced6-eacb-4117-bd4c-8fdd4d70f69e · outbound

This paper cites Multi-source toponym data integration and mediation for a meta-gazetteer service.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Multi-source toponym data integration and mediation for a meta-gazetteer service

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.686267Z

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Observation 17042a2a-dc11-40b8-8b4b-934ee8bd6c81 · outbound

This paper cites Conflating point of interest (poi) data: A systematic review of matching methods.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Conflating point of interest (poi) data: A systematic review of matching methods

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.680095Z

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Observation c15f8425-df69-44c1-a89c-50f81b16be38 · outbound

This paper cites Improving image classification with location context.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Improving image classification with location context

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.673763Z

Source-reported events for the cited work

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Observation 94126b5b-f1b0-4c6a-930d-08af0fb170e2 · outbound

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

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:08.355631Z

Source-reported events for the cited work

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Observation e2ad4fb2-106d-499d-8444-93f7cd7ac462 · outbound

This paper cites Learning localized generative models for 3d point clouds via graph convolution.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Learning localized generative models for 3d point clouds via graph convolution

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.667535Z

Source-reported events for the cited work

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Observation 7f289529-8fd2-4f29-b641-89c8d02ce791 · outbound

This paper cites Attention is all you need.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Attention is all you need

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.661374Z

Source-reported events for the cited work

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Observation 36a2d1ec-ec55-4b6d-b7f4-f1d6ec03b5f9 · outbound

This paper cites Deep Learning for Classification Tasks on Geospatial Vector Polygons.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Deep Learning for Classification Tasks on Geospatial Vector Polygons

Reference 54

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Observation a77357ef-91c8-4891-b47d-39b0baa1de67 · outbound

This paper cites Crowder: crowdsourcing entity resolution.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Crowder: crowdsourcing entity resolution

Reference 55

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T23:04:10.617028Z

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Observation ec962908-f640-44c3-9b12-e07d5552fb60 · outbound

This paper cites Improving text embeddings with large language models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Improving text embeddings with large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.654882Z

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Observation 75de6f9d-ae7d-4045-adef-760e5066652c · outbound

This paper cites GPT-NER: Named Entity Recognition via Large Language Models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution GPT-NER: Named Entity Recognition via Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:08.860813Z

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Observation cb74506c-9176-434f-b3cf-28f1bca48a0e · outbound

This paper cites Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:08.981644Z

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Observation 2768224b-bdf3-4dda-8d10-4945f2535267 · outbound

This paper cites Encoding crowd interaction with deep neural network for pedestrian trajectory prediction.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Encoding crowd interaction with deep neural network for pedestrian trajectory prediction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.648778Z

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Observation 4c2b73c8-2d82-4776-a06e-c7d3138fa29c · outbound

This paper cites Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.642261Z

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Observation 945061bb-ae20-4dd3-9fc5-6fa297dc19cf · outbound

This paper cites Place deduplication with embeddings.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Place deduplication with embeddings

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.636087Z

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Observation 8800467d-913d-4c31-b0e3-3de91159fd26 · outbound

This paper cites Gps2vec: Towards generating worldwide gps embeddings.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Gps2vec: Towards generating worldwide gps embeddings

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.629195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5372513f-91c0-48eb-8a8d-5f34d9617b9c · outbound

This paper cites Pre-trained embeddings for entity resolution: an experimental analysis.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Pre-trained embeddings for entity resolution: an experimental analysis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.621776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e85945e-4235-4c2e-8bce-27b14595f9e0 · outbound

This paper cites Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.614533Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72aae4df-b3d3-4500-b4d4-f75368875b8d · outbound

This paper cites Detecting nearly duplicated records in location datasets.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Detecting nearly duplicated records in location datasets

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.607281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 127b2241-9698-4bea-8e5b-49aa076449b3 · outbound

This paper cites A points of interest matching method using a multivariate weighting function with gradient descent optimization.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution A points of interest matching method using a multivariate weighting function with gradient descent optimization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.599775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 617fafa1-2907-438a-ab2f-cb669aa4a3e9 · outbound

This paper cites Autotqa: Towards autonomous tabular question answering through multi-agent large language models.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Autotqa: Towards autonomous tabular question answering through multi-agent large language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.591982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d013f0e0-8088-4c0d-8b4b-715ea298012c · outbound

This paper cites @esa (Ref.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution @esa (Ref

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:09.830666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deb8db3d-8dd2-4609-b803-e1b27aaf9599 · outbound

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Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:09.950608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e14a9020-103c-45bd-9be0-520d740ac34f · outbound

This paper cites Ty @ @ _@f @ @ @ @.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Ty @ @ _@f @ @ @ @

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.575124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0e54a84-6e7a-48f2-95ee-98d047677705 · outbound

This paper cites , " * write output.state after.block = add.period.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution , " * write output.state after.block = add.period

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.563058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07fa6175-b9af-4bbd-9b6f-8f080c8514a9 · outbound

This paper cites write newline.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution write newline

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:11.352102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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