SOTAVerified

Entity Resolution

Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)

Surveys on entity resolution:

The task of entity resolution is closely related to the task of entity alignment which focuses on matching entities between knowledge bases. The task of entity linking differs from entity resolution as entity linking focuses on identifying entity mentions in free text.

Papers

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Showing 1–50 of 184 papers

TitleStatusHype
Can Foundation Models Wrangle Your Data?Code5
AutoBlock: A Hands-off Blocking Framework for Entity MatchingCode1
Entity Matching using Large Language ModelsCode1
Domain Adaptation for Deep Entity Resolution: A Design Space ExplorationCode1
Dual-Objective Fine-Tuning of BERT for Entity MatchingCode1
Entity Resolution with Hierarchical Graph Attention NetworksCode1
Fine-tuning Large Language Models for Entity MatchingCode1
PIZZA: A new benchmark for complex end-to-end task-oriented parsingCode1
A Critical Re-evaluation of Neural Methods for Entity AlignmentCode1
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and BeyondCode1
Deep Entity Matching with Pre-Trained Language ModelsCode1
Cost-Effective In-Context Learning for Entity Resolution: A Design Space ExplorationCode1
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
A Deep Learning Approach to Geographical Candidate Selection through Toponym MatchingCode1
Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]Code1
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.orgCode1
Intermediate Training of BERT for Product MatchingCode1
Match, Compare, or Select? An Investigation of Large Language Models for Entity MatchingCode1
Supervised Contrastive Learning for Product MatchingCode1
WDC Products: A Multi-Dimensional Entity Matching BenchmarkCode1
Using ChatGPT for Entity MatchingCode1
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data IntegrationCode1
A Practioner's Guide to Evaluating Entity Resolution ResultsCode1
How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name DisambiguationCode1
Anaphora and Coreference Resolution: A Review—0
Automated Metadata Harmonization Using Entity Resolution & Contextual Embedding—0
Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information—0
Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs—0
Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports—0
A Weak Self-supervision with Transition-Based Modeling for Reference Resolution—0
(Almost) All of Entity Resolution—0
Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation—0
Author Name Disambiguation in Bibliographic Databases: A Survey—0
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison—0
A Three-Way Model for Collective Learning on Multi-Relational Data—0
Em-K Indexing for Approximate Query Matching in Large-scale ER—0
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs—0
A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution—0
A Survey on Efficient Processing of Similarity Queries over Neural Embeddings—0
Aleda, a free large-scale entity database for French—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
Clustering Via Crowdsourcing—0
Clustering with Noisy Queries—0
Collective Entity Resolution with Multi-Focal Attention—0
Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution—0
Combining Global and Local Merges in Logic-based Entity Resolution—0
A Study on Entity Resolution for Email Conversations—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage—0
Clustering on the Edge: Learning Structure in Graphs—0
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All datasetsAmazon-GoogleAbt-BuyWDC Products-80%cc-seen-mediumWDC Computers-smallWDC Computers-xlargeWDC Products-50%cc-unseen-mediumWDC Watches-smallMusicBrainz20KWDC Watches-xlargeWDC Products-80%cc-seen-medium-multiWDC Products

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt4-0613_fewshot-10F1 (%)85.21—Unverified
2gpt-4o-mini-2024-07-18_fine_tunedF1 (%)80.25—Unverified
3RoBERTa-SupConF1 (%)79.28—Unverified
4RobEMF1 (%)79.06—Unverified
5Random ForestF1 (%)79—Unverified
6HGF1 (%)76.4—Unverified
7DittoF1 (%)75.58—Unverified
8CorDEL-SumF1 (%)70.2—Unverified
9DeepMatcher - HybridF1 (%)69.3—Unverified
10D-HATF1 (%)67.5—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt4-0613_zeroshotF1 (%)95.78—Unverified
2RoBERTa-SupConF1 (%)94.29—Unverified
3gpt-4o-mini-2024-07-18_fine_tunedF1 (%)94.09—Unverified
4gpt-4o-2024-08-06F1 (%)92.2—Unverified
5RobEMF1 (%)90.9—Unverified
6HGF1 (%)89.8—Unverified
7DittoF1 (%)89.33—Unverified
8gpt-4o-mini-2024-07-18F1 (%)87.68—Unverified
9Meta-Llama-3.1-8B-Instruct_fine_tunedF1 (%)87.34—Unverified
10Random ForestF1 (%)85—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt4-0613_zeroshotF1 (%)89.61—Unverified
2gpt-4o-2024-08-06_fine_tuned_wdc_smallF1 (%)87.1—Unverified
3gpt-4o-mini-2024-07-18_structured_explanationsF1 (%)84.38—Unverified
4gpt-4o-mini-2024-07-18F1 (%)81.61—Unverified
5RoBERTa-SupConF1 (%)79.99—Unverified
6Llama3.1_70B_structured_explanationsF1 (%)76.7—Unverified
7Llama3.1_70BF1 (%)75.2—Unverified
8Llama3.1_8B_error-based_example_selectionF1 (%)74.37—Unverified
9Llama3.1_8B_structured_explanationsF1 (%)74.13—Unverified
10DittoF1 (%)73.93—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTF1 (%)96.53—Unverified
2RoBERTa-SupConF1 (%)95.21—Unverified
3HGF1 (%)88.5—Unverified
4DADER-MMDF1 (%)88—Unverified
5DittoF1 (%)80.76—Unverified
6JointBERTF1 (%)77.55—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-SupConF1 (%)98.33—Unverified
2JointBERTF1 (%)97.49—Unverified
3BERTF1 (%)97.37—Unverified
4HGF1 (%)96.5—Unverified
5DittoF1 (%)95.45—Unverified
6Random ForestF1 (%)78—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-baseF1 (%)71.14—Unverified
2DittoF1 (%)70.66—Unverified
3HGF1 (%)68.74—Unverified
4RoBERTa-SupConF1 (%)57.23—Unverified
#ModelMetricClaimedVerifiedStatus
1HGF1 (%)94—Unverified
2DADER-NoDAF1 (%)88.6—Unverified
3DittoF1 (%)85.12—Unverified
4JointBERTF1 (%)75.83—Unverified
#ModelMetricClaimedVerifiedStatus
1ALMSER-GBF10.95—Unverified
2FAMER-SplitMergeF10.88—Unverified
3FAMER-SplitF10.84—Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERTF1 (%)97.09—Unverified
2DittoF1 (%)96.53—Unverified
3HGF1 (%)96.5—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-SupConF1 Micro88.63—Unverified
2RoBERTa-baseF1 Micro52.03—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt-4o-2024-08-06_fine_tuned_wdc_smallF1 (%)87.07—Unverified