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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 184 papers

TitleStatusHype
ChatPD: An LLM-driven Paper-Dataset Networking SystemCode0
Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information—0
Text2Tracks: Prompt-based Music Recommendation via Generative RetrievalCode0
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants—0
Leveraging User-Generated Metadata of Online Videos for Cover Song Identification—0
Leveraging large language models for efficient representation learning for entity resolution—0
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and ResolutionCode0
Gem: Gaussian Mixture Model Embeddings for Numerical Feature Distributions—0
T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution FrameworkCode0
Learning variant product relationship and variation attributes from e-commerce website structures—0
Fine-tuning Large Language Models for Entity MatchingCode1
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap—0
Learning from Natural Language Explanations for Generalizable Entity Matching—0
Match, Compare, or Select? An Investigation of Large Language Models for Entity MatchingCode1
Towards Universal Dense Blocking for Entity ResolutionCode0
How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name DisambiguationCode1
Methods for Matching English Language Addresses—0
Neural Locality Sensitive Hashing for Entity Blocking—0
Spatial Entity Resolution between Restaurant Locations and Transportation Destinations in Southeast Asia—0
On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach—0
Cost-Effective In-Context Learning for Entity Resolution: A Design Space ExplorationCode1
Entity Matching using Large Language ModelsCode1
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution—0
Graph Representation Learning Towards Patents Network Analysis—0
Revisiting Prompt Engineering via Declarative Crowdsourcing—0
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution—0
Named Entity Resolution in Personal Knowledge Graphs—0
A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching AlgorithmsCode0
Record Deduplication for Entity Distribution Modeling in ASR Transcripts—0
Combining Global and Local Merges in Logic-based Entity Resolution—0
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
Using ChatGPT for Entity MatchingCode1
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data IntegrationCode1
Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]Code1
A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases—0
Another Generic Setting for Entity Resolution: Basic Theory—0
SC-Block: Supervised Contrastive Blocking within Entity Resolution PipelinesCode0
WDC Products: A Multi-Dimensional Entity Matching BenchmarkCode1
KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution—0
PIZZA: A new benchmark for complex end-to-end task-oriented parsingCode1
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
Introducing Semantics into Speech Encoders—0
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.orgCode1
Probing the Robustness of Pre-trained Language Models for Entity MatchingCode0
FlexER: Flexible Entity Resolution for Multiple IntentsCode0
Domain Adaptation for Deep Entity Resolution: A Design Space ExplorationCode1
Entity Resolution with Hierarchical Graph Attention NetworksCode1
Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI—0
Can Foundation Models Wrangle Your Data?Code5
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction—0
Show:102550
← PrevPage 1 of 4Next →
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