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 151–184 of 184 papers

TitleStatusHype
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
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction—0
Can Crowdsourcing be used for Effective Annotation of Arabic?—0
Clustering on the Edge: Learning Structure in Graphs—0
Clustering Via Crowdsourcing—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—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
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison—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
Cost-effective Variational Active Entity Resolution—0
Deriving Adjectival Scales from Continuous Space Word Representations—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
Enforcing Relational Matching Dependencies with Datalog for Entity Resolution—0
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap—0
Entity Linking for Tweets—0
Entity Resolution and Federated Learning get a Federated Resolution—0
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data—0
Entity resolution for noisy ASR transcripts—0
Entity Resolution in Open-domain Conversations—0
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution—0
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution—0
ErGAN: Generative Adversarial Networks for Entity Resolution—0
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
Fast Learning from Distributed Datasets without Entity Matching—0
Flexible Models for Microclustering with Application to Entity Resolution—0
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants—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