Entity Resolution
49 papers with code • 10 benchmarks • 11 datasets
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:
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Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey, 2020.
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Barlaug and Gulla: Neural Networks for Entity Matching: A Survey, 2021.
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.
Benchmarks
These leaderboards are used to track progress in Entity Resolution
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Libraries
Use these libraries to find Entity Resolution models and implementationsDatasets
Amazon-Google
PIZZA
Latest papers with no code
Methods for Matching English Language Addresses
Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to.
Neural Locality Sensitive Hashing for Entity Blocking
We assess the effectiveness of this approach within the context of the entity resolution problem, which frequently involves the use of task-specific metrics in real-world applications.
Spatial Entity Resolution between Restaurant Locations and Transportation Destinations in Southeast Asia
As a tech company, Grab has expanded from transportation to food delivery, aiming to serve Southeast Asia with hyperlocalized applications.
On Leveraging Large Language Models for Enhancing Entity Resolution
Entity resolution, the task of identifying and consolidating records that pertain to the same real-world entity, plays a pivotal role in various sectors such as e-commerce, healthcare, and law enforcement.
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution
We use an extensive set of experimental results to show that an LLM like GPT3. 5 is viable for high-performing unsupervised ER, and interestingly, that more complicated and detailed (and hence, expensive) prompting methods do not necessarily outperform simpler approaches.
Graph Representation Learning Towards Patents Network Analysis
Patent analysis has recently been recognized as a powerful technique for large companies worldwide to lend them insight into the age of competition among various industries.
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties.
Revisiting Prompt Engineering via Declarative Crowdsourcing
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone.
Named Entity Resolution in Personal Knowledge Graphs
We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER.
Record Deduplication for Entity Distribution Modeling in ASR Transcripts
Voice digital assistants must keep up with trending search queries.
