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Blocking

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)

Blocking is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible. Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher generates a final set of matching record pairs.

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Papers

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Showing 251–275 of 524 papers

TitleStatusHype
The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment- A Preprint—0
The Behavior of Internet Traffic for Internet Services during COVID-19 Pandemic Scenario—0
The blocking effect and Korean caki—0
The Complexity of Interactively Learning a Stable Matching by Trial and Error—0
The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation—0
The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems—0
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples—0
Improving Optical Flow on a Pyramid Level—0
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation—0
The lattice of envy-free many-to-many matchings with contracts—0
The lattice of worker-quasi-stable matchings—0
Theta, alpha and gamma traveling waves in a multi-item working memory model—0
Time-Selective RNN for Device-Free Multi-Room Human Presence Detection Using WiFi CSI—0
Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning—0
Trajectories of Blocked Community Members: Redemption, Recidivism and Departure—0
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals—0
Tumor-induced neoneurogenesis and perineural tumor growth: a mathematical approach—0
Unidirectional Imaging using Deep Learning-Designed Materials—0
Unidirectional imaging with partially coherent light—0
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs—0
Universal Silicon Microwave Photonic Spectral Shaper—0
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction—0
Unsupervised Paraphrasing with Pretrained Language Models—0
Using GPI-2 for Distributed Memory Paralleliziation of the Caffe Toolbox to Speed up Deep Neural Network Training—0
Using mathematical models of heart cells to assess the safety of new pharmaceutical drugs—0
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