Search Results for author:
Found 12 papers, 8 papers with code
The Battleship Approach to the Low Resource Entity Matching Problem
Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity.
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction
Vendors often times provide unstructured product descriptions consisting only of an offer title and a textual description.
Ranked #1 on
Attribute Value Extraction
on AE-110k
(F1-score metric)
Generative Benchmark Creation for Table Union Search
We thoroughly evaluate recent existing table union search methods over existing benchmarks and our new benchmark.
Product Information Extraction using ChatGPT
Hence, extracting attribute/value pairs from textual product descriptions is an essential enabler for e-commerce applications.
SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
To reduce these runtimes, entity resolution pipelines are constructed of two parts: a blocker that applies a computationally cheap method to select candidate record pairs, and a matcher that afterwards identifies matching pairs from this set using more expensive methods.
Ranked #1 on
Blocking
on Amazon-Google
FlexER: Flexible Entity Resolution for Multiple Intents
In what follows, we introduce the problem of multiple intents entity resolution (MIER), an extension to the universal (single intent) entity resolution task.
HumanAL: Calibrating Human Matching Beyond a Single Task
This work offers a novel view on the use of human input as labels, acknowledging that humans may err.
Human's Role in-the-Loop
Given the availability of data and the improvement of machine learning techniques, this blog discusses the respective roles of humans and machines in achieving cognitive tasks in matching, aiming to determine whether traditional roles of humans and machines are subject to change.
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)
In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data.
PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching
Schema matching is a core task of any data integration process.
Mapping Patterns for Virtual Knowledge Graphs
Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources.
Learning to Characterize Matching Experts
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements.
