10 citations · 35 across the 25 of their papers we have counts for
25 papers
Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching
Nicholas Pulsone, Gregory Goren, Roee Shraga
Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world…
BEACON: Budget-Aware Entity Matching Across Domains (Extended Technical Report)
Nicholas Pulsone, Roee Shraga, Gregory Goren
Entity Matching (EM)--the task of determining whether two data records refer to the same real-world entity--is a core task in data integration. Recent advances in deep learning hav…
3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)
Bar Genossar, Sagi Dalyot, Roee Shraga +1
Urban environments are continuously mapped and modeled by various data collection platforms, including satellites, unmanned aerial vehicles and street cameras. The growing availabi…
SAVeD: Semantic Aware Version Discovery
Artem Frenk, Roee Shraga
Our work introduces SAVeD (Semantically Aware Version Detection), a contrastive learning-based framework for identifying versions of structured datasets without relying on metadata…
Diverse Unionable Tuple Search: Novelty-Driven Discovery in Data Lakes [Technical Report]
Aamod Khatiwada, Roee Shraga, Renée J. Miller
Unionable table search techniques input a query table from a user and search for data lake tables that can contribute additional rows to the query table. The definition of unionabi…
Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability
Sreeram Marimuthu, Nina Klimenkova, Roee Shraga
Data discovery and table unionability in particular became key tasks in modern Data Science. However, the human perspective for these tasks is still under-explored. Thus, this rese…
Fuzzy Integration of Data Lake Tables
Aamod Khatiwada, Roee Shraga, Renée J. Miller
Data integration is an important step in any data science pipeline where the objective is to unify the information available in different datasets for comprehensive analysis. Full…
Gen-T: Table Reclamation in Data Lakes
Grace Fan, Roee Shraga, Renée J. Miller
We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source tabl…
The Battleship Approach to the Low Resource Entity Matching Problem
Bar Genossar, Avigdor Gal, Roee Shraga
Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity. Recent advances in deep learning methods, usi…
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction
Alexander Brinkmann, Roee Shraga, Christian Bizer
E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. Ho…
Generative Benchmark Creation for Table Union Search
Koyena Pal, Aamod Khatiwada, Roee Shraga +1
Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data siz…
LOUC: Leave-One-Out-Calibration Measure for Analyzing Human Matcher Performance
Matan Solomon, Bar Genossar, Roee Shraga +1
Schema matching is a core data integration task, focusing on identifying correspondences among attributes of multiple schemata. Numerous algorithmic approaches were suggested for s…
Product Information Extraction using ChatGPT
Alexander Brinkmann, Roee Shraga, Reng Chiz Der +1
Structured product data in the form of attribute/value pairs is the foundation of many e-commerce applications such as faceted product search, product comparison, and product recom…
DIALITE: Discover, Align and Integrate Open Data Tables
Aamod Khatiwada, Roee Shraga, Renée J. Miller
We demonstrate a novel table discovery pipeline called DIALITE that allows users to discover, integrate and analyze open data tables. DIALITE has three main stages. First, it allow…
SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
Alexander Brinkmann, Roee Shraga, Christian Bizer
The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computati…
Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V (Technical Report)
Roee Shraga, Renée J. Miller
In multi-user environments in which data science and analysis is collaborative, multiple versions of the same datasets are generated. While managing and storing data versions has r…
FlexER: Flexible Entity Resolution for Multiple Intents
Bar Genossar, Roee Shraga, Avigdor Gal
Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat en…
SANTOS: Relationship-based Semantic Table Union Search
Aamod Khatiwada, Grace Fan, Roee Shraga +4
Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in…
HumanAL: Calibrating Human Matching Beyond a Single Task
Roee Shraga
This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a behavioral profile for human annotators which is used as a feature…
Human's Role in-the-Loop
Avigdor Gal, Roee Shraga
Data integration has been recently challenged by the need to handle large volumes of data, arriving at high velocity from a variety of sources, which demonstrate varying levels of…
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)
Roee Shraga, Gil Katz, Yael Badian +2
Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that da…
PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching
Roee Shraga, Avigdor Gal
Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web and data mining for many years, the main challenge r…
OSOUM Framework for Trading Data Research
Gregory Goren, Roee Shraga, Alexander Tuisov
In the last decades, data have become a cornerstone component in many business decisions, and copious resources are being poured into production and acquisition of the high-quality…
Learning to Characterize Matching Experts
Roee Shraga, Ofra Amir, Avigdor Gal
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements. Matching problems were traditionally solved in a semi-aut…