15 citations · 18 across the 11 of their papers we have counts for
13 papers
Billiger.de Products: A Bilingual Entity Matching Benchmark
Aaron Steiner, Ksenia Elagin, Ralph Peeters +2
Existing product matching benchmarks primarily contain English-language product data and are often dominated by a single product category, such as electronics. This paper introduce…
SINT-Flow: Schema Integration using Large Language Model Workflows
Keti Korini, Christian Bizer
The goal of schema integration is, given a set of input schemata or tables, to derive a global, unified schema that is able to represent the concepts, attributes, and relationships…
MaDI-Bench: An End-to-End Data Integration Benchmark
Aaron Steiner, Ralph Peeters, Christian Bizer
Data integration is the process of combining data from multiple, heterogeneous sources into a consistent, unified representation. Data integration involves a sequence of interdepen…
Labeling Training Data for Entity Matching Using Large Language Models
Aaron Steiner, Christian Bizer
Large language models (LLMs) achieve strong entity matching performance without task-specific training data, but applying them to large sets of candidate pairs is slow and costly.…
Automatic End-to-End Data Integration using Large Language Models
Aaron Steiner, Christian Bizer
Designing data integration pipelines typically requires substantial manual effort from data engineers to configure pipeline components and label training data. While LLMs have show…
MCP vs RAG vs NLWeb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report)
Aaron Steiner, Ralph Peeters, Christian Bizer
Large language model agents are increasingly used to automate web tasks such as product search, offer comparison, and checkout. Current research explores different interfaces throu…
WebMall -- A Multi-Shop Benchmark for Evaluating Web Agents
Ralph Peeters, Aaron Steiner, Luca Schwarz +2
LLM-based web agents have the potential to automate long-running web tasks, such as searching for products in multiple e-shops and subsequently ordering the cheapest products that…
Evaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type Annotation
Keti Korini, Christian Bizer
Understanding the semantics of columns in relational tables is an important pre-processing step for indexing data lakes in order to provide rich data search. An approach to establi…
Self-Refinement Strategies for LLM-based Product Attribute Value Extraction
Alexander Brinkmann, Christian Bizer
Structured product data, in the form of attribute-value pairs, is essential for e-commerce platforms to support features such as faceted product search and attribute-based product…
Fine-tuning Large Language Models for Entity Matching
Aaron Steiner, Ralph Peeters, Christian Bizer
Generative large language models (LLMs) are a promising alternative to pre-trained language models for entity matching due to their high zero-shot performance and ability to genera…
Desk-AId: Humanitarian Aid Desk Assessment with Geospatial AI for Predicting Landmine Areas
Flavio Cirillo, Gürkan Solmaz, Yi-Hsuan Peng +2
The process of clearing areas, namely demining, starts by assessing and prioritizing potential hazardous areas (i.e., desk assessment) to go under thorough investigation of experts…
Using LLMs for the Extraction and Normalization of Product Attribute Values
Alexander Brinkmann, Nick Baumann, Christian Bizer
Product offers on e-commerce websites often consist of a product title and a textual product description. In order to enable features such as faceted product search or to generate…
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…