Result Details
SmartScrape: A Neuro-Symbolic Web Information Extraction
We introduce SmartScrape, a neuro-symbolic framework for extracting structured information from the web. The task addressed is to extract records that follow a predefined schema from semi-structured sources, such as product web pages. The system integrates a Graph Neural Network (GNN) that scores content elements with an Integer Linear Programming (ILP) constraint solver, which guarantees schema consistency, uniqueness, and additional logical and presentation constraints. We illustrate the method on a basic (product, price) extraction scenario. The implemented system outputs visual proof-carrying justifications, supports drift detection using the stability metric σ(P), and we also include an ablation study contrasting ILP-based and greedy reasoning modes. On a set of 100 pages, SmartScrape attains 97% field-level accuracy with no constraint violations.
Constraint solving | Drift detection | FitLayout | GNN | ILP | Web information extraction
@inproceedings{BUT212243,
author="Ganjali {Imanov} and Radek {Burget}",
title="SmartScrape: A Neuro-Symbolic Web Information Extraction",
booktitle="Lecture Notes in Computer Science",
year="2026",
journal="Lecture Notes in Computer Science",
number="16625 LNCS",
pages="301--305",
publisher="Springer Science and Business Media Deutschland GmbH",
address="Lyon",
doi="10.1007/978-3-032-29372-5\{_}29",
isbn="9783032293718",
url="https://link.springer.com/chapter/10.1007/978-3-032-29372-5_29"
}