# CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web

arXiv: https://arxiv.org/abs/1804.04635 · HTML: https://mlsanity.com/paper/1804.04635/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [DOM-LM: Learning Generalizable Representations for HTML Documents](https://mlsanity.com/paper/2201.10608/) (2022)
- [AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation](https://mlsanity.com/paper/2404.12753/) (2024)
- [Simplified DOM Trees for Transferable Attribute Extraction from the Web](https://mlsanity.com/paper/2101.02415/) (2021)
- [A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach](https://mlsanity.com/paper/2211.10018/) (2022)
- [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023)
- [Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path](https://mlsanity.com/paper/2305.13805/) (2023)
- [Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?](https://mlsanity.com/paper/2509.25107/) (2025)
- [Label-Efficient Self-Training for Attribute Extraction from Semi-Structured Web Documents](https://mlsanity.com/paper/2208.13086/) (2022)
- [SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning](https://mlsanity.com/paper/2510.01832/) (2025)

## Abstract (arXiv)

The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.