# ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages

arXiv: https://arxiv.org/abs/2005.07105 · HTML: https://mlsanity.com/paper/2005.07105/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 30 of 30)

- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [AutoData: A Multi-Agent System for Open Web Data Collection](https://mlsanity.com/paper/2505.15859/) (2025)
- [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)
- [Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration](https://mlsanity.com/paper/2309.01131/) (2023)
- [Simplified DOM Trees for Transferable Attribute Extraction from the Web](https://mlsanity.com/paper/2101.02415/) (2021)
- [Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation](https://mlsanity.com/paper/2109.01048/) (2021)
- [ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select](https://mlsanity.com/paper/2210.14427/) (2022)
- [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://mlsanity.com/paper/2205.06435/) (2022)
- [Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding](https://mlsanity.com/paper/2402.18262/) (2024)
- [Position Masking for Improved Layout-Aware Document Understanding](https://mlsanity.com/paper/2109.00442/) (2021)
- [PLAtE: A Large-scale Dataset for List Page Web Extraction](https://mlsanity.com/paper/2205.12386/) (2022)
- [Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents](https://mlsanity.com/paper/2210.05613/) (2022)
- [Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation](https://mlsanity.com/paper/2305.14828/) (2023)
- [Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting](https://mlsanity.com/paper/2305.13533/) (2023)
- [Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path](https://mlsanity.com/paper/2305.13805/) (2023)
- [High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models](https://mlsanity.com/paper/2312.08274/) (2023)
- [Combining Language and Graph Models for Semi-structured Information Extraction on the Web](https://mlsanity.com/paper/2402.14129/) (2024)
- [Hypertext Entity Extraction in Webpage](https://mlsanity.com/paper/2403.01698/) (2024)
- [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)
- [A systematic review of relation extraction task since the emergence of Transformers](https://mlsanity.com/paper/2511.03610/) (2025)
- [Metaknowledge Extraction Based on Multi-Modal Documents](https://mlsanity.com/paper/2102.02971/) (2021)
- [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)
- [Webscraper: Leverage Multimodal Large Language Models for Index-Content Web Scraping](https://mlsanity.com/paper/2603.29161/) (2026)

## Abstract (arXiv)

In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.