# Combining Language and Graph Models for Semi-structured Information Extraction on the Web

arXiv: https://arxiv.org/abs/2402.14129 · HTML: https://mlsanity.com/paper/2402.14129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [HTLM: Hyper-Text Pre-Training and Prompting of Language Models](https://mlsanity.com/paper/2107.06955/) (2021)
- [ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages](https://mlsanity.com/paper/2005.07105/) (2020)
- [DOM-LM: Learning Generalizable Representations for HTML Documents](https://mlsanity.com/paper/2201.10608/) (2022)
- [The Diminishing Returns of Masked Language Models to Science](https://mlsanity.com/paper/2205.11342/) (2022)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

Relation extraction is an efficient way of mining the extraordinary wealth of human knowledge on the Web. Existing methods rely on domain-specific training data or produce noisy outputs. We focus here on extracting targeted relations from semi-structured web pages given only a short description of the relation. We present GraphScholarBERT, an open-domain information extraction method based on a joint graph and language model structure. GraphScholarBERT can generalize to previously unseen domains without additional data or training and produces only clean extraction results matched to the search keyword. Experiments show that GraphScholarBERT can improve extraction F1 scores by as much as 34.8% compared to previous work in a zero-shot domain and zero-shot website setting.