# The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models

arXiv: https://arxiv.org/abs/2111.02168 · HTML: https://mlsanity.com/paper/2111.02168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Training Graph Neural Networks with 1000 Layers](https://mlsanity.com/paper/2106.07476/) (2021)
- [DOM-Q-NET: Grounded RL on Structured Language](https://mlsanity.com/paper/1902.07257/) (2019)
- [Multi-label Classification with Partial Annotations using Class-aware Selective Loss](https://mlsanity.com/paper/2110.10955/) (2021)
- [FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents](https://mlsanity.com/paper/2010.10755/) (2020)
- [Simplified DOM Trees for Transferable Attribute Extraction from the Web](https://mlsanity.com/paper/2101.02415/) (2021)
- [Vertex nomination schemes for membership prediction](https://mlsanity.com/paper/1312.2638/) (2013)
- [CoVA: Context-aware Visual Attention for Webpage Information Extraction](https://mlsanity.com/paper/2110.12320/) (2021)

## Built on by (most cited first, 2 of 2)

- [NEXT-EVAL: Next Evaluation of Traditional and LLM Web Data Record Extraction](https://mlsanity.com/paper/2505.17125/) (2025)
- [Visual-Aware Representation of Web Pages for Machine Learning Applications](https://mlsanity.com/paper/2608.18727/) (2026)

## Abstract (arXiv)

Web automation holds the potential to revolutionize how users interact with the digital world, offering unparalleled assistance and simplifying tasks via sophisticated computational methods. Central to this evolution is the web element nomination task, which entails identifying unique elements on webpages. Unfortunately, the development of algorithmic designs for web automation is hampered by the scarcity of comprehensive and realistic datasets that reflect the complexity faced by real-world applications on the Web. To address this, we introduce the Klarna Product Page Dataset, a comprehensive and diverse collection of webpages that surpasses existing datasets in richness and variety. The dataset features 51,701 manually labeled product pages from 8,175 e-commerce websites across eight geographic regions, accompanied by a dataset of rendered page screenshots. To initiate research on the Klarna Product Page Dataset, we empirically benchmark a range of Graph Neural Networks (GNNs) on the web element nomination task. We make three important contributions. First, we found that a simple Convolutional GNN (GCN) outperforms complex state-of-the-art nomination methods. Second, we introduce a training refinement procedure that involves identifying a small number of relevant elements from each page using the aforementioned GCN. These elements are then passed to a large language model for the final nomination. This procedure significantly improves the nomination accuracy by 16.8 percentage points on our challenging dataset, without any need for fine-tuning. Finally, in response to another prevalent challenge in this field - the abundance of training methodologies suitable for element nomination - we introduce the Challenge Nomination Training Procedure, a novel training approach that further boosts nomination accuracy.