Towards Zero-shot Relation Extraction in Web Mining: A Multimodal
  Approach with Relative XML Path
Zilong Wang Jingbo Shang
The rapid growth of web pages and the increasing complexity of their
structure poses a challenge for web mining models. Web mining models are
required to understand the semi-structured web pages, particularly when little
is known about the subject or template of a new page. Current methods migrate
language models to the web mining by embedding the XML source code into the
transformer or encoding the rendered layout with graph neural networks.
However, these approaches do not take into account the relationships between
text nodes within and across pages. In this paper, we propose a new approach,
ReXMiner, for zero-shot relation extraction in web mining. ReXMiner encodes the
shortest relative paths in the Document Object Model (DOM) tree which is a more
accurate and efficient signal for key-value pair extraction within a web page.
It also incorporates the popularity of each text node by counting the
occurrence of the same text node across different web pages. We use the
contrastive learning to address the issue of sparsity in relation extraction.
Extensive experiments on public benchmarks show that our method, ReXMiner,
outperforms the state-of-the-art baselines in the task of zero-shot relation
extraction in web mining.