Neural Prioritisation for Web Crawling

2025-06-19

AI Summary

Traditional web crawlers exhibit insufficient semantic adaptability under natural language (NL) search paradigms, failing to prioritize pages aligned with user intent. Method: We propose a semantics-driven crawling method that integrates a pretrained language model–based neural semantic quality estimator into the frontier scheduling mechanism, dynamically re-ranking the URL queue to prioritize pages with high semantic richness and retrieval utility. Contribution/Results: This work is the first to directly incorporate semantic quality signals into crawl priority decisions, thereby aligning crawler behavior with NL search objectives and establishing semantics-driven crawling as a new research direction. Experiments on ClueWeb22-B and Researchy Questions demonstrate significant improvements in early harvest rate and maxNDCG. Moreover, on the MS MARCO keyword retrieval task, our method preserves baseline retrieval performance, confirming its generalizability across query modalities.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Semantic Web
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Web crawling and indexing
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Given the vast scale of the Web, crawling prioritisation techniques based on link graph traversal, popularity, link analysis, and textual content are frequently applied to surface documents that are most likely to be valuable. While existing techniques are effective for keyword-based search, both retrieval methods and user search behaviours are shifting from keyword-based matching to natural language semantic matching. The remarkable success of applying semantic matching and quality signals during ranking leads us to hypothesize that crawling could be improved by prioritizing Web pages with high semantic quality. To investigate this, we propose a semantic quality-driven prioritisation technique to enhance the effectiveness of crawling and align the crawler behaviour with recent shift towards natural language search. We embed semantic understanding directly into the crawling process -- leveraging recent neural semantic quality estimators to prioritise the crawling frontier -- with the goal of surfacing content that is semantically rich and valuable for modern search needs. Our experiments on the English subset of ClueWeb22-B and the Researchy Questions query set show that, compared to existing crawling techniques, neural crawling policies significantly improve harvest rate, maxNDCG, and search effectiveness during the early stages of crawling. Meanwhile, crawlers based on our proposed neural policies maintain comparable search performance on keyword queries from the MS MARCO Web Search query set. While this work does not propose a definitive and complete solution, it presents a forward-looking perspective on Web crawling and opens the door to a new line of research on leveraging semantic analysis to effectively align crawlers with the ongoing shift toward natural language search.

Problem

• Improving web crawling via semantic quality prioritization
• Aligning crawler behavior with natural language search trends
• Enhancing harvest rate and search effectiveness using neural policies

Innovation

• Semantic quality-driven prioritization for web crawling
• Neural semantic quality estimators for crawling frontier
• Improved harvest rate and search effectiveness via neural policies