SemLink: A Semantic-Aware Automated Test Oracle for Hyperlink Verification using Siamese Sentence-BERT

2026-04-07

AI Summary

This study addresses the limitation of traditional hyperlink detection tools, which rely solely on HTTP status codes and fail to identify semantic drift—cases where a link remains technically valid but its target content diverges semantically from the source context. To overcome this, the authors propose SemLink, the first approach to integrate a lightweight semantic model into hyperlink validation. SemLink employs a Siamese neural network built upon Sentence-BERT to compute semantic consistency between the source context and the target page. Evaluated on a newly curated dataset of over 60,000 samples (HWPPs), SemLink achieves a recall of 96.00%, matching the performance of GPT-5.2 while offering approximately 47.5× faster inference and substantially reduced computational overhead.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Web applications rely heavily on hyperlinks to connect disparate information resources. However, the dynamic nature of the web leads to link rot, where targets become unavailable, and more insidiously, semantic drift, where a valid HTTP 200 connection exists, but the target content no longer aligns with the source context. Traditional verification tools, which primarily function as crash oracles by checking HTTP status codes, often fail to detect semantic inconsistencies, thereby compromising web integrity and user experience. While Large Language Models (LLMs) offer semantic understanding, they suffer from high latency, privacy concerns, and prohibitive costs for large-scale regression testing. In this paper, we propose SemLink, a novel automated test oracle for semantic hyperlink verification. SemLink leverages a Siamese Neural Network architecture powered by a pre-trained Sentence-BERT (SBERT) backbone to compute the semantic coherence between a hyperlink's source context (anchor text, surrounding DOM elements, and visual features) and its target page content. To train and evaluate our model, we introduce the Hyperlink-Webpage Positive Pairs (HWPPs) dataset, a rigorously constructed corpus of over 60,000 semantic pairs. Our evaluation demonstrates that SemLink achieves a Recall of 96.00%, comparable to state-of-the-art LLMs (GPT-5.2), while operating approximately 47.5 times faster and requiring significantly fewer computational resources. This work bridges the gap between traditional syntactic checkers and expensive generative AI, offering a robust and efficient solution for automated web quality assurance.

Problem

• semantic drift
• hyperlink verification
• test oracle
• web integrity
• link rot

Innovation

• Semantic Hyperlink Verification
• Siamese Neural Network
• Sentence-BERT
• Automated Test Oracle
• Link Rot and Semantic Drift