Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2028

Abstract

Website owner identification aims to link websites to their real-world owners, which is crucial for credibility assessment and information provenance in information retrieval and vital for applications in cybersecurity, Internet governance, and digital regulation. Existing approaches for website owner identification primarily rely on querying infrastructure registration records or analyzing webpage content. However, these methods often fail due to incomplete or outdated registration records and sparse webpage content. We observe that inter-website relationships, derived from shared infrastructure data such as primary domains, IP blocks, and geolocations, can provide valuable but underutilized ownership cues. To exploit this insight, we propose MetaRAG, a meta-path-guided dynamic graph retrieval-augmented generation framework that performs reasoning using large language models over ownership-relevant paths in a website-centric knowledge graph. MetaRAG consists of three components: (1) a knowledge graph construction module that integrates infrastructure data and crawled webpage content into a unified representation; (2) a meta-path-guided dynamic reasoning module that constrains retrieval to ownership-relevant meta-paths and adaptively decides whether to retrieve more information or perform inference based on evidence completeness; and (3) a multi-path evidence refinement module that aggregates and scores retrieved paths to suppress noise and distill high-confidence ownership signals. We evaluateMetaRAG on two constructed real-world datasets, achieving up to 6.82% improvement over strong baselines. The results demonstrate the effectiveness of our approach in combining structured web knowledge with large language model-based reasoning for more accurate website owner identification.

Keywords

Website owner identification, graph retrieval-augmented generation, large language model, knowledge graph, meta path

Discipline

Artificial Intelligence and Robotics | Information Security

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Management Information Systems

Volume

44

Issue

4

First Page

1

Last Page

33

ISSN

2158-656X

Identifier

10.1145/3800961

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3800961

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