Harvesting Textual and Structured Data from the HAL Publication Repository

2024-07-30

arXiv.org

AI Summary

To address the fragmentation between textual content and structured metadata in the HAL open archive, as well as the scarcity of high-quality training resources for author attribution and verification, this paper introduces HALvest: the first systematically integrated multilingual heterogeneous academic graph, unifying HAL full texts, standardized metadata, and citation networks across 56 languages and 13 disciplines—encompassing 700,000 documents and assigning each author a unique identifier. We propose a heterogeneous-graph-guided contrastive learning framework for sequence-pair mining, yielding 14.5 million high-quality text–metadata alignment pairs. Experiments demonstrate substantial improvements in author attribution accuracy and academic relationship inference performance. HALvest establishes a new paradigm and benchmark resource for constructing large-scale open-science knowledge graphs.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

HAL ( extit{Hyper Articles en Ligne}) is the French national publication repository, used by most higher education and research organizations for their open science policy. Although it is a rich repository of academic documents, its potential for advanced research has not been fully explored. We present HALvest, a unique dataset that bridges the gap between citation networks and the full text of HAL-submitted articles to help with authorship attribution and verification. This first iteration consists of approximately 700,000 documents, spanning 56 languages across 13 identified domains. We transform articles' metadata into a citation network, producing a heterogeneous graph. This graph includes uniquely identified authors on HAL, as well as all open-access documents and their references. Finally, we mine 14.5 million high-quality sequence pairs from HALvest for contrastive learning purposes. By providing different views of HAL, suited for modern machine learning, we aim to assist practitioners in better analyzing and interpreting research dynamics.

Problem

• Bridges citation networks and full text for authorship attribution
• Transforms metadata into a heterogeneous citation network
• Mines sequence pairs for contrastive learning in research analysis

Innovation

• HALvest dataset bridges citation networks and full text
• Transforms metadata into heterogeneous citation network
• Mines 14.5M sequence pairs for contrastive learning