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In the scientific digital libraries, some papers from different researchcommunities can be described by community-dependent keywords even if they sharea semantically similar topic. Articles that are not tagged with enough keywordvariations are poorly indexed in any information retrieval system which limitspotentially fruitful exchanges between scientific disciplines. In this paper,we introduce a novel experimentally designed pipeline for multi-labelsemantic-based tagging developed for open-access metadata digital libraries.The approach starts by learning from a standard scientific categorization and asample of topic tagged articles to find semantically relevant articles andenrich its metadata accordingly. Our proposed pipeline aims to enableresearchers reaching articles from various disciplines that tend to usedifferent terminologies. It allows retrieving semantically relevant articlesgiven a limited known variation of search terms. In addition to achieving anaccuracy that is higher than an expanded query based method using a topicsynonym set extracted from a semantic network, our experiments also show ahigher computational scalability versus other comparable techniques. We createda new benchmark extracted from the open-access metadata of a scientific digitallibrary and published it along with the experiment code to allow furtherresearch in the topic.

Metadata Enrichment of Multi-Disciplinary Digital Library: A Semantic-based Approach