Increasing Papers' Discoverability with Precise Semantic Labeling: the sci.AI Platform
Roman Gurinovich, Alexander Pashuk, Yuriy Petrovskiy, Alex, Dmitrievskij, Oleg Kuryan, Alexei Scerbacov, Antonia Tiggre, Elena Moroz,, Yuri Nikolsky
TL;DR
The paper introduces the sci.AI platform, which enhances biomedical papers' discoverability by enabling precise semantic labeling to address terminology variability and ambiguity in retrieval.
Contribution
It presents a novel platform that improves biomedical literature retrieval through accurate semantic labeling of domain-specific terms.
Findings
Terminology variability significantly impacts paper retrieval.
The sci.AI platform enables precise semantic labeling to mitigate retrieval challenges.
Enhanced labeling improves relevance of search results.
Abstract
The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Topic Modeling · Natural Language Processing Techniques