IRJun 28, 2021

The DELICES project: Indexing scientific literature through semantic expansion

arXiv:2106.14731v1
Originality Synthesis-oriented
AI Analysis

This addresses the problem of limited access to scientific knowledge for researchers and users of digital libraries, but appears incremental as it builds on existing semantic representation methods.

The DELICES project tackles the challenge of retrieving relevant scientific publications by exploiting semantic relations between articles to improve and enrich indexing, aiming to increase keyphrase relevance and extend indexing to new terms from similar documents.

Scientific digital libraries play a critical role in the development and dissemination of scientific literature. Despite dedicated search engines, retrieving relevant publications from the ever-growing body of scientific literature remains challenging and time-consuming. Indexing scientific articles is indeed a difficult matter, and current models solely rely on a small portion of the articles (title and abstract) and on author-assigned keyphrases when available. This results in a frustratingly limited access to scientific knowledge. The goal of the DELICES project is to address this pitfall by exploiting semantic relations between scientific articles to both improve and enrich indexing. To this end, we will rely on the latest advances in semantic representations to both increase the relevance of keyphrases extracted from the documents, and extend indexing to new terms borrowed from semantically similar documents.

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