2 research outputs found
Towards a Semantic Search Engine for Scientific Articles
Because of the data deluge in scientific publication, finding relevant
information is getting harder and harder for researchers and readers. Building
an enhanced scientific search engine by taking semantic relations into account
poses a great challenge. As a starting point, semantic relations between
keywords from scientific articles could be extracted in order to classify
articles. This might help later in the process of browsing and searching for
content in a meaningful scientific way. Indeed, by connecting keywords, the
context of the article can be extracted. This paper aims to provide ideas to
build such a smart search engine and describes the initial contributions
towards achieving such an ambitious goal
Automated Machine Learning for Information Retrieval in Scientific Articles
International audienceThe amount of scientific conferences and journal articles continues to increase and new approaches are required to support users in finding relevant publications. This study investigates to what extent a new machine learning (ML) pipeline may preferentially identify links between similar scientific articles. The characteristics of intersections and unions of keywords, contextualized keywords (i.e., synsets) and neighbors are computed and used to train a ML model. Automated machine learning (AutoML) is then applied to ease the search for a new pipeline. Extensive experiments demonstrated that a newly designed ML model achieves an accuracy of 90% on a dataset of approximately 120,000 article pairs. These results suggest that application of ML for proposing new recommendation systems could have in the long term a positive impact in the literature