relation: http://eprints.imtlucca.it/4030/
title: ScienceWISE: Topic Modeling over Scientific Literature Networks
creator: Martini, Andrea
creator: Lutov, Artem
creator: Gemmetto, Valerio
creator: Magalich, Andrii
creator: Cardillo, Alessio
creator: Constantin, Alex
creator: Palchykov, Vasyl
creator: Khayati, Mourad
creator: Cudre-Mauroux, Philippe
creator: Boyarsky, Alexey
creator: Ruchayskiy, Oleg
creator: Garlaschelli, Diego
creator: Rios, Paolo De De
creator: Aberer, Karl
subject: QC Physics
description: We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept-based browsing. However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double-edged sword: a more fine-grained characterization of articles is possible, but at the cost of introducing more spurious relations among them. In this context, the challenge of continuously recommending relevant articles to users lies in tackling a network partitioning problem, where nodes represent articles and co-occurring concepts create edges between them. In this paper, we discuss the three research directions we have taken for solving this issue: i) the identification of generic concepts to reinforce inter-article similarities; ii) the adoption of a bipartite network representation to improve scalability; iii) the design of a clustering algorithm to identify concepts for cross-disciplinary articles and obtain fine-grained topics for all articles.
publisher: arXiv
date: 2017
type: Working Paper
type: NonPeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/4030/1/1612.07636.pdf
identifier:   Martini, Andrea and Lutov, Artem and Gemmetto, Valerio and Magalich, Andrii and Cardillo, Alessio and Constantin, Alex and Palchykov, Vasyl and Khayati, Mourad and Cudre-Mauroux, Philippe and Boyarsky, Alexey and Ruchayskiy, Oleg and Garlaschelli, Diego and Rios, Paolo De De and Aberer, Karl  ScienceWISE: Topic Modeling over Scientific Literature Networks.  Working Paper   arXiv       (Submitted)   
relation: http://arxiv.org/pdf/1612.07636
relation: arXiv:1612.07636