分类:ScienceWISE: Topic Modeling over Scientific Literature Networks

Andrea Martini, Artem Lutov, Valerio Gemmetto, Andrii Magalich, Alessio Cardillo, Alex Constantin, Vasyl Palchykov, Mourad Khayati, Philippe Cudré-Mauroux, Alexey Boyarsky, Oleg Ruchayskiy, Diego Garlaschelli, Paolo De Los Rios, Karl Aberer, ScienceWISE: Topic Modeling over Scientific Literature Networks, https://arxiv.org/abs/1612.07636

Abstract

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.

总结和评价

文章的作者是ScienceWISE的开发者。ScienceWISE^[1]企图用算法实现文章的主题发现和分类。其主要通过Ontology来实现。可以看看大概怎么做的，能不能有借鉴的地方。

参考文献