A hybrid semantic approach to building dynamic maps of research communities

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems

AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

Being aware of new research topics is an important asset for anybody involved in the research environment, including researchers, academic publishers and institutional funding bodies. In recent years, the amount of scholarly data available on the web has increased steadily, allowing the development of several approaches for detecting emerging research topics and assessing their trends. However, current methods focus on the detection of topics which are already associated with a label or a substantial number of documents. In this paper, we address instead the issue of detecting embryonic topics, which do not possess these characteristics yet. We suggest that it is possible to forecast the emergence of novel research topics even at such early stage and demonstrate that the emergence of a new topic can be anticipated by analysing the dynamics of pre-existing topics. We present an approach to evaluate such dynamics and an experiment on a sample of 3 million research papers, which confirms our hypothesis. In particular, we found that the pace of collaboration in sub-graphs of topics that will give rise to novel topics is significantly higher than the one in the control group