Exploring Scholarly Data with Rexplore

Abstract

Abstract. Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors 'semantically' (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. 'ordinary' users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves.

Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks

“…This indicates that two topics can be treated as equivalent for the purpose of exploring research data -e.g., "ontology mapping" and "ontology matching". Skos:broaderGeneric and relatedEquivalent are necessary to build a taxonomy of topics and to handle different labels for the same research areas, while contributesTo provides an additional relationship that can be used to assist the user in browsing research topics [5] and analyzing research data -e.g., for identifying topic-based research communities [10].…”

“…The output is a populated OWL ontology describing the semantic relationships between the research topics identified from the set of keywords and the other data provided as input. This semantic network can then be used for improving the processes of searching and performing analytics on scholarly data [3,5,6,7]. As in the case of the Klink algorithm, Klink-2 generates an ontology of research topics linked by the three relationships introduced above.…”

“…Increasingly, Semantic Web standards are being used to represent this complex data and, as a result, we have seen the emergence of a number of bibliographic repositories in the Linked Data Cloud [1,2,3] and a variety of ontologies to describe scholarly data, including SWRC 1 , BIBO 2 , BiDO 3 , AKT 4 and FABIO 5 . The semantic enhancement of scholarly articles, known as semantic publishing [4], is also becoming an important topic, attracting the interest of major publishers and leading to the formation of new communities (e.g., FORCE11 6 ), workshops (e.g., Linked Science at ISWC, Sepublica at ESWC, SAVE-SD at WWW), and challenges (e.g., the ESWC Semantic Publishing Challenge 7 ).…”

“…However, there is growing consensus that semantic technologies can help to overcome this problem by improving our ability to discover, query, explore, annotate and visualize research information on the web [4,5,6,7,8,9,10]. Nonetheless, we still face some important technical challenges before this vision can be realized.…”

“…This approach was demonstrated to work very well in comparison with the state of art and the ontology produced by Klink has been used to provide a comprehensive semantic topic network for Rexplore [5], a novel system which integrates semantic technologies, statistical analysis and visual analytics to provide effective support for making sense of scholarly data. In particular, the ontology generated by Klink enhances semantically a variety of data mining and information extraction techniques, and improves search and visual analytics.…”