rexplore technology full details
Professor of Knowledge Technologies
Timeline:01 Nov 2012
Exploring Research Data
Rexplore leverages novel solutions in large-scale data mining, semantic technologies and visual analytics, to provide an innovative environment for exploring and making sense of scholarly data. In particular, Rexplore allows users:
To detect and make sense of important trends in research, such as, significant migrations of researchers from one area to another, the emergence of new topics, the evolution of communities within a particular area, and several others.
To identify a variety of interesting relations between researchers, e.g., recognizing authors who share similar research trajectories. These relations go well beyond the standard co-authorship links or relationships informed by social networks, which are commonly found in other systems.
To perform fine-grained expert search with respect to detailed multi-dimensional parameters.
To analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities identified on the basis of dynamic criteria.
An important aspect of Rexplore is that it does not rely on manually-generated taxonomies of research areas, which tend to be shallow and date very rapidly, but uses instead an innovative ontology population algorithm, Klink, which automatically constructs a semantic network of fine-grained research areas, linked by semantic relations, such as sameAs and subAreaOf. The use of Klink ensures a fine-grained handling of research areas and affords Rexplore a very high level of precision and recall in associating topics to publications and researchers.
Rexplore offers an advanced graphical interface, comprising a variety of innovative and fine grained visualizations, which support users in exploring authors, topics, and research communities. To support effective exploration, all graphical elements can be clicked on, thus enabling a seamless and contextualized navigation.
11 Jan 2021
14 Nov 2019
16 Sep 2019
10 Jan 2019
Meloni, A., Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2021) AIDA-Bot: A Conversational Agent to ExploreScholarly Knowledge Graphs, The International Semantic Web Conference, Online
Angioni, S., Salatino, A.A., Osborne, F., Birukou, A., Recupero, D. and Motta, E. (2021) Assessing Scientific Conferences through Knowledge Graphs, Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, Virtual, Online
Nayyeri, M., Cil, G., Vahdati, S., Osborne, F., Kravchenko, A., Angioni, S., Salatino, A.A., Recupero, D., Motta, E. and Lehmann, J. (2021) Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain, IEEE Access, 9, pp. 116002-116014
Salatino, A.A., Osborne, F. and Motta, E. (2021) CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics, International Journal on Digital Libraries, pp. (Early Access)
Danilo, D., Osborne, F., Recupero, D., Buscaldi, D. and Motta, E. (2021) Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain, Future Generation Computer Systems, 116, pp. 253-264