Rexplore

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.

Angelo Salatino

KMi Reporter

Jane Whild

Angelo Salatino

Angioni, S., Salatino, A.A., Osborne, F., Birukou, A., Recupero, D. and Motta, E. (2022) Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature, 21st International Semantic Web Conference, ISWC 2022, Hangzhou, China

Dessí, D., Osborne, F., Recupero, D., Buscaldi, D. and Motta, E. (2022) CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science, ISWC, Online

Dessí, D., Osborne, F., Recupero, D., Buscaldi, D. and Motta, E. (2022) SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain, Knowledge-Based Systems, Elsevier

Manghi, P., Mannocci, A., Osborne, F., Sacharidis, D., Salatino, A.A. and Vergoulis, T. (2022) Sci-K 2022 - International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, WWW '22: The ACM Web Conference 2022, Lyon, France (Virtual)

Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2022) AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry, Quantitative Science Studies, pp. (In Press)