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Unveiling Scholarly Communities over Knowledge Graphs

Paper accepted in the 22nd International Conference on Theory and Practice of Digital Libraries, 2018
: Vahdati, Sahar; Palma, Guillermo; Nath, Rahul Jyoti; Christoph Lange; Auer, Soeren; Vidal, Maria-Esther

Fulltext urn:nbn:de:0011-n-5126576 (1.8 MByte PDF)
MD5 Fingerprint: 62f174e8f2588595c75b041cca38ff6f
Created on: 19.10.2018

Fulltext ()

Online im WWW, 2018, arXiv:1807.06816, 12 pp.
International Conference on Theory and Practice of Digital Libraries (TPDL) <22, 2018, Porto>
European Commission EC
Horizon 2020; 727658; iASiS
Conference Paper, Electronic Publication
Fraunhofer IAIS ()

Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present Korona, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks. Korona implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly e ntities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks. Results observed from our empirical evaluation suggest that exploiting semantics in scholarly knowledge graphs enables the identification of previously unknown relations between researchers. By extending the ontology, these observations can be generalized to other scholarly entities, e.g., articles or institutions, for the prediction of other scholarly patterns, e.g., co-citations or academic collaboration.