Virtual Talk with Danilo Dessi


Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather inefficient. Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal of making it machine-readable and understandable. This talk will give an overview of existing knowledge graph solutions within the scholarly domain, present an automatic approach to describe research papers based on their textual content, and introduce the audience to the Computer Science Knowledge Graph (CS-KG). The resulting knowledge graphs can pave the way for a new generation of AI systems able to reuse and reason on this knowledge and support the work of researchers by suggesting relevant literature, generating novel hypotheses, study the state of the art, and verifying scientific facts.



Danilo Dessì obtained his M.Sc. and Ph.D. at the University of Cagliari, Italy. He is a Senior Researcher at the Knowledge Technologies for the Social Sciences Department at GESIS Leibniz Institute for the Social Sciences in Cologne, Germany. Before, he was a postdoctoral researcher at FIZ-Karlsruhe and assistant professor at the University of Cagliari. His research interests include Artificial Intelligence, Information Extraction, Knowledge Graphs, Science of Science, and Semantic Web technologies. Recently his research has targeted the extraction and construction of large-scale knowledge graphs to make sense of data and infer knowledge, with the long-term goal of building intelligent systems and services for the scientific community. An output of his research is the Computer Science Knowledge Graph, a knowledge base that describes 10M entities among methods, tasks, materials, and metrics in the Computer Science domain.

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