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Reasoning in Knowledge Graphs (Invited Paper)

Authors Ricardo Guimarães , Ana Ozaki



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Author Details

Ricardo Guimarães
  • University of Bergen, Norway
Ana Ozaki
  • University of Bergen, Norway

Acknowledgements

Part of this work has been done in the context of CEDAS (Center for Data Science, University of Bergen, Norway).

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Ricardo Guimarães and Ana Ozaki. Reasoning in Knowledge Graphs (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 2:1-2:31, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.AIB.2022.2

Abstract

Knowledge Graphs (KGs) are becoming increasingly popular in the industry and academia. They can be represented as labelled graphs conveying structured knowledge in a domain of interest, where nodes and edges are enriched with metaknowledge such as time validity, provenance, language, among others. Once the data is structured as a labelled graph one can apply reasoning techniques to extract relevant and insightful information. We provide an overview of deductive and inductive reasoning approaches for reasoning in KGs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • Knowledge Graphs
  • Description Logics
  • Knowledge Graph Embeddings

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