License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.AIB.2022.2
URN: urn:nbn:de:0030-drops-160005
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GuimarĂ£es, Ricardo ; Ozaki, Ana

Reasoning in Knowledge Graphs (Invited Paper)

OASIcs-AIB-2022-2.pdf (1.0 MB)


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.

BibTeX - Entry

  author =	{Guimar\~{a}es, Ricardo and Ozaki, Ana},
  title =	{{Reasoning in Knowledge Graphs}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{2:1--2:31},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-160005},
  doi =		{10.4230/OASIcs.AIB.2022.2},
  annote =	{Keywords: Knowledge Graphs, Description Logics, Knowledge Graph Embeddings}

Keywords: Knowledge Graphs, Description Logics, Knowledge Graph Embeddings
Collection: International Research School in Artificial Intelligence in Bergen (AIB 2022)
Issue Date: 2022
Date of publication: 25.05.2022

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