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.2022.4
URN: urn:nbn:de:0030-drops-160021
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16002/
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Boschin, Armand ; Jain, Nitisha ; Keretchashvili, Gurami ; Suchanek, Fabian

Combining Embeddings and Rules for Fact Prediction (Invited Paper)

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OASIcs-AIB-2022-4.pdf (0.8 MB)

Abstract

Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction.

BibTeX - Entry

@InProceedings{boschin_et_al:OASIcs.AIB.2022.4,
  author =	{Boschin, Armand and Jain, Nitisha and Keretchashvili, Gurami and Suchanek, Fabian},
  title =	{{Combining Embeddings and Rules for Fact Prediction}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{4:1--4:30},
  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 =		{https://drops.dagstuhl.de/opus/volltexte/2022/16002},
  URN =		{urn:nbn:de:0030-drops-160021},
  doi =		{10.4230/OASIcs.AIB.2022.4},
  annote =	{Keywords: Rule Mining, Embeddings, Knowledge Bases, Deep Learning}
}

Keywords: Rule Mining, Embeddings, Knowledge Bases, Deep Learning
Collection: International Research School in Artificial Intelligence in Bergen (AIB 2022)
Issue Date: 2022
Date of publication: 25.05.2022

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