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Combining Embeddings and Rules for Fact Prediction (Invited Paper)

Authors Armand Boschin, Nitisha Jain, Gurami Keretchashvili, Fabian Suchanek

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Armand Boschin
  • Télécom Paris, Institut Polytechnique de Paris, France
Nitisha Jain
  • Hasso Plattner Institute, University of Potsdam, Germany
Gurami Keretchashvili
  • Télécom Paris, Institut Polytechnique de Paris, France
Fabian Suchanek
  • Télécom Paris, Institut Polytechnique de Paris, France

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Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek. Combining Embeddings and Rules for Fact Prediction (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 4:1-4:30, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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.

Subject Classification

ACM Subject Classification
  • Information systems → Information systems applications
  • Rule Mining
  • Embeddings
  • Knowledge Bases
  • Deep Learning


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