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Are Quantified Boolean Formulas Hard for Reason-Able Embeddings?


[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • artificial intelligence
  • neural-symbolic reasoning
  • knowledge representation
  • description logics

EN We aim to establish theoretical boundaries for the applicability of reason-able embeddings, a recently proposed method employing a transferable neural reasoner to shape a latent space of knowledge graph embeddings. Since reason-able embeddings rely on the ALC description logic, we construct a dataset of the hardest concepts in ALC by translating quantified boolean formulas (QBF) from QBFLIB, a benchmark for QBF solvers. We experimentally show the dataset is hard for a symbolic reasoner FaCT++, and analyze the results of reasoning with reason-able embeddings, concluding that the dataset is too hard for them.

Pages (from - to)

343 - 348





Progress in Polish Artificial Intelligence Research 4

Presented on

4th Polish Conference on Artificial Intelligence PP-RAI'2023, 24-26.04.2023, Łódź, Polska

License type

dla wszystkich w zakresie dozwolonego użytku

Open Access Mode

open repository

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / chapter


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