Cone Semantics for Logics with Negation

Authors: Özgür Lütfü Özçep, Mena Leemhuis, Diedrich Wolter

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This paper presents an embedding of ontologies expressed in the ALC description logic into a realvalued vector space, comprising restricted existential and universal quantifiers, as well as concept negation and concept disjunction. Our main result states that an ALC ontology is satisfiable in the classical sense iff it is satisfiable by a partial faithful geometric model based on cones. The paper is structured with definitions (Section 3, 4), propositions and proofs (e.g., Proposition 1, 2, 3, 4, 5, Proof sketch), and theoretical constructions rather than empirical evaluations on datasets.
Researcher Affiliation Academia Ozg ur L utf u Ozc ep1 , Mena Leemhuis1 and Diedrich Wolter2 1University of L ubeck, Germany 2University of Bamberg, Germany oezcep@ifis.uni-luebeck.de, mena.leemhuis@student.uni-luebeck.de, diedrich.wolter@uni-bamberg.de
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks. It describes mathematical constructions and proofs.
Open Source Code No The paper does not provide any statement or link indicating the release of open-source code for the methodology described.
Open Datasets No The paper is theoretical and does not use or reference any specific public datasets for training. It describes a hypothetical 'incremental learning scenario' but does not conduct it.
Dataset Splits No The paper is theoretical and does not involve empirical validation on datasets, thus no dataset split information is provided.
Hardware Specification No The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are provided.
Software Dependencies No The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are provided.
Experiment Setup No The paper is theoretical and does not describe empirical experiments with hyperparameters or specific training configurations.