Reasoning in SHIQ with Axiom- and Concept-Level Standpoint Modalities

Standpoint logic is a recently proposed modal logic framework that is well-suited for multiperspective reasoning and ontology integration. For this reason, combinations of standpoint logic with description logics (DLs) are of special interest. Prior work has shown that it is possible to add standpoints to numerous decidable fragments of first-order logics - including very expressive DLs up to SROIQbs - while preserving their reasoning complexity, so long as standpoint modalities are limited to the axiom level. A more expressive tighter modal integration, where standpoint modalities are also allowed to occur in concept expressions, has so far only been investigated for the much less expressive DL EL+. In this paper, we push this line of research showing that the DL SHIQ allows for a tight modal integration with standpoints without compromising its ExpTime reasoning complexity. The core insight toward this result is that any satisfiable knowledge base admits a model with only polynomially many worlds, an argument which requires a rather elaborate model-theoretic construction. This allows us to establish a polynomial equisatisfiable translation into plain SHIQ which, beyond showing the theoretical result, enables us to use highly optimised OWL reasoners to provide practical reasoning support for ontology languages extended by standpoint modelling. We complement our findings with the observation that our techniques would fail upon adding the modelling feature of nominals to the underlying DL.

Authors

Institutions

Publication Details

Published
2024-10-26
DOI
https://doi.org/10.24963/kr.2024/36
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reasoning in SHIQ with Axiom- and Concept-Level Standpoint Modalities

Sebastian Rudolph, Lucía Gómez Álvarez
Semantic Web and Ontologies
article

Reasoning in SHIQ with Axiom- and Concept-Level Standpoint Modalities

Sebastian Rudolph, Lucía Gómez Álvarez
article en

Abstract

Standpoint logic is a recently proposed modal logic framework that is well-suited for multiperspective reasoning and ontology integration. For this reason, combinations of standpoint logic with description logics (DLs) are of special interest. Prior work has shown that it is possible to add standpoints to numerous decidable fragments of first-order logics - including very expressive DLs up to SROIQbs - while preserving their reasoning complexity, so long as standpoint modalities are limited to the axiom level. A more expressive tighter modal integration, where standpoint modalities are also allowed to occur in concept expressions, has so far only been investigated for the much less expressive DL EL+. In this paper, we push this line of research showing that the DL SHIQ allows for a tight modal integration with standpoints without compromising its ExpTime reasoning complexity. The core insight toward this result is that any satisfiable knowledge base admits a model with only polynomially many worlds, an argument which requires a rather elaborate model-theoretic construction. This allows us to establish a polynomial equisatisfiable translation into plain SHIQ which, beyond showing the theoretical result, enables us to use highly optimised OWL reasoners to provide practical reasoning support for ontology languages extended by standpoint modelling. We complement our findings with the observation that our techniques would fail upon adding the modelling feature of nominals to the underlying DL.

Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), German Research Centre for Artificial Intelligence (DE), Centre Inria de l'Université Grenoble Alpes (FR), Laboratoire d'Informatique de Grenoble (FR), Center for Scalable Data Analytics and Artificial Intelligence (DE), Technische Universität Dresden (DE), Université Grenoble Alpes (FR)
Quality Education
Openalex Percentile: Top 83%
Semantic Web and Ontologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.