- Titel
- Maximum Entropy Reasoning via Model Counting in (Description) Logics that Count
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-963356
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 25-01
- Erstveröffentlichung
- 2025
- DOI
- https://doi.org/10.25368/2025.015
- Abstract (EN)
- In previous work it was shown that the logic ALC^ME , which extends the description logic (DL) ALC with probabilistic conditionals, has domain-lifted inference. Here, we extend this result from the base logic ALC to two logics that can count, the two-variable fragment C2 of first-order logic (FOL) with counting quantifiers, and the DL ALCSCC, which is not a fragment of FOL. As an auxiliary result, we prove that model counting in ALCSCC can be realized in a domain-liftable way.
- This is an extended version of an article accepted at the 38th International FLAIRS Conference (FLAIRS-38), 2025.
- Zitiert in
- Proceedings of the 38th International FLAIRS Conference (FLAIRS-38), 2025
- Freie Schlagwörter (DE)
- Beschreibung Logik (DL), Probabilistische Logik, Zählquantoren, Modellzählung
- Freie Schlagwörter (EN)
- Description Logic, Probabilistic Logic, Counting Quantifiers, Model Counting
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Herausgeber (Institution)
- Technische Universität Dresden
- Förder- / Projektangaben
- Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-963356
- Veröffentlichungsdatum Qucosa
- 10.04.2025
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0