Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general (#P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers (C²) is domain-liftable. However, many properties of real-world data, like acyclicity in citation networks and connectivity in social networks, cannot be modeled in C², or first order logic in general. In this work, we expand the domain liftability of C² with multiple such properties. We show that any C² sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of counting by splitting. Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.
en
dc.language.iso
en
-
dc.publisher
ELSEVIER
-
dc.relation.ispartof
Artificial Intelligence
-
dc.subject
Lifted inference
en
dc.subject
Enumerative combinatorics
en
dc.subject
Weighted model counting
en
dc.subject
First order logic
en
dc.subject
Counting quantfiers
en
dc.subject
Statistical relational learning
en
dc.subject
Directed acyclic graphs
en
dc.subject
Connected graphs
en
dc.title
Lifted inference beyond first-order logic
en
dc.type
Article
en
dc.type
Artikel
de
dc.contributor.affiliation
Fondazione Bruno Kessler, Italy
-
dc.contributor.affiliation
Fondazione Bruno Kessler, Italy
-
dc.type.category
Original Research Article
-
tuw.container.volume
342
-
tuw.journal.peerreviewed
true
-
tuw.peerreviewed
true
-
wb.publication.intCoWork
International Co-publication
-
tuw.researchTopic.id
I1
-
tuw.researchTopic.name
Logic and Computation
-
tuw.researchTopic.value
100
-
dcterms.isPartOf.title
Artificial Intelligence
-
tuw.publication.orgunit
E194-06 - Forschungsbereich Machine Learning
-
tuw.publisher.doi
10.1016/j.artint.2025.104310
-
dc.date.onlinefirst
2025-02-24
-
dc.identifier.articleid
104310
-
dc.identifier.eissn
1872-7921
-
dc.description.numberOfPages
29
-
tuw.author.orcid
0000-0001-6700-4311
-
tuw.author.orcid
0009-0004-0420-0453
-
tuw.author.orcid
0000-0003-4812-1031
-
wb.sci
true
-
wb.sciencebranch
Informatik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.value
100
-
item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
-
item.languageiso639-1
en
-
item.fulltext
no Fulltext
-
item.grantfulltext
none
-
item.openairetype
research article
-
item.cerifentitytype
Publications
-
crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.dept
Fondazione Bruno Kessler
-
crisitem.author.dept
Fondazione Bruno Kessler
-
crisitem.author.orcid
0009-0004-0420-0453
-
crisitem.author.orcid
0000-0003-4812-1031
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering