3 research outputs found
Towards Efficient Axiom Pinpointing of EL+ Ontologies
The EL family of Description Logics (DLs) has been the subject of interest in
recent years. On the one hand, these DLs are tractable, but fairly
inexpressive. On the other hand, these DLs can be used for designing different
classes of ontologies, most notably ontologies from the medical domain.
Unfortunately, building ontologies is error-prone. As a result, inferable
subsumption relations among concepts may be unintended. In recent years, the
problem of axiom pinpointing has been studied with the purpose of providing
minimal sets of axioms that explain unintended subsumption relations. For the
concrete case of EL and EL+, the most efficient approaches consist of encoding
the problem into propositional logic, specifically as a Horn formula, which is
then analyzed with a dedicated algorithm. This paper builds on this earlier
work, but exploits the important relationship between minimal axioms sets and
minimal unsatisfiable subformulas in the propositional domain. In turn, this
relationship allows applying a vast body of recent work in the propositional
domain to the concrete case of axiom pinpointing for EL and its variants. From
a practical perspective, the algorithms described in this paper are often
several orders of magnitude more efficient that the current state of the art in
axiom pinpointing for the EL family of DLs
Efficient MUS Enumeration of Horn Formulae with Applications to Axiom Pinpointing
The enumeration of minimal unsatisfiable subsets (MUSes) finds a growing
number of practical applications, that includes a wide range of diagnosis
problems. As a concrete example, the problem of axiom pinpointing in the EL
family of description logics (DLs) can be modeled as the enumeration of the
group-MUSes of Horn formulae. In turn, axiom pinpointing for the EL family of
DLs finds important applications, such as debugging medical ontologies, of
which SNOMED CT is the best known example. The main contribution of this paper
is to develop an efficient group-MUS enumerator for Horn formulae, HGMUS, that
finds immediate application in axiom pinpointing for the EL family of DLs. In
the process of developing HGMUS, the paper also identifies performance
bottlenecks of existing solutions. The new algorithm is shown to outperform all
alternative approaches when the problem domain targeted by group-MUS
enumeration of Horn formulae is axiom pinpointing for the EL family of DLs,
with a representative suite of examples taken from different medical
ontologies
Axiom Pinpointing Using an Assumption-Based Truth Maintenance System
The problem of axiom pinpointing [1, 22], that is, finding the minimal set of axioms responsible for an unwanted consequence, is an important problem in ontology debugging. One approach to identifying the axioms responsible for an unwanted consequence is to trace dependencies between inferences leading to the consequence. Several author