127 research outputs found
Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?
Abstract
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies
Extracting Rules from Neural Networks with Partial Interpretations
We investigate the problem of extracting rules, expressed in Horn logic, from
neural network models. Our work is based on the exact learning model, in which
a learner interacts with a teacher (the neural network model) via queries in
order to learn an abstract target concept, which in our case is a set of Horn
rules. We consider partial interpretations to formulate the queries. These can
be understood as a representation of the world where part of the knowledge
regarding the truthiness of propositions is unknown. We employ Angluin s
algorithm for learning Horn rules via queries and evaluate our strategy
empirically
Extracting Rules from Neural Networks with Partial Interpretations
We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthness of propositions is unknown. We employ Angluin’s algorithm for learning Horn rules via queries and evaluate our strategy empirically.publishedVersio
Semiring Provenance for Lightweight Description Logics
We investigate semiring provenance--a successful framework originally defined
in the relational database setting--for description logics. In this context,
the ontology axioms are annotated with elements of a commutative semiring and
these annotations are propagated to the ontology consequences in a way that
reflects how they are derived. We define a provenance semantics for a language
that encompasses several lightweight description logics and show its
relationships with semantics that have been defined for ontologies annotated
with a specific kind of annotation (such as fuzzy degrees). We show that under
some restrictions on the semiring, the semantics satisfies desirable properties
(such as extending the semiring provenance defined for databases). We then
focus on the well-known why-provenance, which allows to compute the semiring
provenance for every additively and multiplicatively idempotent commutative
semiring, and for which we study the complexity of problems related to the
provenance of an axiom or a conjunctive query answer. Finally, we consider two
more restricted cases which correspond to the so-called positive Boolean
provenance and lineage in the database setting. For these cases, we exhibit
relationships with well-known notions related to explanations in description
logics and complete our complexity analysis. As a side contribution, we provide
conditions on an ELHI_bot ontology that guarantee tractable reasoning.Comment: Paper currently under review. 102 page
A Model for Learning Description Logic Ontologies Based on Exact Learning
We investigate the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries posed to an oracle. We consider membership queries of the form “is a tuple a of individuals a certain answer to a data retrieval query q in a given ABox and the unknown target ontology?” and completeness queries of the form “does a hypothesis ontology entail the unknown target ontology?” Given a DL L and a data retrieval query language Q, we study polynomial learnability of ontologies in L using data retrieval queries in Q and provide an almost complete classification for DLs that are fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries. Some results are proved by non-trivial reductions to learning from subsumption examples
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