[Thesis]. Manchester, UK: The University of Manchester; 2017.
In computer science, an ontology is a machine-processable representation of knowledge
about some domain. Ontologies are encoded in ontology languages, such as the Web Ontology
Language (OWL) based on Description Logics (DLs). An ontology is a set of logical
statements, called axioms. Some axioms make universal statements, e.g. all fathers
are men, while others record data, i.e. facts about specific individuals, e.g. Bob
is a father. A set of universal statements is called TBox, as it encodes terminology,
i.e. schema-level conceptual relationships, and a set of facts is called ABox, as
it encodes instance-level assertions.
Ontologies are extensively developed and widely used in domains such as biology and
medicine. Manual engineering of a TBox is a difficult task that includes modelling
conceptual relationships of the domain and encoding those relationships in the ontology
language, e.g. OWL. Hence, it requires the knowledge of domain experts and skills
of ontology engineers combined together. In order to assist engineering of TBoxes
and potentially automate it, acquisition (or induction) of axioms from data has attracted
research attention and is usually called Ontology Learning (OL).
This thesis investigates the problem of OL from general principles. We formulate it
as General Terminology Induction that aims at acquiring general, expressive TBox axioms
(called general terminology) from data. The thesis addresses and investigates in depth
two main questions: how to rigorously evaluate the quality of general TBox axioms
and how to efficiently construct them. We design an approach for General Terminology
Induction and implement it in an algorithm called DL-Miner. We extensively evaluate
DL-Miner, compare it with other approaches, and run case studies together with domain
experts to gain insight into its potential applications.
The thesis should be of interest to ontology developers seeking automated means to
facilitate building or enriching ontologies. In addition, as our experiments show,
DL-Miner can deliver valuable insights into the data, i.e. can be useful for data
analysis and debugging.