Learning OWL Class Expressions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{jl_2010/phd_thesis,
  title =        "Learning {OWL} Class Expressions",
  author =       "Jens Lehmann",
  school =       "University of Leipzig",
  year =         "2010",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://jens-lehmann.org/files/2010/phd_thesis.pdf",
  size =         "223 pages",
  abstract =     "With the advent of the Semantic Web and Semantic
                 Technologies, ontologies have become one of the most
                 prominent paradigms for knowledge representation and
                 reasoning. The popular ontology language OWL, based on
                 description logics, became a W3C recommendation in 2004
                 and a standard for modelling ontologies on the Web. In
                 the meantime, many studies and applications using OWL
                 have been reported in research and industrial
                 environments, many of which go beyond Internet usage
                 and employ the power of ontological modelling in other
                 fields such as biology, medicine, software engineering,
                 knowledge management, and cognitive systems.

                 However, recent progress in the field faces a lack of
                 well-structured ontologies with large amounts of
                 instance data due to the fact that engineering such
                 ontologies requires a considerable investment of
                 resources. Nowadays, knowledge bases often provide
                 large volumes of data without sophisticated schemata.
                 Hence, methods for automated schema acquisition and
                 maintenance are sought. Schema acquisition is closely
                 related to solving typical classification problems in
                 machine learning, e.g. the detection of chemical
                 compounds causing cancer. In this work, we investigate
                 both, the underlying machine learning techniques and
                 their application to knowledge acquisition in the
                 Semantic Web.

                 In order to leverage machine-learning approaches for
                 solving these tasks, it is required to develop methods
                 and tools for learning concepts in description logics
                 or, equivalently, class expressions in OWL. In this
                 thesis, it is shown that methods from Inductive Logic
                 Programming (ILP) are applicable to learning in
                 description logic knowledge bases. The results provide
                 foundations for the semi-automatic creation and
                 maintenance of OWL ontologies, in particular in cases
                 when extensional information (i.e. facts, instance
                 data) is abundantly available, while corresponding
                 intensional information (schema) is missing or not
                 expressive enough to allow powerful reasoning over the
                 ontology in a useful way. Such situations often occur
                 when extracting knowledge from different sources, e.g.
                 databases, or in collaborative knowledge engineering
                 scenarios, e.g. using semantic wikis. It can be argued
                 that being able to learn OWL class expressions is a
                 step towards enriching OWL knowledge bases in order to
                 enable powerful reasoning, consistency checking, and
                 improved querying possibilities. In particular, plugins
                 for OWL ontology editors based on learning methods are
                 developed and evaluated in this work.

                 The developed algorithms are not restricted to ontology
                 engineering and can handle other learning problems.
                 Indeed, they lend themselves to generic use in machine
                 learning in the same way as ILP systems do. The main
                 difference, however, is the employed knowledge
                 representation paradigm: ILP traditionally uses logic
                 programs for knowledge representation, whereas this
                 work rests on description logics and OWL. This
                 difference is crucial when considering Semantic Web
                 applications as target use cases, as such applications
                 hinge centrally on the chosen knowledge representation
                 format for knowledge interchange and integration. The
                 work in this thesis can be understood as a broadening
                 of the scope of research and applications of ILP
                 methods. This goal is particularly important since the
                 number of OWL-based systems is already increasing
                 rapidly and can be expected to grow further in the
                 future.

                 The thesis starts by establishing the necessary
                 theoretical basis and continues with the specification
                 of algorithms. It also contains their evaluation and,
                 finally, presents a number of application scenarios.
                 The research contributions of this work are
                 threefold:",
  abstract =     "The first contribution is a complete analysis of
                 desirable properties of refinement operators in
                 description logics. Refinement operators are used to
                 traverse the target search space and are, therefore, a
                 crucial element in many learning algorithms. Their
                 properties (completeness, weak completeness,
                 properness, redundancy, infinity, minimality) indicate
                 whether a refinement operator is suitable for being
                 employed in a learning algorithm. The key research
                 question is which of those properties can be combined.
                 It is shown that there is no ideal, i.e. complete,
                 proper, and finite, refinement operator for expressive
                 description logics, which indicates that learning in
                 description logics is a challenging machine learning
                 task. A number of other new results for different
                 property combinations are also proven. The need for
                 these investigations has already been expressed in
                 several articles prior to this PhD work. The
                 theoretical limitations, which were shown as a result
                 of these investigations, provide clear criteria for the
                 design of refinement operators. In the analysis, as few
                 assumptions as possible were made regarding the used
                 description language.

                 The second contribution is the development of two
                 refinement operators. The first operator supports a
                 wide range of concept constructors and it is shown that
                 it is complete and can be extended to a proper
                 operator. It is the most expressive operator designed
                 for a description language so far. The second operator
                 uses the light-weight language EL and is weakly
                 complete, proper, and finite. It is straight-forward to
                 extend it to an ideal operator, if required. It is the
                 first published ideal refinement operator in
                 description logics. While the two operators differ a
                 lot in their technical details, they both use
                 background knowledge efficiently.

                 The third contribution is the actual learning
                 algorithms using the introduced operators. New
                 redundancy elimination and infinity-handling techniques
                 are introduced in these algorithms. According to the
                 evaluation, the algorithms produce very readable
                 solutions, while their accuracy is competitive with the
                 state-of-the-art in machine learning. Several
                 optimisations for achieving scalability of the
                 introduced algorithms are described, including a
                 knowledge base fragment selection approach, a dedicated
                 reasoning procedure, and a stochastic coverage
                 computation approach.

                 The research contributions are evaluated on benchmark
                 problems and in use cases. Standard statistical
                 measurements such as cross validation and significance
                 tests show that the approaches are very competitive.
                 Furthermore, the ontology engineering case study
                 provides evidence that the described algorithms can
                 solve the target problems in practice. A major outcome
                 of the doctoral work is the DL-Learner framework. It
                 provides the source code for all algorithms and
                 examples as open-source and has been incorporated in
                 other projects.",
  notes =        "Some experiments on hybrid GP.

                 supervisors: Prof. Klaus-Peter F{\"a}hnrich, Dr.
                 S{\"o}ren Auer",
}

Genetic Programming entries for Jens Lehmann

Citations