Fine-Grained Diagnostics of Ontologies with Assurance - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 79

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Fine-Grained Diagnostics of Ontologies
with Assurance

Stefan Rass, Fadi Al Machot and Kyandoghere Kyamakya
Alpen-Adria Universität Klagenfurt
Austria

1. Introduction
Description logics (DL) is a class of logics for knowledge modeling, which are derived from
semantic networks. Essentially they are to be understood as accessible fragments of predicate
logic of first order, allowing strong expressions to be formulated.
Other description logics permit strong (complex) expressions in a very compact
representation. For description logics, there are special inputs and intuitive notation that
facilitates the handling of them substantially.
Modeling in expert systems is very important, especially in systems within highly complex
domains where spatial and temporal data needs to be modeled. The ontology model permits
the use of a reasoner that can check definitions of the statements in the ontology for consistency
against each other. It can also recognize which concepts are the best for which definitions,
pursuing an optimal solution in terms of size, speed, etc. This is particularly helpful when
dealing with multiple classes hierarchies, therefore expert systems permit creating complex
concepts from definitions of simpler ones. So, an ontology is an engineering artifact or is a
formal representation of knowledge as a set of concepts within a domain, it often includes
classification based information and constraints capturing the knowledge about the domain
(cf. Kohler et al. (2003)).
Rule-based systems are successfully applied across a lot of domains. The interest in ontologies
has become stronger to develop a common rule base that could be computed by different rule
engines. This effort has led to the development of several rule languages such as the rule
markup language (RuleML), the semantic web rule language (SWRL), Metalog, ISO Prolog, and
many others.
Beside the weaknesses of SWRL are the weaknesses of the SPARQL protocol and RDF Query
Language (SPARQL), where RDF is the acronym for resource description framework (see World
Wide Web Consortium (2010)), to query an ontology, which requires the query writer to
understand the data structure of the RDF resources. This understanding can be derived from
eye parsing where sometimes the RDF or OWL ontology are large and the human being is not
able to follow any more. This can become a major obstacle when debugging or extending an
existing ontology.
Other computing paradigms, such as constraint satisfaction, quantified boolean formulas
(QBF), or first order logic (FOL), do not naturally offer the powerful expressive possibilities
to define our knowledge database of to model the spatial and context models. In general the
tasks posed in the constraint satisfaction paradigm are computationally intractable (NP-hard).