Fine-Grained Diagnostics of Ontologies with Assurance - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 81

recommendations is a hardly considered problem, and a solution proposal for it is the core
contribution of this chapter. In the following, we discuss both problems separately, tackling
the correctness issue with a refined form of diagnosis. Optimality of recommendation will be
achieved with a post-processing step, to be described in section 4.
2. Ontological reasoning in practice
The most recent developments for ontology design is ontology web language (OWL), with its
dialects OWL DL (particularly optimized for description logics) and OWL Lite. It has various
sets of operators and it is based on different logical models which simplify the description
of the concepts. The semantic web rule language is based on OWL DL, OWL Lite, and the
Rule Markup Language (cf. Snidaro et al. (2008)). All rules are expressed in terms of OWL
concepts (classes, properties, individuals). This means that rules can be used to extract new
knowledge from existing OWL ontologies. Therefore complex rules must be transformed to
the requirements of SWRL (cf. Matheus et al. (2005)). Also, there are no inference engines that
fully support SWRL up to now.
The first step in building a context model is to specify the desired system behavior. The
developer then lists a set of possible scenarios, where each scenario is a relationship between
entities to be observed. The requirements for modeling information contexts (cf. Fuchs (2008)):
Applicability: The model must restrict the domain of application.
Traceability: The model must provide support for recording of provenance and processing
of information.
Inference: The model should include tools that permit the definition of new contextual
categories and facts on the basis of low-order context.
Re-usability: The model should allow re-usability in other independent modeling tasks.
Flexibility: The model should not be easily changeable to extend the ontology.
Completeness: The model should cover all relevant concepts and properties.
Redundancy: The model should not contain a lot of defined instances that have the same
properties.
Context reasoning extends context information implicitly by introducing deduced context
derived from other types of context. It is a perfect solution to resolve context inconsistency.
In the light of the key role that ontologies play in many applications, it is essential to provide
tools and services to support users in designing and maintaining high quality ontologies. This
calls for:
1. All named classes can be instantiated (i.e. there are no "abstract" classes)
2. Correctly captured intuitions of domain experts
3. Minimal redundancy and no unintended synonyms
4. Rich axiomatization and (sufficient) detail
Answer queries over ontology classes and instances, e.g.:
1. Find more general/specific classes
2. Retrieve individuals/tuples matching a given query
Context interpreters consist of context reasoning engines and context knowledge-bases
(context KB). The context reasoning engines provide the inference services including inferring
contexts, resolving context conflicts (basically a problem of diagnostics ) and maintaining the
consistency of context knowledge-bases. Different rules for consistency can be specified and