Fine-Grained Diagnostics of Ontologies with Assurance - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 97

A query optimization strategy is crucial to obtain reasonable performance over queries against
ontology data models, especially if they are done over a highly distributed architecture.
However, using the answer set programming paradigm, we are able to model the spatial
and temporal context models and define the relationships between classes and individuals
(rules) and generating the answer sets using advanced solvers that support binder splitting,
backjumping and all other features. The statements can be optimized to find the maximal or
the minimal answer set of the logic program. The statement can be weighted or not, therefore
weights can be omitted. If there are several minimize maximize statements in the logic
program the latter will be preferred. The difference between the performance of ASP solvers
and other existed ontology query languages is clearly high.The estimation of the cardinality
of a query usually is used to approximate the data transfer times of the result set, as part of
the estimation of the total cost of executing a query.
6. Conclusion
In the light of the growing importance and complexity of nowadays ontologies, tools that
support debugging of reasoning systems are as well an increasing demand. In many cases,
particularly for legacy systems, complex rules and facts that contribute to an inconsistency
can present themselves as mere "black-boxes" to the human engineer. Fine-grained diagnosis
targets at turning such black-boxes into white-boxes by enhancing the accuracy of the
diagnosis up to the smallest grammatical construct that the language permits. Even if an
ontology is consistent, the user should not be overloaded with (logically correct) answers,
without a tool for choosing among them. Besides taking the degree of satisfaction as
a selection criterion, the concept of assurance yields answers that are provably optimal
according to a user-definable goal. Hence, we can have the ontology give us the answer that
will maximize the benefit for the user. The process is cyclic in the sense that assurance hinges
on consistency and vice versa. Moreover, retrieving answers timely calls for optimization,
which in turn can make rules and facts even more complicated, again calling for fine-grained
diagnostics. This chapter is hoped to provide useful starting points to enter this (implicit)
cycle at any point for future research.
7. References
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