Fine-Grained Diagnostics of Ontologies with Assurance - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 96

User

Recommendation

Sampler

Nash-Equlibrium

query

Ontology

PS 1
(set of solutions/
answer-set)

Game-Database

Fig. 3. Reasoning with game-theoretic assurance

Grounder Solver

Problem
representation

Intermediate
representation

Variable
assignment

Fig. 4. Architecture of an ASP System (cf. Gebser (2008))

3. set up a game-matrix and solve for its equilibrium,
4. sample from the equilibrium profile and return the (so randomly selected)

recommendation to the user.
From this process, it is evident that ontology and query optimization are major concerns, and
therefore receive attention in the next section.

5. Maximizing reasoning performance
Optimization is the act of design and developing systems to take maximum advantage of the
resources available. Ontology optimization formally defines the vocabularies in describing
optimization methods and their configurations aiming to provide knowledge guidance in
optimization selection and configuration Tao et al. (2004). Query optimization over an
ontology consider the possible query plans to determine which of those plans will be most
efficient. An ASP system usually consists of a grounder a grounder and a solver (see figure 4).
First, a grounder translate A logic program in the language of AnsProlog s a non-ground
problem description into a propositional program, which can be processed by the second
component of the system, the solver (cf. Gebser (2008)).
There are algorithms that can be used for an effective optimization strategy for queries on
knowledge databases query optimization.
One of the hardest problems in query optimization is the accurate estimation of the costs of
alternative query plans. Cardinality estimation depend on estimates of the selection factor
of predicates in the query. 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 (cf. Gebser (2008)). As an example a maximal complete query pattern
path consists of a maximal query pattern path and a set of value constraints (cf. Shironoshita
et al. (2007)). The total cardinality of such a path is obtained by calculating the product of the
cardinality estimate of its maximal query path with all the value ratios for every variable in
the query pattern path (cf. Shironoshita et al. (2007)).
In a highly distributed architecture where data in different locations connected through
the internet, this is the most critical aspect of query execution time and the speed of the
connections and the amount of data plays an important role.