Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento
Resumen
Large growing knowledge bases have been an interesting field in many researches in the
past few years. Most techniques focus on constructing algorithms to help a Knowledge
Base (KB) automatically (or semi automatically) expands. However, many tools used to expand
the KBs can extract incomplete or incorrect data, turning the KB inconsistent. In this
way, this work has the objective to expand large knowledge bases as well as detect inconsistencies
on them. To accomplish that, an association rule mining algorithm and temporal
correlation are used. Applying an algorithm to extract association rules in large knowledge
bases, the missing value problem need to be considered, once these bases grow day to day,
and do not have all of the data. Therefore, a new parameter was created to perform the
support calculation, the MSC parameter, to deal with missing values. Besides, a major problem
on using association rules is the effort spent to analyze each extracted rule. Thus,
this work developed ER component, which eliminates redundant and irrelevant association
rules. Each valid rule is used by TARE component with the purpose of detecting inconsistencies.
TARE introduces the concept of STARs (specific temporal association rules),
which are used to detect possible inconsistencies. Each relevant STAR is used as an input
to TCI component in order to get temporal correlations to (i) detect possible inconsistencies
and (ii) to help populating the KB. Experiments showed that the association rules and the
temporal correlation are capable to expand the knowledge base, decreasing the amount of
missing values. Moreover, both TARE and TCI components were efficient in the process of
detecting possible inconsistencies in the data set. Finally, the ER component reduced the
number of rules in more then 30% without any lost in the process of populating the KB.
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