![](bg4.png)
Once we have modelled all fuzzy properties, they
can be used in fuzzy rules for classification of sci-
entific documents. In this experiment, fuzzy rules
should contain C0 to C5 properties in the antecedent
part, and a class in the consequent that corresponds to
the inferred class of the ontology. In order to auto-
matically learn these rules, we have applied the Wang
and Mendel method (Wang and Mendel, 1992) over
the document-cluster matrix. As a result, we have ob-
tained nineteen rules in total, which were modelled
using Jena rule syntax. Due to space limitations, List-
ing 2 shows only two of them, just to illustrate how
they are specified according to the proposed model.
[rule1: (?x c0 ’low’), (?x c1 ’low’), (?x c2 ’high’), (?x c3 ’low’),
(?x c4 ’low’), (?x c5 ’low’) −> (?x rdf:type Machine_Learning)]
[rule2: (?x c0 ’low’), (?x c1 ’medium’), (?x c2 ’low’), (?x c3 ’low’),
(?x c4 ’medium’), (?x c5 ’low’) −> (?x rdf:type Fuzzy_Logics)]
Listing 2: Fuzzy rules generated by Wang and Mendel.
At this stage, fuzzy reasoning methods can be ap-
plied to classify scientific documents regarding Ar-
tificial Intelligence subareas. When a new docu-
ment needs to be classified, it passes through the pre-
processing step in order to obtain its fuzzy property
values. After that, the user chooses a fuzzy reasoning
method (classical or general) to obtain the inferred
class. In our tests, general method performed better
than classical one, as the former considers all fired
rules by combining their association degree. How-
ever, this research does not intend to evaluate the
best method, our goal is making them available for
ontology-based applications that require the represen-
tation of fuzzy properties and classification rules.
Concluding this case study, we have observed
that the proposed model contributed to represent
the vagueness present in textual information content.
Moreover, fuzzy reasoning methods can automati-
cally infer the classes of new documents, an informa-
tion that can be analyzed by document retrieval sys-
tems for improving query results.
5 CONCLUSIONS AND FUTURE
WORK
We have proposed a model for representing fuzzy
properties and fuzzy rules in ontologies, which can
be instantiated using any traditional ontology lan-
guage that models basic representational primitives.
Furthermore, fuzzy reasoning methods (classical and
general) were implemented in order to support clas-
sification of new instances according to their fuzzy
property values. The results obtained from the study
case demonstrate that the proposed model contributed
to manage vagueness on text documents, representing
a good approach not only for classification but also
for organization of the text documents.
Finally, we plan to incorporate more fuzzy set
concepts, such as linguistic hedges, fuzzy relations
etc. We intend to support other types of rules and their
fuzzy reasoning methods (e.g. Mamdani and Larsen
methods), as well as defuzzification methods.
ACKNOWLEDGEMENTS
We thank CAPES and INEP agencies for supporting
this research, inside the scope of WebPIDE project.
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A MODEL FOR REPRESENTING VAGUE LINGUISTIC TERMS AND FUZZY RULES FOR CLASSIFICATION IN
ONTOLOGIES
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