Authors:
Walmir Oliveira Couto
1
;
2
;
Emerson Cordeiro Morais
2
and
Marcos Didonet Del Fabro
1
Affiliations:
1
C3SL Labs, Federal University of Paraná, Curitiba PR, Brazil
;
2
LADES Icibe, Federal Rural University of Amazon, Belém PA, Brazil
Keyword(s):
Classifying Unstructured Models, Model Recognition, Artificial Neural Network, MLP.
Abstract:
Models and metamodels created using model-based approaches have restrict conformance relations. However, there has been an increase of semi-structured or schema-free data formats, such as document-oriented representations, which are often persisted as JSON documents. Despite not having an explicit schema/metamodel, these documents could be categorized to discover their domain and to partially conform to a metamodel. Recent approaches are emerging to extract information or to couple modeling with cognification. However, there is a lack of approaches exploring semi-structured formats classification. In this paper, we present a methodology to analyze and classify JSON documents according to existing metamodels. First, we describe how to extract metamodels elements into a Multi-Layer Perceptron (MLP) network to be trained. Then, we translate the JSON documents into the input format of the encoded MLP. We present the step-by-step tasks to classify JSON documents according to existing meta
models extracted from a repository. We have conducted a series of experiments, showing that the approach is effective to classify the documents.
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