Intelligent text classification system based on self-administered ontology

Over the last couple of decades, web classification has gradually transitioned from a syntax- to semantic-centered approach that classifies the text based on domain ontologies. These ontologies are either built manually or populated automatically using machine learning techniques. A prerequisite condition to build such systems is the availability of ontology, which may be either full-fledged domain ontology or a seed ontology that can be enriched automatically. This is a dependency condition for any given semantics-based text classification system. We share the details of a proof of concept of a web classification system that is self-governed in terms of ontology population and does not require any prebuilt ontology, neither full-fledged nor seed. It starts from a user query, builds a seed ontology from it, and automatically enriches it by extracting concepts from the downloaded documents only. The evaluated parameters like precision (85{\%}), accuracy (86{\%}), AUC (convex), and MCC (high positive) demonstrate the better performance of the proposed system when compared with similar automated text classification systems.

Anahtar Kelimeler:

Ontology, support vector machine, resource description framework, text classification

Intelligent text classification system based on self-administered ontology

Keywords:

Ontology, support vector machine, resource description framework, text classification,

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area, our framework is independent of any specific domain area and purely focused towards user query, picking up the domain at run time. Prefixing of the domain also requires [12] to generate seed ontology first and then proceed towards the enrichment process. Our framework does not have such dependencies, which results in making it a ‘self-governed’ learning system.
Conclusion and future work
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