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Domain Based Ontology and Automated Text Categorization Based on Improved Term Frequency – Inverse Document Frequency

Authors: Sukanya Ray; Nidhi Chandra;

Domain Based Ontology and Automated Text Categorization Based on Improved Term Frequency – Inverse Document Frequency

Abstract

In recent years there has been a massive growth in textual information in textual information especially in the internet. People now tend to read more e-books than hard copies of the books. While searching for some topic especially some new topic in the internet it will be easier if someone knows the pre- requisites and post- requisites of that topic. It will be easier for someone searching a new topic. Often the topics are found without any proper title and it becomes difficult later on to find which document was for which topic. A text categorization method can provide solution to this problem. In this paper domain based ontology is created so that users can relate to different topics of a domain and an automated text categorization technique is proposed that will categorize the uncategorized documents. The proposed idea is based on Term Frequency - Inverse Document Frequency (tf -idf) method and a dependency graph is also provided in the domain based ontology so that the users can visualize the relations among the terms.

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    6
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
gold