Abstract

Knowledge documents are growing remarkably to serve the organization for information processing and various management tasks. Text mining is hard but important research topic in knowledge discovery where hidden information is extracted from unstructured and semi-structured data. Patents are rich knowledge source needed to be organized efficiently and conveniently. Patent documents are used for gathering business intelligence and identifying key trends in technology development. The main focus of this study is to propose a electrical patent classification framework based on Semantic Deep Learner (SDL). In this framework, initially key terms of the patent documents are extracted and represented using Vector Space Model (VSM), the importance of the key terms are weighted based up on their frequencies using TF-IDF. The semantic similarity between the key features is computed using cosine measure. Terms with higher correlations are synthesized into a smaller set of features. Finally the semantic deep learner is trained using the correlated features and accordingly patents are classified. The target output identifies the category of a patent document based on a hierarchical classification scheme of the International Patent Classification (IPC) standard. Our approach is new to the patent domain and shows some improvement in the classification accuracy when compared to the other state of art classifier.

Keywords:

International patent classification, semantic deep learner, semantic similarity, text mining, TF-IDF, topic classification, vector space model,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.