International Journal of Management and Applied Science (IJMAS)
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Statistics report
Nov. 2018
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 54
Paper Published : 3316
No. of Authors : 6857
  Journal Paper




Paper Title :
Get-In: An Integrated-Ontology-Based Approach for Patent Search

Author :Cong Phuoc Phan, Hong-Quang Nguyen, Hoang-Minh Nguyen

Article Citation :Cong Phuoc Phan ,Hong-Quang Nguyen ,Hoang-Minh Nguyen , (2017 ) " Get-In: An Integrated-Ontology-Based Approach for Patent Search " , International Journal of Management and Applied Science (IJMAS) , pp. 99-105, Volume-3,Issue-5

Abstract : Patents have become increasingly critical to success of many businesses because they create competitive edge from their exclusive rights granted for inventions – i.e., new technical solutions to problems. As they offer knowledge-rich data, many researches has been extensively conducted to classify and search for their technical information. However, the relationships between patent documents and classification schemes have inadequately exploited. Even well-known patent search systems, such as USPTO, only search for technical information by exploiting either classification scheme or patent document – without integrating them together. This explains why user queries have been insufficiently analyzed to improve the accuracy of search results. In this paper, we address such limitations. Our contribution is threefold. First, we propose the GeT-In ontology that consists of four concepts: Class, Document, Phrase, and Term, to link semantic relationships between the classification scheme and the patent documents. Second, we propose a method to construct our ontology from a well-known classification scheme (USPC). Our method does not perform rule-based matching, thus reducing much manual effort compared to traditional ontology-construction approaches. Third, we propose our patent-search method using our GeT-In ontology. Our experiments with 500 patents showed that the result yields up to 8% increase in terms of F-measure when compared to traditional keyword-based approaches. Keywords— Integrated-Ontology-based Patent Search, Ontology Construction, Information Retrieval.

Type : Research paper

Indexed : Google Scholar


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