DIVA: a visualization system for exploring document databases for technology forecasting
Computers and Industrial Engineering
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Communications of the ACM - Bioinformatics
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Discovering competitive intelligence by mining changes in patent trends
Expert Systems with Applications: An International Journal
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Development of a GTM-based patent map for identifying patent vacuums
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Enhancing technology clustering through heuristics by using patent counts
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Creating patents on the new technology using analogy-based patent mining
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future.