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Applying Graph Representations to Automatic Extraction of Semantic Information from Chinese Patent text
Received date: 2015-08-20
Revised date: 2015-10-18
Online published: 2015-11-05
[Purpose/significance]This paper proposes a graph representation based approach to extract automatically semantic information from Chinese patent texts; such information can be used to provide semantic support for text-content based patent intelligent analysis. [Method/process]The author devised two graph models using graph representations: ①a keyword based text graph model, ②a dependency tree based text graph model. The first graph model was constructed by computing the similarities between any two keywords; the second graph model was constructed by extracting syntactic relations from text sentences. In the case study, the author utilized a frequent subgraph mining algorithm to discover frequent subgraph patterns, and such patterns were further used as features to build text classifiers for the purpose of testing the expressivity and effectiveness of the graph models built before. [Result/conclusion] The constructed text classifiers were tested on datasets consisting of patents from four different technology domains, in comparison with using a classic text classifier. The experimental results show that the performance of two text classifiers using graph models has a gain of 2.1%-10.5% than a classic text classifier by using a smaller number of features. Thus, it can be inferred that employing graph representations and graph mining techniques to extract semantic information from patent texts is effective and facilitates a further patent text analysis.
Jiang Chuntao . Applying Graph Representations to Automatic Extraction of Semantic Information from Chinese Patent text[J]. Library and Information Service, 2015 , 59(21) : 115 -122 . DOI: 10.13266/j.issn.0252-3116.2015.21.017
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