Open Access
ARTICLE
Association Link Network Based Concept Learning in Patent Corpus
Wei Qin, Xiangfeng Luo
School of Computer Engineering and Science, Shanghai University, China
* Corresponding Author: Wei Qin,
Intelligent Automation & Soft Computing 2018, 24(3), 653-661. https://doi.org/10.31209/2018.100000032
Abstract
Concept learning has attracted considerable attention as a means to tackle
problems of representation and learning corpus knowledge. In this paper, we
investigate a challenging problem to automatically construct a patent concept
learning model. Our model consists of two main processes; which is the
acquisition of the initial concept graph and refined process for the initial concept
graph. The learning algorithm of a patent concept graph is designed based on the
Association Link Network (ALN). A concept is usually described by multiple
documents utilizing ALN here in concept learning. We propose a mixture-ALN,
which add links between documents and the lexical level, compared with the ALN.
Then, a heuristic algorithm is proposed to refine the concept graph, leading to a
more concise and simpler knowledge for the concept. The heuristic algorithm
consists of four phases; first, for simplifying bag of words for concept in patent
corpus, we start to select a core node from the initial concept graph. Second,for
learning the association rule for the concept, we searched important association
rules around the core node in our rules collection. Third, to ensure coherent
semantics of the concept, we selected corresponding documents based on the
selected association rules and words. Finally, for enriching semantics of the
refined concept, we iteratively selected core nodes based on the corresponding
documents and restarted our heuristic algorithm. In the experiments, our model
shows effectiveness and improvements in prediction accuracy in the retrieve task
of the patent.
Keywords
Cite This Article
W. Qin and X. Luo, "Association link network based concept learning in patent corpus,"
Intelligent Automation & Soft Computing, vol. 24, no.3, pp. 653–661, 2018.