@Article{2018.100000032,
AUTHOR = {Wei Qin, Xiangfeng Luo},
TITLE = {Association Link Network Based Concept Learning in Patent Corpus},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {3},
PAGES = {653--661},
URL = {http://www.techscience.com/iasc/v24n3/39790},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2018.100000032}
}