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Association Link Network based Concept Learning in Patent Corpus


Concept learning has attracted considerable attention as a means to tackle problem of representation and learning corpus knowledge. In this paper, we investigate a challenging problem to automatically construct patent concept learning model. Our model consist of two main processes, which are the acquisition of the initial concept graph and refine process for initial concept graph. The learning algorithm of patent concept graph is designed based on Association Link Network (ALN). A concept is usually described by multiple document, enable ALN to be used in concept learning, we propose mixture-ALN, which add links between document and lexical level, compared with ALN. Then, a heuristic algorithm is proposed to refine the concept graph which could learn a more concise and simpler knowledge for concept. The heuristic algorithm consists of four phases, firstly, for simplifying bag of words for concept in patent corpus, we start to select core node from initial concept graph. Secondly, for learning the association rule for concept, we search important association rules around core node in our rules collection. Thirdly, ensure coherent semantics of the concept, we select corresponding documents based on the selected association rules and words. Finally, for enriching semantics of refined concept, we iteratively select core nodes based on corresponding documents and restart our heuristic algorithm. In the experiments, our model shows effectiveness and improvement in prediction accuracy in retrieve task of patent.



Total Pages: 9
Pages: 653-661


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Volume: 24
Issue: 3
Year: 2018

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present

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