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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

Cite this document


A. Aizawa. (2003). Title, An information-theoretic perspective of tf–idf measures .Information Processing & Management, 39(1), 45-65.

D. Angluin. (1988). Title, Queries and concept learning. Machine Learning, 2(4), 319-342.

J. J. Collins & C. C. Chow. (1998). Title, It”s a small world. Nature, 393(6684), 409.

N. D. Goodman, J. B. Tenenbaum, J. Feldman, & T. L. Griffiths. (2010). Title, A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108-154.

D. Guthrie, B. Allison, W. Liu, L. Guthrie, & Y. Wilks. (2006). Title, A Closer Look at Skip-gram Modelling. (pp.1222--1225).

R. Hammer, G. Diesendruck, D. Weinshall, & S. Hochstein. (2009). Title, The development of category learning strategies: what makes the difference?. Cognition, 112(1), 105-119.

B. Hjørland. (2009). Title, Concept theory. Journal of the American Society for Information Science & Technology, 60(8), 1519-1536.

T. Huang, X. Hu, & S. X. Yang. (2016). Title, Networks based computing and automation, 22(4), 533-534.

S. S. Hussain, M. Hashmani, M. Moinuddin, & K. Raza. (2014). Title, A novel topology in modular ann approach for multi-modal concept identification and image retrieval. Intelligent Automation & Soft Computing, 20(1), 131-141.

K. A. D. Jong. (1975). Title, Analysis of the behavior of a class of genetic adaptive systems. Ph.d. thesis University of Michigan.

E. Kushilevitz, R. Ostrovsky, & T. Rabani. (1998). Title, Efficient search for approximate nearest neighbor in high dimensional spaces. Thirtieth ACM Symposium on Theory of Computing (Vol.30, pp.614-623).

X. Luo, Z. Xu, J. Yu, & X. Chen. (2011). Title, Building association link network for semantic link on web resources. IEEE Transactions on Automation Science & Engineering, 8(3), 482-494.

P. Mahdabi & F. Crestani. (2014). Title, Patent Query Formulation by Synthesizing Multiple Sources of Relevance Evidence. ACM.

T. Mikolov, K. Chen, G. Corrado, & J. Dean. (2013). Title, Efficient estimation of word representations in vector space. Computer Science.

Robertson, Stephen. "The Probabilistic Relevance Framework: BM25 and Beyond." Foundations and Trends® in Information Retrieval 3.4 (2010): 333-389. Crossref. Web.

J. N. Rouder & R. Ratcliff. (2006). Title, Comparing exemplar- and rule-based theories of categorization. Current Directions in Psychological Science, 15(1), 9-13.

G. Salton. (1971). Title, Experiments in Automatic Thesaurus Construction for Information Retrieval. In Proceedings Ifip Congress, Ta-2 (Vol.71, pp.115-123).

I. Yoo & X. Hu. (2006). Title, Clustering Ontology-enriched Graph Representation for Biomedical Documents based on Scale-Free Network Theory. International IEEE Conference on Intelligent Systems (pp.851-858). IEEE.

Z. Zhang, Q. Wang, L. Si, & J. Gao. (2016). Title, Learning for efficient supervised query expansion via two-stage feature selection. 265-274.


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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