Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds “product-term” bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.
| Published in | American Journal of Neural Networks and Applications (Volume 5, Issue 1) |
| DOI | 10.11648/j.ajnna.20190501.15 |
| Page(s) | 28-35 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
TF-IDF, Random Walk, Semantic Annotation
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APA Style
Mingxi Zhang, Guanying Su. (2019). Random Walk-Based Semantic Annotation for On-demand Printing Products. American Journal of Neural Networks and Applications, 5(1), 28-35. https://doi.org/10.11648/j.ajnna.20190501.15
ACS Style
Mingxi Zhang; Guanying Su. Random Walk-Based Semantic Annotation for On-demand Printing Products. Am. J. Neural Netw. Appl. 2019, 5(1), 28-35. doi: 10.11648/j.ajnna.20190501.15
AMA Style
Mingxi Zhang, Guanying Su. Random Walk-Based Semantic Annotation for On-demand Printing Products. Am J Neural Netw Appl. 2019;5(1):28-35. doi: 10.11648/j.ajnna.20190501.15
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Download@article{10.11648/j.ajnna.20190501.15,
author = {Mingxi Zhang and Guanying Su},
title = {Random Walk-Based Semantic Annotation for On-demand Printing Products},
journal = {American Journal of Neural Networks and Applications},
volume = {5},
number = {1},
pages = {28-35},
doi = {10.11648/j.ajnna.20190501.15},
url = {https://doi.org/10.11648/j.ajnna.20190501.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.15},
abstract = {Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds “product-term” bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.},
year = {2019}
}
TY - JOUR T1 - Random Walk-Based Semantic Annotation for On-demand Printing Products AU - Mingxi Zhang AU - Guanying Su Y1 - 2019/07/04 PY - 2019 N1 - https://doi.org/10.11648/j.ajnna.20190501.15 DO - 10.11648/j.ajnna.20190501.15 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 28 EP - 35 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20190501.15 AB - Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds “product-term” bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products. VL - 5 IS - 1 ER -
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