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Research Articles

Text GCN-SW-KNN: a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

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Pages 66-89 | Received 03 Aug 2020, Accepted 10 Jan 2021, Published online: 24 Feb 2021

References

  • Berger, M. J. (2014). Large scale multi-label text classification with semantic word vectors. Technical Report.
  • Blum, A., & Mitchell, T. (1998, July 24–26). Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (pp. 92–100). Madison, WI. doi:10.1145/279943.279962.
  • Chen, G., Ye, D., Xing, Z., Chen, J., & Cambria, E. (2017, May 14–19). Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In 2017 International joint conference on neural networks (IJCNN) (pp. 2377–2383). Anchorage, AK. doi:10.1109/IJCNN.2017.7966144.
  • Elisseeff, A., & Weston, J. (2002). A kernel method for multi-labelled classification. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems 14 (NIPS 2001)(pp. 681–687). Vancouver, Canada: The MIT Press. doi:10.7551/mitpress/1120.003.0092
  • Fellbaum, C., & Miller, G. (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press.
  • Godbole, S., & Sarawagi, S. (2004). Discriminative methods for multi-labeled classification. Advances in Knowledge Discovery and Data Mining, (vol), 3056. doi:10.1007/978-3-540-24775-3_5
  • Gui, Z., Cao, J., Liu, X., Cheng, X., & Wu, H. (2016). Global-scale resource survey and performance monitoring of public OGC web map services. ISPRS International Journal of Geo-Information, 5(6), 88. doi:10.3390/ijgi5060088
  • Gui, Z., Yang, C., Xia, J., Li, J., Rezgui, A., Sun, M., … Fay, D.. (2013a). A visualization-enhanced graphical user interface for geospatial resource discovery. Annals of GIS, 19(2), 109–121. doi:10.1080/19475683.2013.782467
  • Gui, Z., Yang, C., Xia, J., Liu, K., & Lostritto, P. (2013b). A performance, semantic and service quality-enhanced distributed search engine for improving geospatial resource discovery. International Journal of Geographical Information Science, 27(6), 1109–1132. doi:10.1080/13658816.2012.739692
  • Henaff, M., Bruna, J., & Lecun, Y. (2015). Deep convolutional networks on graph-structured data. Computer Science,10 pp. arXiv:1506.05163.
  • Hu, K., Gui, Z., Cheng, X., Qi, K., & Wu, H. (2016). Content-based discovery for web map service using support vector machine and user relevance feedback. PLoS ONE, 11(11), e0166098. doi:10.1371/journal.pone.0166098
  • Hu, Y., Janowicz, K., Prasad, S., & Gao, S. (2015). Metadata topic harmonization and semantic search for linked‐data‐driven geoportals: A case study using ArcGIS online. Transactions in GIS, 19(3), 398–416. doi:10.1111/tgis.12151
  • John, G., & Langley, P. (1995, August 18–20). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the 11th conference on uncertainty in artificial intelligence (pp. 338–345). Montreal, Quebec, Canada.
  • Kim, Y. (2014, October 25–29). Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (pp. 1746–1751). Doha, Qatar. doi:10.3115/v1/D14-1181.
  • Kipf, T. N., & Welling, M. (2017, April 24–26). Semi-supervised classification with graph convolutional networks. In 5th international conference on learning representations (p. 14). Toulon, France.
  • Kurata, G., Xiang, B., & Zhou, B. (2016, June 12–17). Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 521–526). San Diego, CA. doi:10.18653/v1/N16-1063.
  • Li, M., Gui, Z., Cheng, X., Wu, H., & Qin, K. (2019). A content-based WMS layer retrieval method combining multiple kernel learning and user feedback. Acta Geodaetica et Cartographica Sinica, 48(10), 1320–1330.
  • Li, W., Yang, C., Nebert, D., Raskin, R., Houser, P., & Wu, H. (2011). Semantic-based web service discovery and chaining for building an arctic spatial data infrastructure. Computers & Geosciences, 37(11), 1752–1762. doi:10.1016/j.cageo.2011.06.024
  • Liu, J., Chang, W., Wu, Y., & Yang, Y. (2017, August 7–11). Deep learning for extreme multi-label text classification. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 115–124). Shinjuku, Tokyo, Japan. doi:10.1145/3077136.3080834.
  • Liu, K., Yang, C., Li, W., Li, Z., Wu, H., Rezgui, A., & Xia, J. (2011, June 24–26). The GEOSS clearinghouse high performance search engine. In Proceedings - 19th international conference on geoinformatics, geoinformatics 2011 (Geoinformatics 2011). Shanghai, China. doi:10.1109/GeoInformatics.2011.5981077.
  • Liu, P., Qiu, X., & Huang, X. (2016, July 9–15). Recurrent neural network for text classification with multi-task learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (pp. 2873–2879). New York, NY.
  • Madjarov, G., Kocev, D., Gjorgjevikj, D., & DEroski, S. (2012). An extensive experimental comparison of methods for multi-label learning. Pattern Recognition, 45(9), 3084–3104. doi:10.1016/j.patcog.2012.03.004
  • Raskin, R. G., & Pan, M. J. (2005). Knowledge representation in the semantic web for earth and environmental terminology (SWEET). Computers & Geosciences, 31(9), 1119–1125. doi:10.1016/j.cageo.2004.12.004
  • Read, J., Pfahringer, B., & Holmes, G. (2008, December 15–19). Multi-label classification using ensembles of pruned sets. In Proceedings -of the 8th IEEE international conference on data mining (ICDM 2008)(pp. 995–1000). Pisa, Italy: ICDM. doi:10.1109/ICDM.2008.74.
  • Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333–359. doi:10.1007/s10994-011-5256-5
  • Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv:1609.04747.
  • Schapire, R. E., & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3), 135–168. doi:10.1023/A:1007649029923
  • Spyromitros, E., Tsoumakas, G., & Vlahavas, I. (2008). An empirical study of lazy multilabel classification algorithms. Artificial Intelligence: Theories, Models and Applications, 5138, 401–406. doi:10.1007/978-3-540-87881-0_40
  • Tai, K. S., Socher, R., & Manning, C. D. (2015, July 26–31). Improved semantic representations from tree-structured long short-term, memory networks. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (pp. 1556–1566). Beijing, China. doi:10.3115/v1/P15-1150
  • Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1–13. doi:10.4018/jdwm.2007070101
  • Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016, November 1–5). Attention-based LSTM for Aspect-level Sentiment Classification. In Proceedings of the 2016 conference on empirical methods in natural language processing (EMNLP 2016) (pp. 606–615). Austin, TX. doi:10.18653/v1/D16-1058.
  • Wu, H., Li, Z., Zhang, H., Yang, C., & Shen, S. (2011). Monitoring and evaluating the quality of Web Map Service resources for optimizing map composition over the internet to support decision making. Computers & Geosciences, 37(4), 485–494. doi:10.1016/j.cageo.2010.05.026
  • Yang, Z., Gui, Z., Wu, H., & Li, W. (2019). A latent feature-based multimodality fusion method for theme classification on web map service. IEEE Access, 8, 25299–25309.
  • Yang, Z., Yang, D., Dyer, C., He, X., & Hovy, E. (2016, June 12–17). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. (pp. 1480–1489). San Diego, CA. doi:10.18653/v1/N16-1174.
  • Yao, L., Mao, C., & Luo, Y. (2018). Graph convolutional networks for text classification. arXiv:1809.05679.
  • Zhang, M., Gui, Z., Cheng, X., Cao, J., & Wu, H. (2019). A text-based WMS domain themes extraction and metadata extension method. Geomatics and Information Science of Wuhan University, 44(11), 1730–1738.
  • Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038–2048. doi:10.1016/j.patcog.2006.12.019
  • Zhang, X., Zhao, J., & Lecun, Y. (2015,  December 7–12). Character-level convolutional networks for text classification. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, &  R. Garnett (Eds.), Advances in neural information processing systems 28 (NIPS 2015) (pp. 649–657). Montreal, Quebec: The MIT Press.
  • Zhou, C., Sun, C., Liu, Z., & Lau, F. C. M. (2015). A C-LSTM neural network for text classification. Computer Science, 1(4), 39–44.
  • Zhou, P., Shi, W., Tian, J., Qi, Z., & Xu, B. (2016). Attention-based bidirectional long short-term memory networks for relation classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2, 207–212. doi:10.18653/v1/P16-2034