Abstract
Recently more and more observation sensors carried by different platforms make people be able to obtain massive multi-source data, this kind of explosive increase of information extraction brings great challenge. However, remote sensing image interpretation depends on the knowledge from experience of the experts, there are also few available knowledge graphs for the intelligent interpretation of multi-source remote sensing big data. In this article, we provide a survey of such knowledge graph and propose the framework to auxiliary interpreters.
This work was supported by National Defense Science and Technology Innovation Fund of the Chinese Academy of Sciences (CXJJ-16M109).
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References
Deren, L., Liangpei, Z., Guisong, X.: Automatic analysis and mining of remote sensing big data. Acta Geodaetica Cartogr. Sin. 43(12), 1211–1216 (2014)
Xie, R., Luo, Z.W., Wang, Y.C., Chen, W.: Key techniques for establishing domain specific large scale knowledge graph. Radio Eng. 47(04), 1–6 (2017)
Bashar, M.A., Li, Y., Gao, Y.: A framework for automatic personalised ontology learning. In: IEEE International Conference on Web Intelligence, pp. 105–112 (2016)
Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25, 161–197 (1998)
Narayanaswamy, M., Ravikumar, K.E., Vijay-shanker, K.: A biological named entity recognizer. In: Proceedings of Pacific Symposium on Biocomputing, p. 427 (2003)
Bordes, A., Gabrilovich, E.: Constructing and mining web-scale knowledge graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 1967–1967. ACM (2014)
Wang, J.: Chinese named body recognition based on maximum entropy model. Nanjing University of Science and Technology (2005)
Yu, H., Zhang, H., Liu, Q., Lv, X., Shi, S.: Chinese named entity recognition based on cascaded hidden Markov model. J. Commun. 27, 86–93 (2006)
Sun, X., Sun, Z., Ren, F.: Biomedical named entity recognition based on deep conditional random field. Pattern Recogn. Artif. Intell. 29(11), 997–1008 (2016)
Knublauch, H., Oberle, D., Tetlow, P., Wallace, E.: A semantic web primer for object-oriented software developers. W3C. Accessed 30 July 2008
Settles, B.: Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, pp. 104–107 (2004)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional LSTM-CNN. TACL, vol. 4, pp. 357–370 (2016)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. CoRR abs/1508.01991 (2015)
Limsopatham, N., Collier, N.: Learning orthographic features in bi-directional lstm for biomedical named entity recognition. In: Proceedings of the 2016 Biennial Workshops on Building and Evaluating Resources for Biomedical Text Mining. Association for Computational Linguistics (2016)
Dai, H.-J., Tsai, R.T.-H., Hsu, W.-L.: Entity disambiguation using a markov logic network. In: Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 846–855 (2011)
Liu, X., Zhou, M., Wei, F., Fu, Z., Zhou, X.: Joint inference of named entity recognition and normalization for tweets. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers (ACL 2012), vol. 1, pp. 526–535. Association for Computational Linguistics, Stroudsburg, PA, USA (2012)
Li, C., Liu, Y.: Improving text normalization via unsupervised model and discriminative reranking. In: ACL (2014)
Sproat, R., Jaitly, N.: RNN approaches to text normalization: a challenge (2016)
Chen, Y., He, S., Liu, K., Zhao, J., Lv, X.: Entity linking based on multiple feature. J. Chin. Inf. Process. 30(4), 176–183 (2016)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of ACLIJCNLP, pp. 1003–1011 (2009)
Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Proceedings of ECML-PKDD, pp. 148–163 (2010)
Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of EMNLP, pp. 455–465 (2012)
Socher, R., Bauer, J., Manning, C.D., Ng, N.Y.: Parsing with compositional vector grammars. In: Proceedings of ACL (2013)
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING, pp. 2335–2344 (2014)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (2016)
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Meeting of the Association for Computational Linguistics, pp. 2124–2133 (2016)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Wang, Q., Liu, J., Luo, Y., Wang, B., Lin, C.-Y.: Knowledge base completion via coupled path ranking. In: Meeting of the Association for Computational Linguistics (ACL), pp. 1308–1318 (2016)
Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Proceedings of the 11th International Semantic Web Conference, pp. 542–557 (2013)
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June–July DBLP, pp. 809–816 (2011)
Nickel, M., Tresp, V.: An analysis of tensor models for learning on structured data. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8189, pp. 272–287. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40991-2_18
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence, number EPFL-CONF-192344 (2011)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. In: Proceedings of ACL, pp. 84–94 (2015)
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 985–991 (2016)
Goh, K.I., Kahng, B., Kim, D.: Universal behavior of load distribution in scale-free networks. Phys. Rev. Lett. 87(27), 278701 (2001)
Cao, N., Sun, J., Lin, Y.-R., Gotz, D., Liu, S., Qu, H.: FacetAtlas: multifaceted visualization for rich text corpora. IEEE Trans. Vis. Comput. Graph. 16(6): 1172–1181 (2010)
Lohmann, S., Negru, S., Haag, F., Ertl, T.: VOWL2: user-oriented visualization of ontologies. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 266–281. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13704-9_21
Lohmann, S., Negru, S., Bold, D.: The ProtégéVOWL plugin: ontology visualization for everyone. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 395–400. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11955-7_55
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Jiang, B., Ma, L., Cai, L. (2018). Remote Sensing Image Intelligent Interpretation Based on Knowledge Graph. In: Yu, Q. (eds) Space Information Networks. SINC 2017. Communications in Computer and Information Science, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-10-7877-4_30
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