Knowledge Graph Construction Method for Carbon Trading

doi:10.3969/j.issn.1006-2475.2018.08.021

Abstract

Abstract: In order to solve the problem of data integration in carbon trading, a new method of constructing carbon trading knowledge graph is proposed. For processing the semi-structured and unstructured data in carbon trading, a self-defined Web data wrapper and a method by combining BiLSTM-CRF model with dependency parser are applied to extract triples from data respectively. Then a complete carbon trading knowledge graph can be obtained by transforming the acquired knowledge into linked data, and the semantic query of it can be achieved by fuseki based on Jena. The experimental results show that the proposed method can construct carbon trading knowledge graph rapidly and effectively, and can retrieve useful information from the massive data of carbon trading.

Key words: carbon trading, knowledge graph, triple extraction, linked data, semantic query

CLC Number:

TP391.1

WANG Liang-yu. Knowledge Graph Construction Method for Carbon Trading[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.08.021.

References

［1］秦慧元. 我国碳交易市场的背景、现状及发展建议［J］. 中国环保产业, 2017(8):35-39.
［2］陈宏宏. 低碳经济背景下国际碳交易市场发展及其前景［D］. 长春:吉林大学, 2017.
［3］漆桂林,高桓,吴天星. 知识图谱研究进展［J］. 情报工程, 2017,3(1):4-25.
［4］ Singhal A. Introducing the knowledge graph: Things, not strings［J］. Official Google Blog, 2012:1-8.
［5］ Auer S, Bizer C, Kobilarov G, et al. Dbpedia: A nucleus for a Web of open data［J］. The Semantic Web, 2007:722-735.
［6］ Suchanek F M, Kasneci G, Weikum G. Yago: A core of semantic knowledge［C］// Proceedings of the 16th International Conference on World Wide Web. 2007:697-706.
［7］ Bollacker K, Evans C, Paritosh P, et al. Freebase: A collaboratively created graph database for structuring human knowledge［C］// Proceedings of 2008 ACM SIGMOD International Conference on Management of Data. 2008:1247-1250.
［8］ Niu Xing, Sun Xinruo, Wang Haofen, et al. Zhishi.me-weaving Chinese linking open data［J］. The Semantic Web-ISWC, 2011:205-220.
［9］阮彤,孙程琳,王昊奋,等. 中医药知识图谱构建与应用［J］. 医学信息学杂志, 2016,37(4):8-13.
［10］刘鎏. 基于地理本体的吉林地域知识图谱的构建［D］. 北京:北京交通大学, 2017.
［11］王巍巍,王志刚,潘亮铭,等. 双语影视知识图谱的构建研究［J］. 北京大学学报(自然科学版), 2016,52(1):25-34.
［12］Scrapy. Scrapy 1.4 Documentation ［EB/OL］. http://doc.scrapy.org/en/latest/index.html, 2017-05-18.
［13］Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch［J］. Journal of Machine Learning Research, 2011,12(1):2493-2537.
［14］Chiu J P C, Nichols E. Named entity recognition with bidirectional LSTM-CNNs［J］. Transactions of the Association for computational Linguistics, 2016(4):357-370.
［15］Graves A. Long short-term memory［M］// Supervised Sequence Labelling with Recurrent Neural Networks. 2012:1735-1780.
［16］Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks［C］// Proceedings of the 30th International Conference on Machine Learning. 2013:1310-1318.
［17］Pulford G. The Viterbi algorithm［C］// IEE Seminar on Target Tracking: Algorithms and Applications. 2006:53-65.
［18］甘丽新,万常选,刘德喜,等. 基于句法语义特征的中文实体关系抽取［J］. 计算机研究与发展, 2016,53(2):284-302.
［19］黄昌宁,苑春法,潘诗梅. 语料库、知识获取和句法分析［J］. 中文信息学报, 1992(3):1-6.
［20］Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space［J］. Computer Science, 2013.
［21］Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks［C］// Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010:249-256.

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