山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 67-73.doi: 10.6040/j.issn.1672-3961.0.2021.362
• • 上一篇
田轶群,林荣恒*
TIAN Yiqun, LIN Rongheng*
摘要： 为解决电网客服领域人力成本过高、业务处理受到时空限制的问题,探究基于行业知识图谱的智能客服查询显示系统。根据电网客服领域的业务需求和数据特点,采用半自动化的方式对源数据进行语义标注与关系抽取,建立行业知识图谱。在实现自动问答系统的语义解析环节,提出一种多模式匹配和相似度度量融合的实体识别算法,提高实体识别模块的性能。通过设计100道问题对构建好的智能客服自动问答系统进行测试,最终有93道问题被正确处理。该系统的实现过程为构建基于行业知识图谱的智能问答系统提供参考。
中图分类号:
| [1] 徐增林,盛泳潘,贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4): 589-606. XU Zenglin, SHENG Yongpan, HE Lirong, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4): 589-606. [2] 王元卓,贾岩涛,刘大伟,等. 基于开放网络知识的信息检索与数据挖掘[J]. 计算机研究与发展, 2015, 52(2): 456-474. WANG Yuanzhuo, JIA Yantao, LIU Dawei, et al. Open Web knowledge aided information search and data mining[J]. Journal of Computer Research and Development, 2015, 52(2): 456-474. [3] FADER A, ZETTLEMOYER L, ETZIONI O. Open question answering over curated and extracted knowledge bases[C] //Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 1156-1165. [4] 刘峤, 李杨, 段宏, 等.知识图谱构建技术综述[J].计算机研究与发展, 2016, 53(3): 582-600. LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600. [5] AL-ARFAJ A, AL-SALMAN A M. Ontology cons-truction from text: challenges and trends[J]. International Journal of Artificial Intelligence and Expert Systems, 2015, 6(2): 15-26. [6] CUCERZAN S. Large-scale named entity disambiguation based on Wikipedia data[C] //Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic: Association for Computational Linguistics, 2007: 708-716. [7] 黄勋,游宏梁,于洋. 关系抽取技术研究综述[J]. 现代图书情报技术, 2013(11): 30-39. HUANG Xun, YOU Hongliang, YU Yang. Survey of research on relation extraction technology [J]. Modern Library and Information Technology, 2013(11): 30-39. [8] MIWA M, BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures[C] //Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany: Association for Computational Linguistics, 2016: 1105-1116. [9] BEKOULIS G, DELEU J, DEMEESTER T, et al. Joint entity recognition and relation extraction as a multi-head selection problem[J]. Expert Systems with Applications, 2018, 114: 34-45. [10] ZHUANG Y, LI G, ZHONG Z, et al. Hike: a hybrid human-machine method for entity alignment in large-scale knowledge bases[C] //Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: ACM, 2017: 1917-1926. [11] ZHAO Z, HAN S K, SO I M. Architecture of knowledge graph construction techniques[J]. International Journal of Pure and Applied Mathematics, 2018, 118(19): 1869-1883. [12] DECKER S, MELNIK S, VAN HARMELEN F, et al. The semantic web: the roles of XML and RDF[J]. IEEE Internet computing, 2000, 4(5): 63-73. [13] YAO X, VAN DURME B. Information extraction over structured data: question answering with freebase[C] //Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, USA: Association for Computational Linguistics, 2014: 956-966. [14] BORDES A, WESTON J, USUNIER N. Open question answering with weakly supervised embedding models[C] //Proceedings of the 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECMLPKDD'14). Nancy, France: Springer, 2014: 165-180. [15] DIEFENBACH D, LOPEZ V, SINGH K, et al. Core techniques of question answering systems over knowledge bases: a survey[J]. Knowledge and Information Systems, 2018, 55(3): 529-569. [16] FARMAKIOTOU D, KARKALETSIS V, KOUTSIAS J, et al. Rule-based named entity recognition for Greek financial texts[C] //Proceedings of the Workshop on Computational Lexicography and Multimedia Diction-aries. Patras, Greece: COMLEX, 2000: 75-78. [17] MA X, HOVY E. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF[C] //Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany: Association for Computational Linguistics, 2016: 1064-1074. [18] QIN Ying, ZENG Yingfei. Research of Clinical Named Entity Recognition Based on Bi-LSTM-CRF[J]. Journal of Shanghai Jiaotong University(Science), 2018, 23(3): 392-397. [19] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[C] //Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies. San Diego, USA: Association for Computational Linguistics, 2016: 260-270. [20] DONG Mei, CHANG Zhijun, ZHANG Runjie. A multiple pattern matching algorithm for specifications of incremental metadata for Sci-Tech literature[J]. Data Analysis and Knowledge Discovery, 2021, 5(6): 135-144. [21] 巫喜红,曾锋. AC多模式匹配算法研究[J]. 计算机工程, 2012, 38(6): 279-281. WU Xihong, ZENG Feng. Research on AC multi-pattern matching algorithm[J]. Computer Engineering, 2012, 38(6): 279-281. |
| [1] | 龚乐君,杨璐,高志宏,李华康. LncRNA与疾病关系的知识图谱构建[J]. 山东大学学报 (工学版), 2021, 51(2): 26-33. |
| [2] | 张岩,李英冰,郑翔. 基于微博数据的台风“山竹”舆情演化时空分析[J]. 山东大学学报 (工学版), 2020, 50(5): 118-126. |
| [3] | 段江丽,胡新. 自然语言问答中的语义关系识别[J]. 山东大学学报 (工学版), 2020, 50(3): 1-7. |
| [4] | 张海军,陈映辉. 语义分析及向量化大数据跨站脚本攻击智检[J]. 山东大学学报 (工学版), 2020, 50(2): 118-128. |
| [5] | 苏佳林,王元卓,靳小龙,程学旗. 自适应属性选择的实体对齐方法[J]. 山东大学学报 (工学版), 2020, 50(1): 14-20. |
| [6] | 陈馨菂,李天瑞,杨欢欢. 基于时间序列数据的交互式主题河流可视化[J]. 山东大学学报 (工学版), 2019, 49(4): 29-35, 43. |
| [7] | 赵鹏程,张福全,杨绪兵,吴寅. 基于可视化的森林火灾监测节点优化部署策略[J]. 山东大学学报 (工学版), 2019, 49(1): 30-35, 40. |
| [8] | 谢志峰,吴佳萍,马利庄. 基于卷积神经网络的中文财经新闻分类方法[J]. 山东大学学报(工学版), 2018, 48(3): 34-39. |
| [9] | 刘东慧1,2,姜薇1*. 基于事件本体的Web不良信息挖掘[J]. 山东大学学报(工学版), 2012, 42(5): 35-40. |
| [10] | 林原，林鸿飞*，张平. 一种基于位置优化的排序学习方法[J]. 山东大学学报(工学版), 2012, 42(1): 19-24. |
| [11] | 吴恩启1,杜宝江1,王海鹏1,余建平2. 基于虚拟现实的地下电力管线可视化规划研究[J]. 山东大学学报(工学版), 2010, 40(6): 54-57. |
| [12] | 董乃鹏 赵合计 SCHOMMER Christoph. 作者写作特征提取引擎[J]. 山东大学学报(工学版), 2009, 39(5): 27-31. |
|
||
