Oracle bone studies knowledge graph construction based on multi-source heterogeneous data
XIONG Jing1,2, JIAO Qingju1,2, LIU Yuntong1
1.School of Computer & Information Engineering, Anyang Normal University, Anyang 455000, Henan Province, China 2.Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education, Anyang 455000, Henan Province, China
Abstract:In order to solve or alleviate the problems such as great difficulty, long period, weak knowledge linking and low sharing regarding the oracle bone studies, we construct the oracle bone studies knowledge graph by combining two kinds of knowledge graphs MKD and KG .These two kinds of knowledge graphs are fused and semantic extension is carried out based on knowledge reasoning. The constructed oracle bone studies knowledge graph contains 148 305 entities and 434 032 relations, which can meet the basic needs of oracle bone studies research. It also provides reference for other ancient books knowledge graph construction.
1 宋镇豪. 岁末年初对甲骨学的思考和期待[EB/OL]. [2019-03-20]. http://cass.cssn.cn/xuebuweiyuan/201812/t20181228_4802379.html. SONGZ H. The Thinking and Expectations of Oracle Bone Inscriptions at the End of the Year[EB/OL]. [2019-03-20]. http://cass.cssn.cn/xuebuweiyuan/201812/t20181228_4802379.html. 2 熊晶, 钟珞, 王爱民. 甲骨文知识图谱构建中的实体关系发现研究[J]. 计算机工程与科学, 2015,37(11):2188-2194. XIONGJ , ZHONGL , WANGA M. Research on entity relation discovery for oracle bone inscriptions knowledge mapping construction[J]. Computer Engineering&Science, 2015,37 (11) :2188-2194. 3 马如森. 殷墟甲骨学-带你走进甲骨文的世界[M]. 上海: 上海大学出版社, 2007. MAR S. Yin ruins of Oracle Bone Studies-Take you into the world of Oracle Bone Inscriptions [M]. Shanghai: Shanghai University Press, 2007. 4 陈悦, 刘则渊. 悄然兴起的科学知识图谱[J]. 科学学研究, 2005,23(2):149-154.DOI:10.16192/j.cnki.1003-2053.2005.02.002 CHENY, LIUZ Y. The rise of mapping knowledge domain[J]. Studies in Science of Science, 2005, 23(2): 149-154.DOI:10.16192/j.cnki.1003-2053.2005.02.002 5 王建芳, 吴清强, 张超星, 等. 基于本体的科学知识图谱分析方法研究[EB/OL].[2014-1-15]. http://ir.las.ac.cn/handle/12502/3837. WANGJ F, WUQ Q, ZHANGC X , et al. Analysis Method of Mapping Knowledge Domains based on Ontology [EB/OL].[2014-1-15]. http://ir.las.ac.cn/handle/12502/3837. 6 SINGHALA. Introducing the Knowledge Graph: Things, Not Strings[EB/OL]. [2019-1-20]. http://googleblog.blogspot.com/2012/05/ introducing-knowledge-graph-things-not.html. 7 赵军, 刘康, 何世柱, 等. 知识图谱[M]. 北京: 高等教育出版社, 2018. ZHAOJ, LIUK, HES Z, et al. Knowledge Graph[M]. Beijing: Higher Education Press, 2018. 8 秦长江, 侯汉清. 知识图谱——信息管理与知识管理的新领域[J]. 大学图书馆学报, 2009,27(1):30-37. QINC J, HOUH Q. Mapping knowledge domain-A new field of information management and knowledge management[J]. Journal of Academic Libraries, 2009, 27(1): 30-37. 9 胡泽文, 孙建军, 武夷山. 国内知识图谱应用研究综述[J]. 图书情报工作, 2013,57(3):131-137. HUZ W, SUNJ J, WUY S. Research review on application of knowledge mapping in China[J]. Library and Information Service, 2013, 57(3): 131-137. 10 刘则渊, 陈悦, 侯海燕. 科学知识图谱:方法与应用[M]. 北京: 人民出版社, 2008. LIUZ Y, CHENY, HOUH Y. Mapping Knowledge Domains Methods and Application[M]. Beijing: People’s Publishing House, 2008. 11 CHENC. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the Association for Information Science & Technology, 2014,57(3):359-377. DOI:10.1002/asi.20317 12 CHENC, IBEKWE-SANJUANF, HOUJ. The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis[J]. Journal of the American Society for Information Science & Technology, 2010,61(7):1386-1409. 13 汤建民, 余丰民. 国内知识图谱研究综述与评估:2004—2010年[J]. 情报资料工作, 2012(1):16-21. TANG J M, YU F M. Review and evaluation of knowledge mapping research in China: 2004 -2010[J]. Information and Documentation Services, 2012 (1): 16-21. 14 SUCHANEKF M, KASNECIG, WEIKUMG. Yago: A core of semantic knowledge[C]//International Conference on World Wide Web,WWW 2007. Banff: ACM,2007. 15 CARLSONA, BETTERIDGEJ, KISIELB, et al. Toward an architecture for never-ending language learning[C]//Twenty-Fourth Aaai Conference on Artificial Intelligence. Atlanta: AAAI Press,2010. 16 AUERS, BIZERC, KOBILAROVG, et al. DBpedia: A nucleus for a Web of open data[J]. Semantic Web, 2007,4825:11-15. DOI:10.1007/978-3-540-76298-0_52 17 BOLLACKERK D, EVANSC, PARITOSHP, et al. Freebase: A collaboratively created graph database for structuring human knowledge[C]//SIGMOD 2008. Vancouver: AMC, 2008. 18 DONGX, GABRILOVICHE, HEITZG, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion[C]// Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. NewYork:ACM,2014. 19 XINGN, SUNX, WANGH,et al. Zhishi.me - Weaving Chinese Linking Open Data[C]//Proceedings of the 10th International Semantic Web Conference . Bonn: Springer,2011. 20 WANGZ, LIJ, WANGZ, et al. XLore: A Large-scale English-Chinese Bilingual Knowledge Graph[C]// ISWC2013. Sydney: Springer, 2013. 21 OpenKG.CN-开放的中文知识图谱[EB/OL]. [2019-3-1]. http://www.openkg.cn/. OpenKG.CN-open Chinese knowledge Graph [EB/OL]. [2019-3-1]. http://www.openkg.cn/. 22 GATTANIA, LAMBAD S, GARERAN, et al. Entity extraction, linking, classification, and tagging for social media: a wikipedia-based approach[J]. Proc VLDB Endow, 2013,6(11):1126-1137. 23 DESHPANDEO, LAMBAD S, TOURNM, et al. Building, maintaining, and using knowledge bases: A report from the trenches[C]//ACM SIGMOD International Conference on Management of Data. Newyork: ACM,2013. 24 XUM, WANGZ, BIER, et al. Discovering missing semantic relations between Entities in Wikipedia[C]//International Semantic Web Conference. Sudney:Springer,2013. DOI:10.1007/978-3-642-41335-3_42 25 WANGZ, LIJ, TANGJ. Boosting cross-lingual knowledge linking via concept annotation[C]// Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Beijing: AAAI Press, 2013. 26 LINY, LIUZ, SUNM, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin: AAAI Press,2015. 27 XUB, XUY, LIANGJ, et al. CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System[C]// International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Arras:Springer,2017.DOI:10.1007/978-3-319-60045-1_44 28 UniversityTsinghua,ResearchMicrosoft.Open Academic Graph[EB/OL]. [2019-3-28]. https://www.openacademic.ai/oag/. 29 JIE T. AMiner: Toward Understanding Big Scholar Data[C]// Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (WSDM'16). San Francisco:ACM,2016. 30 ZENGY,WANGD S, ZHANGT L,et al.Belief Engine - Declarative[EB/OL]. [2019-3-25]. http://www.belief-engine.org/declarative/. 31 韩喆,冯岩松.北京大学中文百科知识图谱PKU-PIE 知识库[EB/OL]. [2019-3-25]. http://openkg.cn/dataset/pku-pie. HANZ,FENGY S.Peking University Chinese Encyclopedia Knowledge Graph PKU-Pie Knowledge Base [EB/OL]. [2019-3-25]. http://openkg.cn/dataset/pku-pie. 32 冯新翎, 何胜, 熊太纯, 等. “科学知识图谱”与“Google知识图谱”比较分析——基于知识管理理论视角[J]. 情报杂志, 2017,36(1):149-153. FENGX L, HES, XIONGT C, et al. Comparison and analysis of mapping knowledge domain and google knowledge graph-based on the theory of knowledge management[J]. Journal of Intelligence, 2017,36(1):149-153. 33 熊晶, 高峰, 吴琴霞. 甲骨文大规模基础数据的语义挖掘研究[J]. 现代图书情报技术, 2015,31(2):7-14. XIONGJ, GAOF, WUQ X. Research on semantic mining for large-scale oracle bone inscriptions foundation data [J]. New Technology of Library and Information Service, 2015, 31(2): 7-14. 34 HEQ. Knowledge discovery through co-word analysis[J]. Library Trends, 1999,48(1):133-159. 35 王珊, 萨师煊. 数据库系统概论[M]. 第5版.北京: 高等教育出版社, 2014. WANGS, SA S X. Introduction to Database System[M]. 5th ed. Beijing: Higher Education Press, 2014. 36 GUARINON. Formal ontology in information systems[C]//Proceedings of the first international conference (FOIS'98). Amsterdam: IOS Press, 1998. 37 ZHAOS, CHANGE. From database to semantic web ontology: An overview[J]. Lecture Notes in Computer Science, 2007,4806:1205-1214.DOI:10.1007/978-3-540-76890-6_48 38 鄂海红, 张文静, 肖思琪, 等. 深度学习实体关系抽取研究综述[J]. 软件学报, 2019,30(6):1793-1818.DOI:10.13328/j.cnki.jos.005817 E H H, ZHANGW J, XIAOS Q, et al. A survey of entity relationship extraction based on deep learning[J]. Journal of Software, 2019,30(6):1793-1818. DOI:10.13328/j.cnki.jos.005817 39 SOCHERR, HUVALB, MANNINGC D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//Joint Conference on Empirical Methods in Natural Language Processing & Computational Natural Language Learning.Jeju Island: ACL, 2012. 40 SANTOSC N D, BINGX, ZHOUB. Classifying relations by ranking with convolutional neural networks[J]. Computer Science, 2015,86(86):132-137. 41 ZENGD, LIUK, LAIS, et al. Relation classification via convolutional deep neural network[C]//Proceedings of COLING 2014.Dublin:ACL, 2014. 42 ZENGD, LIUK, CHENY, et al. Distant supervision for relation extraction via piecewise convolutional neural networks[C]// EMNLP 2015,Lisbon:ACL,2015. DOI:10.18653/v1/d15-1203 43 MIWAM, BANSALM. 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:ACL, 2016.DOI:10.18653/v1/p16-1105 44 ZHOUP, SHIW, TIANJ, et al. Attention-Based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Beelin: ACL, 2016. DOI:10.18653/v1/p16-2034 45 LINY, SHENS, LIUZ, et al. Neural relation extraction with selective attention over instances[C]//ACL 2016. Berlin: Association for Computational Linguistics, 2016. DOI:10.18653/v1/p16-1200 46 HUANGY Y, WANGW Y. Deep residual learning for weakly-supervised relation extraction[C]. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.Copenhagen: ACL, 2017. 47 CHEW, LIZ, LIUT. LTP: A Chinese language//technology platform[J]. Journal of Chinese Information Processing, 2010,2(6):13-16. 48 庄严, 李国良, 冯建华. 知识库实体对齐技术综述[J]. 计算机研究与发展, 2016,53(01):165-192. ZHUANGY, LIG L, FEN J H. A survey on entity alignment of knowledge base[J].Journal of Computer Research and Development, 2016, 53 (1) :165-192. 49 展翔. 甲骨著錄重片拾遺及指瑕[EB/OL]. [2019-3-26]. http://www.xianqin.org/blog/archives/11297.html. ZHANX. Oracle Bone Inscriptions repeated pieces collection and errors[EB/OL]. [2019-3-26]. http://www.xianqin.org/blog/archives/11297.html. 50 朱新华, 马润聪, 孙柳, 等. 基于知网与词林的词语语义相似度计算[J]. 中文信息学报, 2016,30(04):29-36. ZHUX H, MAR C, SUNL, et al. Word semantic similarity computation based on HowNet and CiLin[J]. Journal of Chinese Information Processing, 2016, 30 (4) :29-36. 51 林海伦, 王元卓, 贾岩涛, 等. 面向网络大数据的知识融合方法综述[J]. 计算机学报, 2017,40(01):1-27. LINH L, WANGY Z, JIAY T, et al. Network big data oriented knowledge fusion methods:A survey[J].Chinese Journal of Computers, 2017, 40 (1) :1-27. 52 熊晶, 王爱民, 徐建良. 基于领域本体的信息检索优化策略[J]. 计算机工程与设计, 2011,32(08):2695-2699. DOI:10.16208/j.issn1000-7024.2011.08.045 XIONGJ , WANGA M, XUJ L. Information retrieval optimization strategy based on domain ontology[J]. Computer Engineering and Design, 2011, 32(8): 2695-2699. DOI:10.16208/j.issn1000-7024.2011.08.045