知识图谱研究综述
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摘要
知识图谱是以图的形式表现客观世界中的概念和实体及其之间关系的知识库,是语义搜索、智能问答、决策支持等智能服务的基础技术之一.目前,知识图谱的内涵还不够清晰;且因建档不全,已有知识图谱的使用率和重用率不高.为此,本文给出知识图谱的定义,辨析其与本体等相关概念的关系.本体是知识图谱的模式层和逻辑基础,知识图谱是本体的实例化;本体研究成果可以作为知识图谱研究的基础,促进知识图谱的更快发展和更广应用.本文罗列分析了国内外已有的主要通用知识图谱和行业知识图谱及其构建、存储及检索方法,以提高其使用率和重用率.最后指出知识图谱未来的研究方向.
A knowledge graph is a knowledge base that represents objective concepts/entities and their relationships in the form of graph, which is one of the fundamental technologies for intelligent services such as semantic retrieval, intelligent answering, decision support, etc. Currently, the connotation of knowledge graph is not clear enough and the usage/reuse rate of existing knowledge graphs is relatively low due to lack of documentation. This paper clarifies the concept of knowledge graph through differentiating it from related concepts such as ontology in that the ontology is the schema layer and the logical basis of a knowledge graph while the knowledge graph is the instantiation of an ontology. Research results of ontologies can be used as the foundation of knowledge graph research to promote its developments and applications.Existing generic/domain knowledge graphs are briefly documented and analyzed in terms of building, storage, and retrieval methods. Moreover, future research directions are pointed out.
A knowledge graph is a knowledge base that represents objective concepts/entities and their relationships in the form of graph, which is one of the fundamental technologies for intelligent services such as semantic retrieval, intelligent answering, decision support, etc. Currently, the connotation of knowledge graph is not clear enough and the usage/reuse rate of existing knowledge graphs is relatively low due to lack of documentation. This paper clarifies the concept of knowledge graph through differentiating it from related concepts such as ontology in that the ontology is the schema layer and the logical basis of a knowledge graph while the knowledge graph is the instantiation of an ontology. Research results of ontologies can be used as the foundation of knowledge graph research to promote its developments and applications.Existing generic/domain knowledge graphs are briefly documented and analyzed in terms of building, storage, and retrieval methods. Moreover, future research directions are pointed out.
引文
1 Gruber TR. A translation approach to portable ontology specifications. Knowledge Acquisition, 1993, 5(2):199-220.
2 Pan JZ, Horrocks I. RDFS(FA):Connecting RDF(S)and OWL DL. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(2):192-206.[doi:10.1109/TKDE.2007.37]
3 Mcguinness DL, Harmelen F. OWL Web ontology language overview. W3C Recommendation, 2004, 63(45):990-996.
4 Brookes BC. The foundations of information science. PartⅢ:Quantitative aspects:Objective maps and subjective landscapes. Journal of Information Science, 1980,2(6):269-275.[doi:10.1177/016555158000200602]
5马费成.论布鲁克斯情报学基本理论.情报学报,1983, 2(4):314-324
6陈强,廖开际,奚建清.知识地图研究现状与展望.情报杂志,2006, 25(5):43-46.[doi:10.3969/j.issn.1002-1965.2006.05.016]
7 Zins C. Knowledge map of information science. Journal of the Association for Information Science and Technology,2007, 58(4):526-535.
8陈悦,陈超美,刘则渊,等.CiteSpace知识图谱的方法论功能.科学学研究,2015, 33(2):242-253.[doi:10.3969/j.issn.1003-2053.2015.02.009]
9 Garfield E. Citation indexes for science:A new dimension in documentation through association of ideas. Science, 1955,122(3159):108-111.[doi:10.1126/science. 122.3159.108]
10 de Solla Price DJ. Networks of scientific papers. Science,1965, 149(3683):510-515.[doi:10.1126/science. 149.3683.510]
11陈悦,刘则渊.悄然兴起的科学知识图谱.科学学研究,2005, 23(2):149-154.[doi:10.3969/j.issn. 1003-2053.2005.02.002]
12秦长江,侯汉清.知识图谱——信息管理与知识管理的新领域.大学图书馆学报,2009, 27(1):30-37.[doi:10.3969/j.issn. 1002-1027.2009.01.007]
13徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述.电子科技大学学报,2016, 45(4):589-606.[doi:10.3969/j.issn.1001-0548.2016.04.012]
14 Fellbaum C. WordNet:An Electronic Lexical Database.Cambridge:MIT Press,1998.
15 Guha RV,Lenat D. CYC:A large-scale investment in knowledge infrastructure. Applied Artificial Intelligence,1990, 5(1):45-86.[doi:10.1080/08839519108927917]
16 You B,Liu XR,Li N, et al. Using information content to evaluate semantic similarity on HowNet. Proceedings of2012 Eighth International Conference on Computational Intelligence and Security. Guangzhou, China. 2013.
17 YAGO(database). http://www.yago.com/,[2015-01-08].
18 Auer S, Bizer C, Kobilarov G, et al. DBpedia:A nucleus for a web of open data. Proceedings of the Semantic Web,International Semantic Web Conference,Asian Semantic Web Conference, ISWC 2007+Aswc 2007. Busan, Korea.2007. 722-735.
19 Xu B, Xu Y, Liang JQ, et al. CN-DBpedia:A never-ending Chinese knowledge extraction system. Proceedings of the30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Arras,France. 2017. 428-438.
20 Niu X, Sun XR, Wang HF, et al. Zhishi.me-weaving Chinese linking open data. Proceedings of the 10th International Semantic Web Conference. Bonn, Germany. 2011. 205-220.
21胡芳槐.基于多种数据源的中文知识图谱构建方法研究[博士学位论文].上海:华东理工大学,2015.
22 GeoNames. http://www.geonames.org/,[2018-02-27].
23朱嫣岚,闵锦,周雅倩,等.基于HowNet的词汇语义倾向计算.中文信息学报,2006, 20(1):16-22.
24 THUOCL:清华大学开放中文词库.http://thuocl.thunlp.org/,[2018-09-07].
25大词林.http://www.bigcilin.com,[2017-03-27].
26 Zhishi.me. http://zhishi.me/api.[2018-12-23].
27 CN-Probase. http://kw.fudan.edu.cn/,[2016-05-18].
28 XLore. http://xlore.org/,[2019-03-23].
29 Lenat DB. CYC:A large-scale investment in knowledge infrastructure. Communications of the ACM, 1995, 38(11):33-38.[doi:10.1145/219717.219745]
30 Miller GA. WordNet:A lexical database for English.Communications of the ACM, 1995, 38(11):39-41.[doi:10.1145/219717.219748]
31高雪霞,炎士涛.基于WordNet词义消歧的语义检索研究.湘潭大学自然科学学报,2017, 39(2):118-121.
32 Speer R, Havasi C. ConceptNet 5:A large semantic network for relational knowledge. In:Gurevych I, Kim J, eds. The People's Web Meets NLP. Berlin:Springer, 2013. 161-176.
33 Lieberman H, Faaborg A, Daher W, et al. How to wreck a nice beach you sing calm incense. Proceedings of the 10th International Conference on Intelligent User Interfaces. San Diego, CA, USA. 2005. 278-280.
34 de Medeiros Caseli H, Sugiyama BA, Anacleto JC. Using common sense to generate culturally contextualized machine translation. Proceedings of the NAACL Hlt 2010 Young Investigators Workshop on Computational Approaches To Languages of the Americas. Los Angeles, CA, USA. 2010.24-31.
35 Speer R, Lowry-Duda J. ConceptNet at SemEval-2017 Task2:Extending word embeddings with multilingual relational knowledge. arXiv:1704.03560, 2017.
36 Nguyen TN, Takeda H, Nguyen K, et al. A novel method to predict type for DBpedia entity. In:Sieminski A,Kozierkiewicz A, Nunez M, et al, eds. Modern Approaches for Intelligent Information and Database Systems. Cham:Springer,2018.
37朝乐门,张勇,邢春晓.DBpedia及其典型应用.现代图书情报技术,2011,27(3):80-87.
38 Suchanek FM, Kasneci G, Weikum G. YAGO:A large ontology from wikipedia and WordNet. Journal of Web Semantics,2008,6(3):203-217.[doi:10.1016/j.websem.2008.06.001]
39 Thrasher EP,Perry AD. High-leverage apps for the mathematics classroom:WolframAlpha. The Mathematics Teacher, 2015, 109(1):66-70.[doi:10.5951/mathteacher.109.1.0066]
40 Wu WT, Li HS, Wang HX, et al. Probase:A probabilistic taxonomy for text understanding. Proceedings of the 2012ACM SIGMOD International Conference on Management of Data. Scottsdale, AZ, USA. 2012. 481-492.
41 NELL. http://rtw.ml.cmu.edu,[2015-12-27].
42 Erxleben F,Gunther M, Krotzsch M, et al. Introducing wikidata to the linked data web. Proceedings of the 13th International Semantic Web Conference. Riva del Garda,Italy. 2014. 50-65.
43贾君枝,薛秋红.Wikidata的特点、数据获取与应用.图书情报工作,2016, 60(17):136-141,148.
44 Navigli R, Ponzetto SP. BabelNet:Building a very large multilingual semantic network. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden. 2010. 216-225.
45 Navigli R,Ponzetto SP. BabelNet:The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 2012,193:217-250.[doi:10.1016/j.artint.2012.07.001]
46 Microsft Concept Graph. http://concept.research.microsoft.com/.
47王巍巍,王志刚,潘亮铭,等.双语影视知识图谱的构建研究.北京大学学报(自然科学版),2016, 52(1):25-34.
48 Zeng Y, Zhang T, Hao H. Active recommendation of tourist attractions based on visitors interests and semantic relatedness. In:Slezak D, Schaefer G, Vuong ST, et al, eds.Active Media Technology. Cham:Springer, 2014. 263-273.
49华人家谱关联数据集.http://openkg.cn/dataset/jp.
50乳腺癌知识图谱.http://wasp.cs.vu.n1/BreastCancerKG/.
51中医药学语义网络. http://www.tcminformatics.org/wiki/index.php/graph.
52中医药知识图谱.http://www.tcmkb.cn/kg/index.php.
53 Belleau F,Nolin MA,Tourigny N,et al. Bio2RDF:Towards a mashup to build bioinformatics knowledge systems.Journal of Biomedical Informatics, 2008, 41(5):706-716.[doi:10.1016/j.jbi.2008.03.004]
54 Sohn JS, Chung IJ. Dynamic FOAF management method for social networks in the social web environment. The Journal of Supercomputing, 2013, 66(2):633-648.[doi:10.1007/s11227-012-0847-x]
55 Amerland D.谷歌语义搜索.程龚,译.北京:人民邮电出版社,2015. 12-31.
56曹倩,赵一鸣.知识图谱的技术实现流程及相关应用.情报理论与实践,2015, 38(12):127-132.
57赵世奇.百度知识图谱:人工智能的知识心脏.福州:中国计算机大会,2017.
58 Fader. Paralex. http://knowitall.cs.washington.edu/paralex,[2016-05-08].
59 Wikipedia. Evi. http://en.wikipedia.org/wiki/Evi_(software),[2016-03-18].
60 Siri. https://www.apple.com/ios/siri/,[2016-05-02].
61 Wikipedia. Mobvoi. https://en.wikipedia.org/wiki/Mobvoi,[2018-03-20].
62 Wong W, Liu W, Bennamoun M. Ontology learning from text:A look back and into the future. ACM Computing Surveys, 2012, 44(4):Article No.20.
63徐波.百科知识图谱构建.http://cips-upload.bj.bcebos.com/ccks2017/upload/百科知识图谱构建_徐波CCKS2017.pdf.
64 RDF Access to Relational Databases. http://www.w3.org/2003/01/21-RDF-RDB-access/,[2003-01-21].
65邹磊.浅谈知识图谱数据管理.http://blog.openkg.cn/邹磊-浅谈知识图谱数据管理/,[2017-04-27].
66 Pan ZX, Heflin J. DLDB:Extending relational databases to support semantic web queries. Proceedings of the First International Workshop on Practical and Scalable Semantic Systems. Sanibel Island, FL, USA. 2003. 109-113.
67 Neo4j. https://neo4j.com/,[2018-03-06].
68 AllegroGraph. https://allegrograph.com/,[2018-02-08].
69 Zou L, Ozsu MT, Chen L, et al. gStore:A graph-based SPARQL query engine. The VLDB Journal, 2014, 23(4):565-590.[doi:10.1007/s00778-013-0337-7]
70 Erling O, Mikhailov I. Virtuoso:RDF Support in a native RDBMS. de Virgilio R, Giunchiglia F, Tanca L. Semantic Web Information Management. Berlin, Heidelberg:Springer,2010. 501-519.
71 mongoDB. https://www.mongodb.com/,[2018-03-08].
2 Pan JZ, Horrocks I. RDFS(FA):Connecting RDF(S)and OWL DL. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(2):192-206.[doi:10.1109/TKDE.2007.37]
3 Mcguinness DL, Harmelen F. OWL Web ontology language overview. W3C Recommendation, 2004, 63(45):990-996.
4 Brookes BC. The foundations of information science. PartⅢ:Quantitative aspects:Objective maps and subjective landscapes. Journal of Information Science, 1980,2(6):269-275.[doi:10.1177/016555158000200602]
5马费成.论布鲁克斯情报学基本理论.情报学报,1983, 2(4):314-324
6陈强,廖开际,奚建清.知识地图研究现状与展望.情报杂志,2006, 25(5):43-46.[doi:10.3969/j.issn.1002-1965.2006.05.016]
7 Zins C. Knowledge map of information science. Journal of the Association for Information Science and Technology,2007, 58(4):526-535.
8陈悦,陈超美,刘则渊,等.CiteSpace知识图谱的方法论功能.科学学研究,2015, 33(2):242-253.[doi:10.3969/j.issn.1003-2053.2015.02.009]
9 Garfield E. Citation indexes for science:A new dimension in documentation through association of ideas. Science, 1955,122(3159):108-111.[doi:10.1126/science. 122.3159.108]
10 de Solla Price DJ. Networks of scientific papers. Science,1965, 149(3683):510-515.[doi:10.1126/science. 149.3683.510]
11陈悦,刘则渊.悄然兴起的科学知识图谱.科学学研究,2005, 23(2):149-154.[doi:10.3969/j.issn. 1003-2053.2005.02.002]
12秦长江,侯汉清.知识图谱——信息管理与知识管理的新领域.大学图书馆学报,2009, 27(1):30-37.[doi:10.3969/j.issn. 1002-1027.2009.01.007]
13徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述.电子科技大学学报,2016, 45(4):589-606.[doi:10.3969/j.issn.1001-0548.2016.04.012]
14 Fellbaum C. WordNet:An Electronic Lexical Database.Cambridge:MIT Press,1998.
15 Guha RV,Lenat D. CYC:A large-scale investment in knowledge infrastructure. Applied Artificial Intelligence,1990, 5(1):45-86.[doi:10.1080/08839519108927917]
16 You B,Liu XR,Li N, et al. Using information content to evaluate semantic similarity on HowNet. Proceedings of2012 Eighth International Conference on Computational Intelligence and Security. Guangzhou, China. 2013.
17 YAGO(database). http://www.yago.com/,[2015-01-08].
18 Auer S, Bizer C, Kobilarov G, et al. DBpedia:A nucleus for a web of open data. Proceedings of the Semantic Web,International Semantic Web Conference,Asian Semantic Web Conference, ISWC 2007+Aswc 2007. Busan, Korea.2007. 722-735.
19 Xu B, Xu Y, Liang JQ, et al. CN-DBpedia:A never-ending Chinese knowledge extraction system. Proceedings of the30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Arras,France. 2017. 428-438.
20 Niu X, Sun XR, Wang HF, et al. Zhishi.me-weaving Chinese linking open data. Proceedings of the 10th International Semantic Web Conference. Bonn, Germany. 2011. 205-220.
21胡芳槐.基于多种数据源的中文知识图谱构建方法研究[博士学位论文].上海:华东理工大学,2015.
22 GeoNames. http://www.geonames.org/,[2018-02-27].
23朱嫣岚,闵锦,周雅倩,等.基于HowNet的词汇语义倾向计算.中文信息学报,2006, 20(1):16-22.
24 THUOCL:清华大学开放中文词库.http://thuocl.thunlp.org/,[2018-09-07].
25大词林.http://www.bigcilin.com,[2017-03-27].
26 Zhishi.me. http://zhishi.me/api.[2018-12-23].
27 CN-Probase. http://kw.fudan.edu.cn/,[2016-05-18].
28 XLore. http://xlore.org/,[2019-03-23].
29 Lenat DB. CYC:A large-scale investment in knowledge infrastructure. Communications of the ACM, 1995, 38(11):33-38.[doi:10.1145/219717.219745]
30 Miller GA. WordNet:A lexical database for English.Communications of the ACM, 1995, 38(11):39-41.[doi:10.1145/219717.219748]
31高雪霞,炎士涛.基于WordNet词义消歧的语义检索研究.湘潭大学自然科学学报,2017, 39(2):118-121.
32 Speer R, Havasi C. ConceptNet 5:A large semantic network for relational knowledge. In:Gurevych I, Kim J, eds. The People's Web Meets NLP. Berlin:Springer, 2013. 161-176.
33 Lieberman H, Faaborg A, Daher W, et al. How to wreck a nice beach you sing calm incense. Proceedings of the 10th International Conference on Intelligent User Interfaces. San Diego, CA, USA. 2005. 278-280.
34 de Medeiros Caseli H, Sugiyama BA, Anacleto JC. Using common sense to generate culturally contextualized machine translation. Proceedings of the NAACL Hlt 2010 Young Investigators Workshop on Computational Approaches To Languages of the Americas. Los Angeles, CA, USA. 2010.24-31.
35 Speer R, Lowry-Duda J. ConceptNet at SemEval-2017 Task2:Extending word embeddings with multilingual relational knowledge. arXiv:1704.03560, 2017.
36 Nguyen TN, Takeda H, Nguyen K, et al. A novel method to predict type for DBpedia entity. In:Sieminski A,Kozierkiewicz A, Nunez M, et al, eds. Modern Approaches for Intelligent Information and Database Systems. Cham:Springer,2018.
37朝乐门,张勇,邢春晓.DBpedia及其典型应用.现代图书情报技术,2011,27(3):80-87.
38 Suchanek FM, Kasneci G, Weikum G. YAGO:A large ontology from wikipedia and WordNet. Journal of Web Semantics,2008,6(3):203-217.[doi:10.1016/j.websem.2008.06.001]
39 Thrasher EP,Perry AD. High-leverage apps for the mathematics classroom:WolframAlpha. The Mathematics Teacher, 2015, 109(1):66-70.[doi:10.5951/mathteacher.109.1.0066]
40 Wu WT, Li HS, Wang HX, et al. Probase:A probabilistic taxonomy for text understanding. Proceedings of the 2012ACM SIGMOD International Conference on Management of Data. Scottsdale, AZ, USA. 2012. 481-492.
41 NELL. http://rtw.ml.cmu.edu,[2015-12-27].
42 Erxleben F,Gunther M, Krotzsch M, et al. Introducing wikidata to the linked data web. Proceedings of the 13th International Semantic Web Conference. Riva del Garda,Italy. 2014. 50-65.
43贾君枝,薛秋红.Wikidata的特点、数据获取与应用.图书情报工作,2016, 60(17):136-141,148.
44 Navigli R, Ponzetto SP. BabelNet:Building a very large multilingual semantic network. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden. 2010. 216-225.
45 Navigli R,Ponzetto SP. BabelNet:The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 2012,193:217-250.[doi:10.1016/j.artint.2012.07.001]
46 Microsft Concept Graph. http://concept.research.microsoft.com/.
47王巍巍,王志刚,潘亮铭,等.双语影视知识图谱的构建研究.北京大学学报(自然科学版),2016, 52(1):25-34.
48 Zeng Y, Zhang T, Hao H. Active recommendation of tourist attractions based on visitors interests and semantic relatedness. In:Slezak D, Schaefer G, Vuong ST, et al, eds.Active Media Technology. Cham:Springer, 2014. 263-273.
49华人家谱关联数据集.http://openkg.cn/dataset/jp.
50乳腺癌知识图谱.http://wasp.cs.vu.n1/BreastCancerKG/.
51中医药学语义网络. http://www.tcminformatics.org/wiki/index.php/graph.
52中医药知识图谱.http://www.tcmkb.cn/kg/index.php.
53 Belleau F,Nolin MA,Tourigny N,et al. Bio2RDF:Towards a mashup to build bioinformatics knowledge systems.Journal of Biomedical Informatics, 2008, 41(5):706-716.[doi:10.1016/j.jbi.2008.03.004]
54 Sohn JS, Chung IJ. Dynamic FOAF management method for social networks in the social web environment. The Journal of Supercomputing, 2013, 66(2):633-648.[doi:10.1007/s11227-012-0847-x]
55 Amerland D.谷歌语义搜索.程龚,译.北京:人民邮电出版社,2015. 12-31.
56曹倩,赵一鸣.知识图谱的技术实现流程及相关应用.情报理论与实践,2015, 38(12):127-132.
57赵世奇.百度知识图谱:人工智能的知识心脏.福州:中国计算机大会,2017.
58 Fader. Paralex. http://knowitall.cs.washington.edu/paralex,[2016-05-08].
59 Wikipedia. Evi. http://en.wikipedia.org/wiki/Evi_(software),[2016-03-18].
60 Siri. https://www.apple.com/ios/siri/,[2016-05-02].
61 Wikipedia. Mobvoi. https://en.wikipedia.org/wiki/Mobvoi,[2018-03-20].
62 Wong W, Liu W, Bennamoun M. Ontology learning from text:A look back and into the future. ACM Computing Surveys, 2012, 44(4):Article No.20.
63徐波.百科知识图谱构建.http://cips-upload.bj.bcebos.com/ccks2017/upload/百科知识图谱构建_徐波CCKS2017.pdf.
64 RDF Access to Relational Databases. http://www.w3.org/2003/01/21-RDF-RDB-access/,[2003-01-21].
65邹磊.浅谈知识图谱数据管理.http://blog.openkg.cn/邹磊-浅谈知识图谱数据管理/,[2017-04-27].
66 Pan ZX, Heflin J. DLDB:Extending relational databases to support semantic web queries. Proceedings of the First International Workshop on Practical and Scalable Semantic Systems. Sanibel Island, FL, USA. 2003. 109-113.
67 Neo4j. https://neo4j.com/,[2018-03-06].
68 AllegroGraph. https://allegrograph.com/,[2018-02-08].
69 Zou L, Ozsu MT, Chen L, et al. gStore:A graph-based SPARQL query engine. The VLDB Journal, 2014, 23(4):565-590.[doi:10.1007/s00778-013-0337-7]
70 Erling O, Mikhailov I. Virtuoso:RDF Support in a native RDBMS. de Virgilio R, Giunchiglia F, Tanca L. Semantic Web Information Management. Berlin, Heidelberg:Springer,2010. 501-519.
71 mongoDB. https://www.mongodb.com/,[2018-03-08].