A retrospective of knowledge graphs

doi:10.1007/s11704-016-5228-9

Front. Comput. Sci.

2018, Vol. 12

Issue (1) : 55-74 https://doi.org/10.1007/s11704-016-5228-9

REVIEW ARTICLE

A retrospective of knowledge graphs

Jihong YAN^1,², Chengyu WANG¹, Wenliang CHENG¹, Ming GAO¹(

), Aoying ZHOU³

¹. Institute for Data Science and Engineering, East China Normal University, Shanghai 200062, China
². Institute for Computer and Information Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China
³. Shanghai Key Lab for Trustworthy Computing, East China Normal University, Shanghai 200062, China

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Abstract

Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner.

In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.

Keywords knowledge graph knowledge base information extraction logical reasoning graph database

Corresponding Author(s): Ming GAO

Just Accepted Date: 22 February 2016 Online First Date: 17 October 2016 Issue Date: 12 January 2018

Cite this article:

Jihong YAN,Chengyu WANG,Wenliang CHENG, et al. A retrospective of knowledge graphs[J]. Front. Comput. Sci., 2018, 12(1): 55-74.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5228-9
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I1/55

1	Frost A. Introduction to Knowledge Base Systems. Macmillan Publishing Company, Inc., 1986
2	Hua W, Song Y Q, Wang H X, Zhou X F. Identifying users’ topical tasks in Web search. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013, 93–102 https://doi.org/10.1145/2433396.2433410
3	Song Y Q, Wang H X, Wang Z Y, Li H S, Chen W Z. Short text conceptualization using a probabilistic knowledgebase. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 2330–2336
4	Wang J J, Wang H X, Wang Z Y, Zhu K Q. Understanding tables on the Web. In: Proceedings of the International Conference on Conceptual Modeling. 2012, 141–155 https://doi.org/10.1007/978-3-642-34002-4_11
5	Deshpande O, Lamba D, Tourn S, Subramaniam S, Rajaraman A, Harinarayan V, Doan A. Building, maintaining, and using knowledge bases: a report from the trenches. In: Proceedings of ACM Special Interest Group on Management of Data. 2013, 1209–1220 https://doi.org/10.1145/2463676.2465297
6	Hoffart J, Suchanek F, Berberich K, Weikum G. YAGO2: a spatially and temporally enhanced knowledge base from wikipedia. Artificial Intelligence, 2013, 194: 28–61 https://doi.org/10.1016/j.artint.2012.06.001
7	Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes P, Hellmann S, Morsey M, Kleef P, Auer S, Bizer C. DBpedia — a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web Journal, 2015, 6(2): 167–195
8	Kushmerick N. Wrapper induction: efficiency and expressiveness. Artificial Intelligence, 2000, 118: 15–68 https://doi.org/10.1016/S0004-3702(99)00100-9
9	Muslea I, Minton S, Knoblock C. Hierarchical wrapper induction for semistructured information sources. Autonomous Agents and Multi- Agent Systems, 2001, 4(1–2): 93–114 https://doi.org/10.1023/A:1010022931168
10	Buttler D, Liu L, Pu C. A fully automated object extraction system for the World Wide Web. In: Proceedings of ACM International Conference on Distributed Computing Systems. 2001, 361–370 https://doi.org/10.1109/ICDSC.2001.918966
11	Wang R, Cohen W. Language-independent set expansion of named entities using the Web. In: Proceedings of the 7th IEEE International Conference on Data Mining. 2007, 342–350 https://doi.org/10.1109/icdm.2007.104
12	Nie Z, Wen J R, Zhang B, Ma W Y. 2D conditional random fields for Web information extraction. In: Proceedings of the 22nd International Conference on Machine Learning. 2005, 1044–1051
13	Finn A, Kushmerick N. Multi-level boundary classification for information extraction. In: Proceedings of the 15th European Conference on Machine Learning. 2004, 111–122 https://doi.org/10.1007/978-3-540-30115-8_13
14	Sutton C, Rohanimanesh K, Mccallum A. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. In: Proceedings of the 21st International Conference on Machine Learning. 2004, 693–723 https://doi.org/10.1145/1015330.1015422
15	Wellner B, Mccallum A, Peng F, Hay M. An integrated, conditional model of information extraction and coreference with application to citation matching. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 2004, 593–601
16	Zhu J, Nie Z, Wen J R. Simultaneous record detection and attribute labeling in Web data extraction. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 494–503 https://doi.org/10.1145/1150402.1150457
17	Marrero M, Urbano J, Sánchez-Cuadrado S, Morato J, Berbís J. Named entity recognition: fallacies, challenges and opportunities. Computer Standards & Interfaces, 2013, 35(5): 482–489 https://doi.org/10.1016/j.csi.2012.09.004
18	Zhou G D, Su J. Named entity recognition using an hmm-based chunk tagger. In: Proceedings of the 40th AnnualMeeting of the Association for Computational Linguistics, 2002, 473–480
19	Finkel J, Grenager T, Manning C. Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2005, 363–370 https://doi.org/10.3115/1219840.1219885
20	Liu X H, Zhang S D, Wei F R, Zhou M. Recognizing named entities in tweets. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2011, 359–367
21	Pan S J, Toh Z, Su J. Transfer joint embedding for cross-domain named entity recognition. ACM Transactions on Information Systems, 2013, 31(2): 7 https://doi.org/10.1145/2457465.2457467
22	Prokofyev R, Demartini G, Cudré-Mauroux P. Effective named entity recognition for idiosyncratic Web collections. In: Proceedings of the 23rd International Conference on World Wide Web. 2014, 397–408 https://doi.org/10.1145/2566486.2568013
23	Shen W, Wang J Y, Luo P, Wang M. Linden: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 449–458 https://doi.org/10.1145/2187836.2187898
24	Bagga A, Baldwin B. Entity-based cross-document coreferencing using the vector space model. In: Proceedings of the 17th International Conference on Computational Linguistics. 1998, 79–85. https://doi.org/10.3115/980451.980859
25	Pedersen T, Purandare A, Kulkarni A. Name discrimination by clustering similar contexts. In: Proceedings of Computational Linguistics and Intelligent Text Processing. 2005, 226–237 https://doi.org/10.1007/978-3-540-30586-6_24
26	Chen Y, Martin J. Towards robust unsupervised personal name disambiguation. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007, 190–198
27	Lasek I, Vojtás P. Various approaches to text representation for named entity disambiguation In: Proceedings of International Conference on Information Integration and Web-based Applications and Services. 2013, 242–259
28	Shen W, Wang J Y, Luo P, Wang M. Liege: link entities in Web lists with knowledge base. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 1424–1432 https://doi.org/10.1145/2339530.2339753
29	Guo Y H, Che W X, Liu T, Li S. A graph-based method for entity linking. In: Proceedings of the 5th International Joint Conference on Natural Language Processing. 2011, 1010–1018
30	Han X P, Sun L, Zhao J. Collective entity linking inWeb text: a graphbased method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 765–774
31	Han X P, Sun L. A generative entity-mention model for linking entities with knowledge base. In: Proceedings of Annual Meeting on Association for Computational Linguistics, 2011, 945–954
32	Sil A, Yates A. Re-ranking for joint named-entity recognition and inking. In: Proceedings of the 6th Workshop on Ph.D. Students in Information and Knowledge Management. 2013, 2369–2374
33	Liu X H, Zhou M, Zhou X F, Fu Z, Wei F. Joint inference of named entity recognition and normalization for tweets. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2012, 526–535
34	Russell S, Norvig P. Artificial intelligence: a modern approach. New Jersey: Prentice-Hall, Egnlewood Cliffs, 1995, 25
35	Collins M, Duffy N. New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2002, 263–270
36	Knoke D, Burke P. Log-linear Models. New York: SAGE Publications. 1980, 20 https://doi.org/10.4135/9781412984843
37	Ratnaparkhi A. A maximum entropy model for part-of-speech tagging. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1996, 133–142
38	Kambhatla N. Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL on Interactive Poster and Demonstration Sessions. 2004, 20 https://doi.org/10.3115/1219044.1219066
39	Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C. Text classification using string kernels. Journal of Machine Learning Research, 2002, 2(3): 419–444
40	Bunescu R, Mooney R. A shortest path dependency kernel for relation extraction. In: Proceedings of Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005, 724–731 https://doi.org/10.3115/1220575.1220666
41	Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction. Journal of Machine Learning Research, 2003, 3(3): 1083–1106
42	Culotta A, Sorensen J. Dependency tree kernels for relation extraction. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2004, 423–429 https://doi.org/10.3115/1218955.1219009
43	Bunescu R, Mooney R. Subsequence kernels for relation extraction. Advances in Neural Information Processing Systems. 2005, 171–178
44	Hearst M. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics. 1992, 539–545 https://doi.org/10.3115/992133.992154
45	Brin S. Extracting patterns and relations from the World Wide Web. In: Proceedings of WebDBWorkshop at the 6th International Conference on Extending Database Technology. 1998, 172–183
46	Agichtein E, Gravano L. Snowball: extracting relations from large plain-text collections. In: Proceedings of the 5th ACM International Conference on Digital Libraries. 2000, 85–94 https://doi.org/10.1145/336597.336644
47	Etzioni O, Cafarella M, Downey D, Popescu A, Shaked T, Soderland S, Weld D, Yates A. Unsupervised named-entity extraction from the Web: an experimental study. Artificial Intelligence, 2005, 165(1): 91–134 https://doi.org/10.1016/j.artint.2005.03.001
48	Nakashole N, Theobald M, Weikum G. Scalable knowledge harvesting with high precision and high recall. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 227–236 https://doi.org/10.1145/1935826.1935869
49	Banko M, Cafarella M, Soderland S, Broadhead M, Etzioni O. Open information extraction from the Web. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence. 2007, 2670–2676
50	Downey D, Etzioni O, Soderland S. A probabilistic model of redundancy in information extraction. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. 2005, 1034–1041
51	Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011, 1535–1545
52	Etzioni O, Fader A, Christensen J, Soderland S. Open information extraction: the second generation. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 3–10
53	Cergani E, Miettinen P. Discovering relations using matrix factorization methods. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 1549–1552 https://doi.org/10.1145/2505515.2507841
54	Bian J, Gao B, Liu T Y. Knowledge-powered deep learning for word embedding. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2014, 132–148 https://doi.org/10.1007/978-3-662-44848-9_9
55	Lin Y K, Liu Z Y, Luan H B, Sun M S, Rao S W, Liu S. Modeling relation paths for representation learning of knowledge bases. 2015, arXiv:1506.00379
56	Weston J, Bordes A, Yakhnenko O, Usunier N. Connecting language and knowledge bases with embedding models for relation extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, 1366–1371
57	Yu M, Gormley M, Dredze M. Combining word embeddings and feature embeddings for fine-grained relation extraction. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015, 1374–1379 https://doi.org/10.3115/v1/n15-1155
58	McDonald R, Pereira F, Kulick S, Winters R, Jin Y, White P. Simple algorithms for complex relation extraction with applications to biomedical IE. In: Proceedings of Annual Meeting on Association for Computational Linguistics. 2005, 491–498 https://doi.org/10.3115/1219840.1219901
59	Getoor L, Taskar B. Introduction to statistical relational learning. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2010, 173(4): 934–935 https://doi.org/10.1111/j.1467-985X.2010.00663_3.x
60	Ueda K. Guarded horn clauses. In: Proceedings of the Conference on Logic Programming. 1985, 168–179
61	Suchanek F, Sozio M, Weikum G. Sofie: a self-organizing framework for information extraction. In: Proceedings of the 18th International Conference on World Wide Web. 2009, 631–640 https://doi.org/10.1145/1526709.1526794
62	Muggleton S, Feng C. Efficient induction of logic programs. Inductive Logic Programming, 1992, 38: 281–298
63	Quinlan J, Cameron-Jones R. Foil: a midterm report. In: Proceedings of the European Conference on Machine Learning. 1993, 3–20 https://doi.org/10.1007/3-540-56602-3_124
64	Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr. E R, Michell T M. Toward an architecture for never-ending language learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 5: 3
65	Lao N, Mitchell T, Cohen W. Random walk inference and learning in a large scale knowledge base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 529–539
66	Richards B, Mooney R. Learning relations by pathfinding. In: Proceedings of the 10th National Conference on Artificial Intelligence. 1992, 50–55
67	Tong H, Faloutsos C, Pan J. Random walk with restart: fast solutions and applications. Knowledge and Information Systems, 2008, 14(3): 327–346 https://doi.org/10.1007/s10115-007-0094-2
68	Lao N, Cohen W. Relational retrieval using a combination of pathconstrained random walks. Machine Learning, 2010, 81(1): 53–67 https://doi.org/10.1007/s10994-010-5205-8
69	Kotecha J, Djuric P. Gaussian particle filtering. In: Proceedings of IEEE Transactions on Signal Processing. 2003, 51(10): 2592–2601 https://doi.org/10.1109/TSP.2003.816758
70	Thrun S, Burgard W, Fox D. Probabilistic robotics (intelligent robotics and autonomous agents series). Intelligent Robotics and Autonomous Agents, 2002, 45(3): 52–57
71	Gardner M, Talukdar P, Kisiel B, Mitchell T. Improving learning and inference in a large knowledge-base using latent syntactic cues. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, 833–838
72	Wang W, Mazaitis K, Lao N, Mitchell T, Cohen W. Efficient inference and learning in a large knowledge base: reasoning with extracted information using a locally groundable first-order probabilistic logic. 2014, arXiv:1404.3301
73	Cussens J. Parameter estimation in stochastic logic programs. Machine Learning, 2001, 44(3): 245–271 https://doi.org/10.1023/A:1010924021315
74	Zinkevich M, Weimer M, Smola A, Li L. Parallelized stochastic gradient descent. In: Proceedings of Advances in Neural Information Processing Systems. 2010, 2595–2603
75	Socher R, Chen D, Manning C, Ng A. Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of Advances in Neural Information Processing Systems. 2013, 926–934
76	Socher R, Perelygin A, Wu J, Chuang J, Manning C, Ng A, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, 1631: 1642–1653
77	Bordes A, Usunier N, García-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 2013, 2787–2795
78	Wang Z, Zhang J W, Feng J L, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1112–1119
79	Lin Y K, Liu Z, Sun M S, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2181–2187
80	Bordes A, Glorot X, Weston J, Bengio Y. Joint learning of words and meaning representations for open-text semantic parsing. In: Proceedings of the 15th International Conference on Artificial Intelligence and Statistics. 2012, 127–135
81	Bordes A, Weston J, Collobert R, Bengio Y. Learning structured embeddings of knowledge bases. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011
82	Sutskever I, Salakhutdinov R, Tenenbaum J. Modelling relational data using bayesian clustered tensor factorization. Advances in Neural Information Processing Systems, 2009, 1821–1828
83	Weikum G, Theobald M. From information to knowledge: harvesting entities and relationships from Web sources. In: Proceedings of the 29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 2010, 65–76 https://doi.org/10.1145/1807085.1807097
84	Richardson M, Domingos P. Markov logic networks. Machine learning, 2006, 62(1–2): 107–136 https://doi.org/10.1007/s10994-006-5833-1
85	Kindermann R, Snell J. Markov Random Fields and Their Applications. Providence: American Mathematical Society. 1980 https://doi.org/10.1090/conm/001
86	Poon H, Domingos P, Sumner M. A general method for reducing the complexity of relational inference and its application to mcmc. In: Proceedings of the 23rd National Conference on Artificial Intelligence. 2008, 1075–1080
87	Resnik P, Hardisty E. Gibbs Sampling for the Uninitiated. Technical Report, DTIC Document. 2010
88	Duchi J, Tarlow D, Elidan G, Koller D. Using combinatorial optimization within max-product belief propagation. In: Proceedings of Advances in Neural Information Processing Systems. 2006, 369–376
89	Zhu J, Nie Z, Liu X, Zhang B, Wen J R. Statsnowball: a statistical approach to extracting entity relationships. In: Proceedings of the 18th International Conference on World Wide Web. 2009, 101–110 https://doi.org/10.1145/1526709.1526724
90	Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning. 2001, 282–289
91	Chang M, Ratinov L, Rizzolo N, Roth D. Learning and inference with constraints In: Proceedings of the 23rd Conference AAAI on Artificial Intelligence. 2008, 1513–1518
92	Carlson A, Betteridge J, Wang R, Hruschka Jr. E, Mitchell T. Coupled semi-supervised learning for information extraction. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2010, 101–110 https://doi.org/10.1145/1718487.1718501
93	Das S, Agrawal D, Abbadi A. G-store: a scalable data store for transactional multi key access in the cloud. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 163–174 https://doi.org/10.1145/1807128.1807157
94	Momjian B. PostgreSQL: introduction and concepts. New York: Addison-Wesley, 2001
95	Shao B, Wang H X, Li Y T. Trinity: A distributed graph engine on a memory cloud. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2013, 505–516 https://doi.org/10.1145/2463676.2467799
96	Hirabayashi M. Tokyo Cabinet: A Modern Implementation of DBM. , 2010
97	Anderson J, Lehnardt J, Slater N. CouchDB: the Definitive Guide. Sebastopol: O’Reilly Media Inc., 2010
98	Mondal J, Deshpande A. Managing large dynamic graphs efficiently. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2012, 145–156 https://doi.org/10.1145/2213836.2213854
99	Aasman J. Allegro graph: RDF triple database. Technical Report. 2006
100	Miller J. Graph database applications and concepts with Neo4j. In: Proceedings of the Southern Association for Information Systems Conference. 2013, 141–147
101	Martinez-Bazan N, Gomez-Villamor S, Escale-Claveras F. Dex: a high-performance graph database management system. In: Proceedings of IEEE International Conference on Data Engineering Workshops. 2011, 124–127 https://doi.org/10.1109/icdew.2011.5767616
102	Iordanov B. Hypergraphdb: a generalized graph database. In: Proceedings of the 11th International Conference on Web-Age Information Management Workshops. 2010, 25–36 https://doi.org/10.1007/978-3-642-16720-1_3
103	Malewicz G, Austern M, Bik A, Dehnert J, Horn I, Leiser N, Czajkowski G. Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD International Conference onManagement of Data. 2010, 135–146 https://doi.org/10.1145/1807167.1807184
104	Lans R. Infinitegraph: extending business, social and government intelligence with graph analytics. The Analysis, 2010
105	Matuszek C, Cabral J, Witbrock M, DeOliveira J. An introduction to the syntax and content of Cyc. In: Proceedings of AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering. 2006, 44–49
106	Liu H, Singh P. Conceptnet a practical commonsense reasoning toolkit. BT Technology Journal, 2004, 22(4): 211–226 https://doi.org/10.1023/B:BTTJ.0000047600.45421.6d
107	Miller G. Wordnet: a lexical database for English. Communications of ACM, 1995, 38(11): 39–41 https://doi.org/10.1145/219717.219748
108	Magnini B, Strapparava C, Ciravegna F, Pianta E. A project for the constraction of an italian lexical knowledge base in the framework of wordnet. In: Proceedings of International Workshop on the “Future of the Dictionary”. 1994
109	Zhang Z D, Dong Q. Hownet—a hybrid language and knowledge resource. In: Proceedings of Natural Language Processing and Knowledge Engineering. 2003, 820–824
110	Baker C, Fillmore C, Lowe J. The berkeley framenet project. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics. 1998, 86–90
111	Wu W, Li H, Wang H X, Zhu K Q. Probase: a probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 2012, 481–492 https://doi.org/10.1145/2213836.2213891
112	Bollacker K, Cook R, Tufts P. Freebase: a shared database of structured general human knowledge. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence. 2007, 1962–1963
113	Weld D, Hoffmann R, Wu F. Using wikipedia to bootstrap open information extraction. ACM SIGMOD Record, 2008, 37(4): 62–68 https://doi.org/10.1145/1519103.1519113
114	Wu F, Weld D. Autonomously semantifying wikipedia. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management. 2007, 41–50 https://doi.org/10.1145/1321440.1321449
115	Wu F, Weld D. Automatically refining the wikipedia infobox ontology. In: Proceedings of the 17th International World Wide Web Conference. 2008, 635–644 https://doi.org/10.1145/1367497.1367583
116	Suchanek F, Kasneci G, Weikum G. Yago: a core of semantic knowledge. In: Proceedings of the 16th International WorldWideWeb Conference. 2007, 697–706 https://doi.org/10.1145/1242572.1242667
117	Biega J, Kuzey E, Suchanek F. Inside YAGO2s: a transparent information extraction architecture. In: Proceedings of the 22nd International World Wide Web Conference. 2013, 325–328 https://doi.org/10.1145/2487788.2487935
118	Singhal A. Introducing the knowledge graph: things, not strings. Official Google Blog, 2012
119	Sengupta S. Facebook unveils a new search tool. New York Times, 2013
120	Wang C Y, Gao M, He X F, Zhang R. Challenges in Chinese knowledge graph construction. In: Proceedings of the 31st IEEE International Conference on Data Engineering Workshops. 2015, 59–61 https://doi.org/10.1109/icdew.2015.7129545

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