Please wait a minute...

Frontiers of Computer Science

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 YAN1,2, 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
Download: PDF(542 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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 Authors: 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:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-5228-9
http://journal.hep.com.cn/fcs/EN/Y2018/V12/I1/55
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Jihong YAN
Chengyu WANG
Wenliang CHENG
Ming GAO
Aoying ZHOU
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
Related articles from Frontiers Journals
[1] Jing WANG, Zhijing LIU, Hui ZHAO. A probabilistic model with multi-dimensional features for object extraction[J]. Front Comput Sci, 2012, 6(5): 513-526.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed