Sage Journals: Discover world-class research

Abstract

In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term “Knowledge Graph” in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.

Keywords

Knowledge graphs refinement completion correction error detection evaluation

Get full access to this article

View all access options for this article.

References

Acosta,

Zaveri,

Simperl,

Kontokostas,

Auer and

Lehmann, Crowdsourcing Linked Data quality assessment, in: The Semantic Web-ISWC 2013, LNCS, Vol. 8219, Springer, Berlin, Heidelberg, 2013, pp. 260–276. doi:10.1007/978-3-642-41338-4_17.

Akoglu,

Tong and

Koutra, Graph based anomaly detection and description: A survey, Data Mining and Knowledge Discovery 29(3) (2014), 1–63. doi:10.1007/s10618-014-0365-y.

A.P.

Aprosio,

Giuliano and

Lavelli, Extending the coverage of DBpedia properties using distant supervision over Wikipedia, in: NLP&DBpedia, CEUR Workshop Proceedings, Vol. 1064, 2013, http://ceur-ws.org/Vol-1064/ .

A.P.

Aprosio,

Giuliano and

Lavelli, Automatic expansion of DBpedia exploiting Wikipedia cross-language information, in: The Semantic Web: Semantics and Big Data, LNCS, Vol. 7882, Springer, Berlin, Heidelberg, 2013, pp. 397–411. doi:10.1007/978-3-642-38288-8_27.

Bizer,

Heath and

Berners-Lee, Linked Data – the story so far, International Journal on Semantic Web and Information Systems 5(3) (2009), 1–22. doi:10.4018/jswis.2009081901.

Blanco,

B.B.

Cambazoglu,

Mika and

Torzec, Entity recommendations in Web search, in: The Semantic Web-ISWC 2013, LNCS, Vol. 8219, Springer, Berlin, Heidelberg, 2013, pp. 33–48. doi:10.1007/978-3-642-41338-4_3.

D.M.

Blei,

A.Y.

Ng and

M.I.

Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research 3 (2003), 993–1022.

Bock,

Haase,

Ji and

Volz, Benchmarking OWL reasoners, in: Workshop on Advancing Reasoning on the Web: Scalability and Commonsense, CEUR Workshop Proceedings, Vol. 350, 2008, http://ceur-ws.org/Vol-350/ .

Bollacker,

Evans,

Paritosh,

Sturge and

Taylor, Freebase: A collaboratively created graph database for structuring human knowledge, in: Proc. of the 2008 ACM SIGMOD International Conference on Management of Data, ACM, New York, 2008, pp. 1247–1250. doi:10.1145/1376616.1376746.

10.

Bordes and

Gabrilovich, Constructing and mining Web-scale knowledge graphs, in: Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, 2014, p. 1967. doi:10.1145/2623330.2630803.

11.

M.M.

Breunig,

H.-P.

Kriegel,

R.T.

Ng and

Sander, LOF: Identifying density-based local outliers, ACM SIGMOD Record 29(2) (2000), 93–104. doi:10.1145/335191.335388.

12.

Bryl and

Bizer, Learning conflict resolution strategies for cross-language Wikipedia data fusion, in: Proc. of the Companion Publication of the 23rd International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, Geneva, 2014, pp. 1129–1134. doi:10.1145/2567948.2578999.

13.

Buitelaar,

Cimiano and

Magnini, Ontology Learning from Text: Methods, Evaluation and Applications, Frontiers in Artificial Intelligence and Applications, Vol. 123, IOS Press, Clifton, VA, 2005.

14.

Carlson,

Betteridge,

R.C.

Wang,

E.R.

HruschkaJr. and

T.M.

Mitchell, Coupled semi-supervised learning for information extraction, in: Proc. of the Third ACM International Conference on Web Search and Data Mining, ACM, New York, 2010, pp. 101–110. doi:10.1145/1718487.1718501.

15.

Chandola,

Banerjee and

Kumar, Anomaly detection: A survey, ACM Computing Surveys (CSUR) 41(3) (2009). doi:10.1145/1541880.1541882.

16.

Cheze and

J.-M.

Poggi, Iterated boosting for outlier detection, in: Data Science and Classification, Springer, Berlin, Heidelberg, 2006, pp. 213–220. doi:10.1007/3-540-34416-0_23.

17.

Cimiano,

Lopez,

Unger,

Cabrio,

A.-C.N.

Ngomo and

Walter, Multilingual question answering over Linked Data (QALD-3): Lab overview, in: Information Access Evaluation. Multilinguality, Multimodality, and Visualization, LNCS, Vol. 8138, Springer, Berlin, Heidelberg, 2013, pp. 321–332. doi:10.1007/978-3-642-40802-1_30.

18.

G.K.D.

de Vries, A fast approximation of the Weisfeiler-Lehman graph kernel for RDF data, in: Machine Learning and Knowledge Discovery in Databases, LNCS, Vol. 8188, Springer, Berlin, Heidelberg, 2013, pp. 606–621. doi:10.1007/978-3-642-40988-2_39.

19.

Dentler,

Cornet,

ten Teije and

de Keizer, Comparison of reasoners for large ontologies in the OWL 2 EL profile, Semantic Web 2(2) (2011), 71–87. doi:10.3233/SW-2011-0034.

20.

Ding,

Kolari,

Ding and

Avancha, Using ontologies in the semantic web: A survey, in: Ontologies, Integrated Series in Information Systems, Vol. 14, Springer, US, 2007, pp. 79–113. doi:10.1007/978-0-387-37022-4_4.

21.

X.L.

Dong,

Murphy,

Gabrilovich,

Heitz,

Horn,

Lao,

Strohmann,

Sun and

Zhang, Knowledge Vault: A Web-scale approach to probabilistic knowledge fusion, in: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, 2014, pp. 601–610. doi:10.1145/2623330.2623623.

22.

Dragisic,

Eckert,

Euzenat,

Faria,

Ferrara,

Granada,

Ivanova,

Jimenez-Ruiz,

Kempf,

Lambrix et al., Results of the ontology alignment evaluation initiative 2014 in: International Workshop on Ontology Matching, CEUR Workshop Proceedings, Vol. 1317, 2014, pp. 61–104, http://ceur-ws.org/Vol-1317/ .

23.

Dutta,

Meilicke and

S.P.

Ponzetto, A probabilistic approach for integrating heterogeneous knowledge sources, in: The Semantic Web: Trends and Challenges, LNCS, Vol. 8465, Springer, Switzerland, 2014, pp. 286–301. doi:10.1007/978-3-319-07443-6_20.

24.

Fleischhacker,

Paulheim,

Bryl,

Völker and

Bizer, Detecting errors in numerical Linked Data using cross-checked outlier detection, in: The Semantic Web-ISWC 2014, LNCS, Vol. 8796, Springer, Switzerland, 2014, pp. 357–372. doi:10.1007/978-3-319-11964-9_23.

25.

Freund and

R.E.

Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences 55(1) (1997), 119–139. doi:10.1006/jcss.1997.1504.

26.

L.A.

Galárraga,

Teflioudi,

Hose and

Suchanek, AMIE: Association rule mining under incomplete evidence in ontological knowledge bases, in: Proc. of the 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Geneva, 2013, pp. 413–422.

27.

Gangemi,

Guarino,

Masolo and

Oltramari, Sweetening WordNet with DOLCE, AI Magazine 24(3) (2003), 13–24. doi:10.1609/aimag.v24i3.1715.

28.

Gangemi,

A.G.

Nuzzolese,

Presutti,

Draicchio,

Musetti and

Ciancarini, Automatic typing of DBpedia entities, in: The Semantic Web-ISWC 2012, LNCS, Vol. 7649, Springer, Berlin, Heidelberg, 2012, pp. 65–81. doi:10.1007/978-3-642-35176-1_5.

29.

Gerber,

Hellmann,

Bühmann,

Soru,

Usbeck and

A.-C.N.

Ngomo, Real-time RDF extraction from unstructured data streams, in: The Semantic Web-ISWC 2013, LNCS, Vol. 8218, Springer, Berlin, Heidelberg, 2013, pp. 135–150. doi:10.1007/978-3-642-41335-3_9.

30.

Gerber and

A.-C.N.

Ngomo, Bootstrapping the Linked Data web, in: Workshop on Web Scale Knowledge Extraction, 2011, http://iswc2011.semanticweb.org/fileadmin/iswc/Papers/Workshops/WeKEx/paper_3.pdf .

31.

Getoor and

C.P.

Diehl, Link mining: A survey, ACM SIGKDD Explorations Newsletter 7(2) (2005), 3–12. doi:10.1145/1117454.1117456.

32.

Groza,

Oellrich and

Collier, Using silver and semi-gold standard corpora to compare open named entity recognisers, in: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, Piscataway, New Jersey, 2013, pp. 481–485. doi:10.1109/BIBM.2013.6732541.

33.

Halpin,

P.J.

Hayes,

J.P.

McCusker,

D.L.

McGuinness and

H.S.

Thompson, When owl:sameAs isn’t the same: An analysis of identity in Linked Data, in: The Semantic Web-ISWC 2010, LNCS, Vol. 6496, Springer, Berlin, Heidelberg, 2010, pp. 305–320. doi:10.1007/978-3-642-17746-0_20.

34.

Hawkins,

He,

Williams and

Baxter, Outlier detection using replicator neural networks, in: Data Warehousing and Knowledge Discovery, LNCS, Vol. 2454, Springer, Berlin, Heidelberg, 2002, pp. 170–180. doi:10.1007/3-540-46145-0_17.

35.

He,

Xu and

Deng, Discovering cluster-based local outliers, Pattern Recognition Letters 24(9) (2003), 1641–1650. doi:10.1016/S0167-8655(03)00003-5.

36.

M.A.

Hearst, Automatic acquisition of hyponyms from large text corpora, in: Proc. of the 14th Conference on Computational Linguistics (Volume 2), Association for Computational Linguistics, Stroudsburg, PA, 1992, pp. 539–545. doi:10.3115/992133.992154.

37.

Hees,

Roth-Berghofer,

Biedert,

Adrian and

Dengel, BetterRelations: Using a game to rate Linked Data triples, in: KI 2011: Advances in Artificial Intelligence, LNCS, Vol. 7006, Springer, Berlin, Heidelberg, 2011, pp. 134–138. doi:10.1007/978-3-642-24455-1_12.

38.

Hipp,

Güntzer and

Nakhaeizadeh, Algorithms for association rule mining – a general survey and comparison, ACM SIGKDD Explorations Newsletter 2(1) (2000), 58–64. doi:10.1145/360402.360421.

39.

V.J.

Hodge and

Austin, A survey of outlier detection methodologies, Artificial Intelligence Review 22(2) (2004), 85–126. doi:10.1007/s10462-004-4304-y.

40.

Hoffart,

M.A.

Yosef,

Bordino,

Fürstenau,

Pinkal,

Spaniol,

Taneva,

Thater and

Weikum, Robust disambiguation of named entities in text, in: Proc. of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Edinburgh, 2011, pp. 782–792, http://www.aclweb.org/anthology/D11-1072 .

41.

Huang,

Nickel,

Tresp and

H.-P.

Kriegel, A scalable kernel approach to learning in semantic graphs with applications to Linked Data, Semantic Web 5(1) (2010), 5–22. doi:10.3233/SW-130100.

42.

Jang, Megawati

Choi and

M.Y.

Yi, Semi-automatic quality assessment of Linked Data without requiring ontology, in: Workshop on NLP and DBpedia, 2015, https://nlpdbpedia2015.files.wordpress.com/2015/08/nlpdbpedia_2015_submission_2.pdf .

43.

Ji,

Gao and

Huang, Reasoning with noisy Semantic Data, in: The Semanic Web: Research and Applications, LNCS, Vol. 6644, Springer, Berlin, Heidelberg, 2011, pp. 497–502. doi:10.1007/978-3-642-21064-8_42.

44.

Jiang,

Coenen and

Zito, A survey of frequent subgraph mining algorithms, The Knowledge Engineering Review 28(1) (2013), 75–105. doi:10.1017/S0269888912000331.

45.

Kang,

E.M.

van Mulligen and

J.A.

Kors, Training text chunkers on a silver standard corpus: Can silver replace gold? BMC Bioinformatics 13(1) (2012), 17. doi:10.1186/1471-2105-13-17.

46.

Kim,

E.-K.

Kim,

Won,

Nam and

K.-S.

Choi, The association rule mining system for acquiring knowledge of DBpedia from Wikipedia categories, in: Workshop on NLP and DBpedia, 2015, https://nlpdbpedia2015.files.wordpress.com/2015/08/nlpdbpedia_2015_submission_13.pdf .

47.

Kliegr, Linked hypernyms: Enriching DBpedia with targeted hypernym discovery, Web Semantics: Science, Services and Agents on the World Wide Web 31 (2015), 59–69. doi:10.1016/j.websem.2014.11.001.

48.

Knuth,

Hercher and

Sack, Collaboratively patching Linked Data, in: Workshop on Usage Analysis and the Web of Data (USEWOD), 2012, http://arxiv.org/abs/1204.2715 .

49.

Kolthoff and

Dutta, Semantic relation composition in large scale knowledge bases, in: 3rd Workshop on Linked Data for Information Extraction, CEUR Workshop Proceedings, Vol. 1467, 2015, pp. 34–47, http://ceur-ws.org/Vol-1467/ .

50.

Krompaß,

Baier and

Tresp, Type-constrained representation learning in knowledge graphs, in: International Semantic Web Conference, LNCS, Vol. 9366, Springer, Switzerland, 2015, pp. 640–655. doi:10.1007/978-3-319-25007-6_37.

51.

Lafferty,

McCallum and

F.C.N.

Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, in: 18th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 2001, pp. 282–289.

52.

Lange,

Böhm and

Naumann, Extracting structured information from Wikipedia articles to populate infoboxes, in: Proc. of the 19th ACM Conference on Information and Knowledge Management (CIKM), ACM, New York, 2010, pp. 1661–1664. doi:10.1145/1871437.1871698.

53.

Lehmann and

Bühmann, ORE – a tool for repairing and enriching knowledge bases, in: The Semantic Web-ISWC 2010, LNCS, Vol. 6497, Springer, Berlin, Heidelberg, 2010, pp. 177–193. doi:10.1007/978-3-642-17749-1_12.

54.

Lehmann,

Gerber,

Morsey and

A.-C.N.

Ngomo, DeFacto – deep fact validation, in: The Semantic Web-ISWC 2012, LNCS, Vol. 7649, Springer, Berlin, Heidelberg, 2012, pp. 312–327. doi:10.1007/978-3-642-35176-1_20.

55.

Lehmann and

Hitzler, Concept learning in description logics using refinement operators, Machine Learning 78 (2010), 203–250. doi:10.1007/s10994-009-5146-2.

56.

Lehmann,

Isele,

Jakob,

Jentzsch,

Kontokostas,

P.N.

Mendes,

Hellmann,

Morsey,

van Kleef,

Auer and

Bizer, DBpedia – a large-scale, multilingual knowledge base extracted from Wikipedia, Semantic Web Journal 6(2) (2013). doi:10.3233/SW-140134.

57.

D.B.

Lenat, CYC: A large-scale investment in knowledge infrastructure, Communications of the ACM 38(11) (1995), 33–38. doi:10.1145/219717.219745.

58.

Li,

Xu and

Zhong, Probabilistic error detecting in numerical Linked Data, in: Database and Expert Systems Applications, LNCS, Vol. 9261, Springer International, 2015, pp. 61–75. doi:10.1007/978-3-319-22849-5_5.

59.

Liaw and

Wiener, Classification and regression by randomForest, R News 2(3) (2002), 18–22, http://www.bios.unc.edu/~dzeng/BIOS740/randomforest.pdf .

60.

Liu,

d’Aquin and

Motta, Towards Linked Data fact validation through measuring consensus, in: Workshop on Linked Data Quality, CEUR Workshop Proceedings, Vol. 1376, 2015, http://ceur-ws.org/Vol-1376/ .

61.

Lösch,

Bloehdorn and

Rettinger, Graph kernels for RDF data, in: The Semantic Web: Research and Applications, LNCS, Vol. 7295, Springer, Berlin, Heidelberg, 2012, pp. 134–148. doi:10.1007/978-3-642-30284-8_16.

62.

Luther,

Liebig,

Böhm and

Noppens, Who the heck is the father of Bob? in: The Semantic Web: Research and Applications, LNCS, Vol. 5554, Springer, Berlin, Heidelberg, 2009, pp. 66–80. doi:10.1007/978-3-642-02121-3_9.

63.

Ma,

Gao,

Wu and

Qi, Learning disjointness axioms with association rule mining and its application to inconsistency detection of Linked Data, in: The Semantic Web and Web Science,

Zhao,

Du,

Wang,

Ji and

J.Z.

Pan, eds, Communications in Computer and Information Science, Vol. 480, Springer, Berlin, Heidelberg, 2014, pp. 29–41. doi:10.1007/978-3-662-45495-4_3.

64.

Maedche, Ontology Learning for the Semantic Web, Springer Science & Business Media, Luxembourg, 2002.

65.

Mahdisoltani,

Biega and

F.M.

Suchanek, YAGO3: A knowledge base from multilingual Wikipedias, in: Conference on Innovative Data Systems Research, 2015, http://www.cidrdb.org/cidr2015/Papers/CIDR15_Paper1.pdf .

66.

Meusel,

Petrovski and

Bizer, The WebDataCommons microdata, RDFa and microformat dataset series, in: The Semantic Web-ISWC 2014, LNCS, Vol. 8796, Springer International, 2014, pp. 277–292. doi:10.1007/978-3-319-11964-9_18.

67.

G.A.

Miller, WordNet: A lexical database for English, Communications of the ACM 38(11) (1995), 39–41. doi:10.1145/219717.219748.

68.

Mintz,

Bills,

Snow and

Jurafsky, Distant supervision for relation extraction without labeled data, in: Proc. of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Association for Computational Linguistics, Stroudsburg, PA, 2009, pp. 1003–1011.

69.

Muñoz,

Hogan and

Mileo, Triplifying Wikipedia’s tables, in: Linked Data for Information Extraction, CEUR Workshop Proceedings, Vol. 1057, 2013, http://ceur-ws.org/Vol-1057/ .

70.

Nakashole,

Theobald and

Weikum, Scalable knowledge harvesting with high precision and high recall, in: Proc. of the Fourth ACM International Conference on Web Search and Data Mining, ACM, New York, 2011, pp. 227–236. doi:10.1145/1935826.1935869.

71.

Nentwig,

Hartung,

A.-C.N.

Ngomo and

Rahm, A survey of current link discovery frameworks. Semantic Web (2015) (to appear). doi:10.3233/SW-150210.

72.

Neville and

Jensen, Iterative classification in relational data, in: Proc. AAAI-2000 Workshop on Learning Statistical Models from Relational Data, AAAI, Palo Alto, CA, 2000, pp. 13–20. http://www.aaai.org/Library/Workshops/2000/ws00-06-007.php .

73.

Nickel,

Tresp and

H.-P.

Kriegel, Factorizing YAGO: Scalable machine learning for Linked Data, in: Proc. of the 21st International Conference on World Wide Web, ACM, New York, 2012, pp. 271–280. doi:10.1145/2187836.2187874.

74.

Nothman,

Ringland,

Radford,

Murphy and

J.R.

Curran, Learning multilingual named entity recognition from Wikipedia, Artificial Intelligence 194 (2013), 151–175. doi:10.1016/j.artint.2012.03.006.

75.

A.G.

Nuzzolese,

Gangemi,

Presutti and

Ciancarini, Type inference through the analysis of Wikipedia links, in: Linked Data on the Web, CEUR Workshop Proceedings, Vol. 937, 2012, http://ceur-ws.org/Vol-937/ .

76.

Pantel,

Crestan,

Borkovsky,

A.-M.

Popescu and

Vyas, Web-scale distributional similarity and entity set expansion, in: Proc. of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2, Association for Computational Linguistics, Stroudsburg, PA, 2009, pp. 938–947, http://www.aclweb.org/anthology/D09-1098 .

77.

Paulheim, Browsing Linked Open Data with auto complete, Semantic Web Challenge, 2012, https://km.aifb.kit.edu/sites/swc/2012/submissions/swc2012_submission_15.pdf.

78.

Paulheim, Identifying wrong links between datasets by multi-dimensional outlier detection, in: International Workshop on Debugging Ontologies and Ontology Mappings, CEUR Workshop Proceedings, Vol. 1162, 2014, pp. 27–38, http://ceur-ws.org/Vol-1162/ .

79.

Paulheim and

Bizer, Type inference on noisy RDF data, in: The Semantic Web-ISWC 2013, LNCS, Vol. 8218, Springer, Berlin, Heidelberg, 2013, pp. 510–525. doi:10.1007/978-3-642-41335-3_32.

80.

Paulheim and

Bizer, Improving the quality of Linked Data using statistical distributions, International Journal on Semantic Web and Information Systems (IJSWIS) 10(2) (2014), 63–86. doi:10.4018/ijswis.2014040104.

81.

Paulheim and

Fürnkranz, Unsupervised generation of data mining features from Linked Open Data, in: 2nd International Conference on Web Intelligence, Mining and Semantics, ACM, New York, 2012, p. 31. doi:10.1145/2254129.2254168.

82.

Paulheim and

Gangemi, Serving DBpedia with DOLCE – more than just adding a cherry on top, in: International Semantic Web Conference, LNCS, Vol. 9366, Springer International, 2015. doi:10.1007/978-3-319-25007-6_11.

83.

Paulheim and

Meusel, A decomposition of the outlier detection problem into a set of supervised learning problems, Machine Learning 100(2–3) (2015), 509–531. doi:10.1007/s10994-015-5507-y.

84.

Paulheim and

S.P.

Ponzetto, Extending DBpedia with Wikipedia list pages, in: 1st International Workshop on NLP and DBpedia, CEUR Workshop Proceedings, Vol. 1064, 2013, http://ceur-ws.org/Vol-1064/ .

85.

Péron,

Raimbault,

Ménier and

P.-F.

Marteau, On the detection of inconsistencies in RDF data sets and their correction at ontological level, Technical report 00635854, HAL, 2011, https://hal.archives-ouvertes.fr/hal-00635854/en/.

86.

L.L.

Pipino,

Y.W.

Lee and

R.Y.

Wang, Data quality assessment, Communications of the ACM 45(4) (2002), 211–218. doi:10.1145/505248.506010.

87.

Polleres,

Hogan,

Harth and

Decker, Can we ever catch up with the Web? Semantic Web 1(1) (2010), 45–52. doi:10.3233/SW-2010-0016.

88.

Ristoski and

Paulheim, A comparison of propositionalization strategies for creating features from Linked Open Data, in: Linked Data for Knowledge Discovery, CEUR Workshop Proceedings, Vol. 1232, 2014, http://ceur-ws.org/Vol-1232/ .

89.

Ritze,

Lehmberg and

Bizer, Matching HTML tables to DBpedia, in: Proc. of the 5th International Conference on Web Intelligence, Mining and Semantics, ACM, New York, 2015, p. 10. doi:10.1145/2797115.2797118.

90.

Rizzo and

Troncy, NERD: Evaluating named entity recognition tools in the Web of Data, in: Workshop on Web Scale Knowledge Extraction (WEKEX’11), 2011, http://nerd.eurecom.fr/ui/paper/Rizzo_Troncy-wekex2011.pdf .

91.

Russell and

Norvig, Artificial Intelligence: A Modern Approach, Pearson, London, 1995.

92.

Sarjant,

Legg,

Robinson and

Medelyan, “All you can eat” ontology-building: Feeding Wikipedia to Cyc, in: Proc. of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, IEEE Computer Society, Piscataway, NJ, 2009, pp. 341–348. doi:10.1109/WI-IAT.2009.60.

93.

Sarmento,

Jijkuon,

de Rijke and

Oliveira, “More Like These”: Growing entity classes from seeds, in: Proc. of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, ACM, New York, 2007, pp. 959–962. doi:10.1145/1321440.1321585.

94.

Schäfer,

Ristoski and

Paulheim, What is special about Bethlehem, Pennsylvania? – Identifying unexpected facts about DBpedia entities, in: ISWC 2015 – Posters and Demonstrations, CEUR Workshop Proceedings, Vol. 1486, 2015, http://ceur-ws.org/Vol-1486/ .

95.

Schmachtenberg,

Bizer and

Paulheim, Adoption of the Linked Data best practices in different topical domains, in: International Semantic Web Conference, LNCS, Vol. 8796, Springer International, 2014. doi:10.1007/978-3-319-11964-9_16.

96.

C.N.

SillaJr. and

A.A.

Freitas, A survey of hierarchical classification across different application domains, Data Mining and Knowledge Discovery 22(1–2) (2011), 31–72. doi:10.1007/s10618-010-0175-9.

97.

Siorpaes and

Hepp, Games with a purpose for the Semantic Web, IEEE Intelligent Systems 3 (2008), 50–60. doi:10.1109/MIS.2008.45.

98.

Sleeman and

Finin, Type prediction for efficient coreference resolution in heterogeneous semantic graphs, in: 2013 IEEE Seventh International Conference on Semantic Computing (ICSC), IEEE, Piscataway, NJ, 2013, pp. 78–85. doi:10.1109/ICSC.2013.22.

99.

Sleeman,

Finin and

Joshi, Topic modeling for RDF graphs, in: 3rd Workshop on Linked Data for Information Extraction, CEUR Workshop Proceedings, Vol. 1467, 2015, http://ceur-ws.org/Vol-1467/ .

100.

Socher,

Chen,

C.D.

Manning and

Ng, Reasoning with neural tensor networks for knowledge base completion, in: Advances in Neural Information Processing Systems 26 (NIPS 2013), Curran Associates, Inc., Newry, 2013, pp. 926–934, http://papers.nips.cc/paper/5028-reasonin .

101.

F.M.

Suchanek,

Kasneci and

Weikum, YAGO: A core of semantic knowledge unifying WordNet and Wikipedia, in: 16th International Conference on World Wide Web, ACM, New York, 2007, pp. 697–706. doi:10.1145/1242572.1242667.

102.

Töpper,

Knuth and

Sack, DBpedia ontology enrichment for inconsistency detection, in: Proc. of the 8th International Conference on Semantic Systems, ACM, New York, 2012, pp. 33–40. doi:10.1145/2362499.2362505.

103.

Tsoumakas

et al., Multi label classification: An overview, International Journal of Data Warehousing and Mining 3(3) (2007), 1–13. doi:10.4018/jdwm.2007070101.

104.

Vrandečić and

Krötzsch, Wikidata: A free collaborative knowledge base, Communications of the ACM 57(10) (2014), 78–85. doi:10.1145/2629489.

105.

Waitelonis,

Ludwig,

Knuth and

Sack, WhoKnows? – Evaluating linked data heuristics with a quiz that cleans up DBpedia, International Journal of Interactive Technology and Smart Education 8(4) (2011), 236–248. doi:10.1108/17415651111189478.

106.

R.C.

Wang and

W.W.

Cohen, Iterative set expansion of named entities using the Web, in: Eighth IEEE International Conference on Data Mining, IEEE, Piscataway, NJ, 2008, pp. 1091–1096. doi:10.1109/ICDM.2008.145.

107.

West,

Gabrilovich,

Murphy,

Sun,

Gupta and

Lin, Knowledge base completion via search-based question answering, in: Proc. of the 23rd International Conference on World Wide Web, ACM, New York, 2014, pp. 515–526. doi:10.1145/2566486.2568032.

108.

Wienand and

Paulheim, Detecting incorrect numerical data in DBpedia, in: The Semantic Web: Trends and Challenges, LNCS, Vol. 8465, Springer International, 2014, pp. 504–518. doi:10.1007/978-3-319-07443-6_34.

109.

Wu,

Hoffmann and

D.S.

Weld, Information extraction from Wikipedia: Moving down the long tail, in: Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, 2008, pp. 731–739. doi:10.1145/1401890.1401978.

110.

Zaveri,

Kontokostas,

M.A.

Sherif,

Bühmann,

Morsey,

Auer and

Lehmann, User-driven quality evaluation of DBpedia, in: 9th International Conference on Semantic Systems (I-SEMANTICS ’13), ACM, New York, 2013, pp. 97–104. doi:10.1145/2506182.2506195.

111.

Zaveri,

Rula,

Maurino,

Pietrobon,

Lehmann,

Auer and

Hitzler, Quality assessment methodologies for Linked Open Data, Semantic Web Journal 7(1) (2015), 63–93. doi:10.3233/SW-150175.

112.

Zhao,

Gao,

Gallinari and

Guo, Knowledge base completion by learning pairwise-interaction differentiated embeddings, Data Mining and Knowledge Discovery 29(5) (2015), 1486–1504. doi:10.1007/s10618-015-0430-1.

113.

Zimmermann,

Gravier,

Subercaze and

Cruzille, Nell2RDF: Read the Web, and turn it into RDF, in: Knowledge Discovery and Data Mining Meets Linked Open Data, CEUR Workshop Proceedings, Vol. 992, 2013, pp. 2–8, http://ceur-ws.org/Vol-992/ .