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Knowledge graph refinement: A survey of approaches and evaluation methods

Paulheim, Heiko·2016·Semantic Web

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  1. 10.1007/978-3-642-41338-4_17 · DOI
  2. Akoglu, 2014, Graph based anomaly detection and description: A survey, Data Mining and Knowledge Discovery, 29, 1, 10.1007/s10618-014-0365-y · DOI · Paper page
  3. A.P. Aprosio, C. Giuliano and A. 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/.
  4. 10.1007/978-3-642-38288-8_27 · DOI
  5. Bizer, 2009, Linked Data – the story so far, International Journal on Semantic Web and Information Systems, 5, 1, 10.4018/jswis.2009081901 · DOI
  6. 10.1007/978-3-642-41338-4_3 · DOI
  7. Blei, 2003, Latent Dirichlet allocation, Journal of Machine Learning Research, 3, 993
  8. J. Bock, P. Haase, Q. Ji and R. 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/.
  9. 10.1145/1376616.1376746 · DOI · Paper page
  10. 10.1145/2623330.2630803 · DOI
  11. 10.1145/2567948.2578999 · DOI
  12. P. Buitelaar, P. Cimiano and B. Magnini, Ontology Learning from Text: Methods, Evaluation and Applications, Frontiers in Artificial Intelligence and Applications, Vol. 123, IOS Press, Clifton, VA, 2005.
  13. 10.1145/1718487.1718501 · DOI
  14. 10.1145/1541880.1541882 · DOI · Paper page
  15. 10.1007/3-540-34416-0_23 · DOI
  16. 10.1007/978-3-642-40802-1_30 · DOI
  17. 10.1007/978-3-642-40988-2_39 · DOI
  18. Dentler, 2011, Comparison of reasoners for large ontologies in the OWL 2 EL profile, Semantic Web, 2, 71, 10.3233/SW-2011-0034 · DOI
  19. 10.1007/978-0-387-37022-4_4 · DOI
  20. 10.1145/2623330.2623623 · DOI · Paper page
  21. Z. Dragisic, K. Eckert, J. Euzenat, D. Faria, A. Ferrara, R. Granada, V. Ivanova, E. Jimenez-Ruiz, A. Kempf, P. 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/.
  22. 10.1007/978-3-319-07443-6_20 · DOI
  23. 10.1007/978-3-319-11964-9_23 · DOI
  24. L.A. Galárraga, C. Teflioudi, K. Hose and F. 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. · DOI
  25. Gangemi, 2003, Sweetening WordNet with DOLCE, AI Magazine, 24, 13, 10.1609/aimag.v24i3.1715 · DOI
  26. 10.1007/978-3-642-35176-1_5 · DOI
  27. 10.1007/978-3-642-41335-3_9 · DOI
  28. D. 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.
  29. Getoor, 2005, Link mining: A survey, ACM SIGKDD Explorations Newsletter, 7, 3, 10.1145/1117454.1117456 · DOI
  30. 10.1109/BIBM.2013.6732541 · DOI
  31. 10.1007/978-3-642-17746-0_20 · DOI
  32. 10.1007/3-540-46145-0_17 · DOI
  33. He, 2003, Discovering cluster-based local outliers, Pattern Recognition Letters, 24, 1641, 10.1016/S0167-8655(03)00003-5 · DOI
  34. 10.3115/992133.992154 · DOI
  35. 10.1007/978-3-642-24455-1_12 · DOI
  36. Hipp, 2000, Algorithms for association rule mining – a general survey and comparison, ACM SIGKDD Explorations Newsletter, 2, 58, 10.1145/360402.360421 · DOI
  37. Hodge, 2004, A survey of outlier detection methodologies, Artificial Intelligence Review, 22, 85, 10.1007/s10462-004-4304-y · DOI · Paper page
  38. J. Hoffart, M.A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater and G. 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.
  39. Huang, 2010, A scalable kernel approach to learning in semantic graphs with applications to Linked Data, Semantic Web, 5, 5, 10.3233/SW-130100 · DOI
  40. S. Jang, Megawati J. 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.
  41. 10.1007/978-3-642-21064-8_42 · DOI
  42. Jiang, 2013, A survey of frequent subgraph mining algorithms, The Knowledge Engineering Review, 28, 75, 10.1017/S0269888912000331 · DOI
  43. Kang, 2012, Training text chunkers on a silver standard corpus: Can silver replace gold?, BMC Bioinformatics, 13, 17, 10.1186/1471-2105-13-17 · DOI
  44. J. Kim, E.-K. Kim, Y. Won, S. 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.
  45. Kliegr, 2015, Linked hypernyms: Enriching DBpedia with targeted hypernym discovery, Web Semantics: Science, Services and Agents on the World Wide Web, 31, 59, 10.1016/j.websem.2014.11.001 · DOI
  46. M. Knuth, J. Hercher and H. Sack, Collaboratively patching Linked Data, in: Workshop on Usage Analysis and the Web of Data (USEWOD), 2012, http://arxiv.org/abs/1204.2715. · arXiv
  47. C. Kolthoff and A. 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/.
  48. 10.1007/978-3-319-25007-6_37 · DOI
  49. J. Lafferty, A. 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.
  50. 10.1145/1871437.1871698 · DOI
  51. 10.1007/978-3-642-17749-1_12 · DOI
  52. 10.1007/978-3-642-35176-1_20 · DOI
  53. Lehmann, 2010, Concept learning in description logics using refinement operators, Machine Learning, 78, 203, 10.1007/s10994-009-5146-2 · DOI
  54. 10.3233/SW-140134 · DOI
  55. Lenat, 1995, CYC: A large-scale investment in knowledge infrastructure, Communications of the ACM, 38, 33, 10.1145/219717.219745 · DOI
  56. 10.1007/978-3-319-22849-5_5 · DOI
  57. Liaw, 2002, Classification and regression by randomForest, R News, 2, 18
  58. S. Liu, M. d’Aquin and E. 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/.
  59. 10.1007/978-3-642-30284-8_16 · DOI
  60. 10.1007/978-3-642-02121-3_9 · DOI
  61. 10.1007/978-3-662-45495-4_3 · DOI
  62. A. Maedche, Ontology Learning for the Semantic Web, Springer Science & Business Media, Luxembourg, 2002. · DOI
  63. F. Mahdisoltani, J. 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.
  64. 10.1007/978-3-319-11964-9_18 · DOI
  65. M. Mintz, S. Bills, R. Snow and D. 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. · DOI
  66. E. Muñoz, A. Hogan and A. Mileo, Triplifying Wikipedia’s tables, in: Linked Data for Information Extraction, CEUR Workshop Proceedings, Vol. 1057, 2013, http://ceur-ws.org/Vol-1057/.
  67. 10.1145/1935826.1935869 · DOI
  68. 10.3233/SW-150210 · DOI
  69. J. Neville and D. 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.
  70. 10.1145/2187836.2187874 · DOI · Paper page
  71. Nothman, 2013, Learning multilingual named entity recognition from Wikipedia, Artificial Intelligence, 194, 151, 10.1016/j.artint.2012.03.006 · DOI
  72. A.G. Nuzzolese, A. Gangemi, V. Presutti and P. 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/.
  73. P. Pantel, E. Crestan, A. Borkovsky, A.-M. Popescu and V. 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. · DOI
  74. H. 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/.
  75. 10.1007/978-3-642-41335-3_32 · DOI
  76. Paulheim, 2014, Improving the quality of Linked Data using statistical distributions, International Journal on Semantic Web and Information Systems (IJSWIS), 10, 63, 10.4018/ijswis.2014040104 · DOI
  77. 10.1145/2254129.2254168 · DOI
  78. 10.1007/978-3-319-25007-6_11 · DOI
  79. Paulheim, 2015, A decomposition of the outlier detection problem into a set of supervised learning problems, Machine Learning, 100, 509, 10.1007/s10994-015-5507-y · DOI · Paper page
  80. H. 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/.
  81. Pipino, 2002, Data quality assessment, Communications of the ACM, 45, 211, 10.1145/505248.506010 · DOI · Paper page
  82. Polleres, 2010, Can we ever catch up with the Web?, Semantic Web, 1, 45, 10.3233/SW-2010-0016 · DOI
  83. P. Ristoski and H. 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/.
  84. 10.1145/2797115.2797118 · DOI
  85. G. Rizzo and R. 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.
  86. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, London, 1995.
  87. 10.1109/WI-IAT.2009.60 · DOI
  88. 10.1145/1321440.1321585 · DOI
  89. B. Schäfer, P. Ristoski and H. 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/.
  90. 10.1007/978-3-319-11964-9_16 · DOI
  91. Silla, 2011, A survey of hierarchical classification across different application domains, Data Mining and Knowledge Discovery, 22, 31, 10.1007/s10618-010-0175-9 · DOI
  92. Siorpaes, 2008, Games with a purpose for the Semantic Web, IEEE Intelligent Systems, 3, 50, 10.1109/MIS.2008.45 · DOI
  93. 10.1109/ICSC.2013.22 · DOI
  94. J. Sleeman, T. Finin and A. 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/.
  95. R. Socher, D. Chen, C.D. Manning and A. 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.
  96. 10.1145/1242572.1242667 · DOI · Paper page
  97. 10.1145/2362499.2362505 · DOI
  98. Tsoumakas, 2007, Multi label classification: An overview, International Journal of Data Warehousing and Mining, 3, 1, 10.4018/jdwm.2007070101 · DOI
  99. Vrandečić, 2014, Wikidata: A free collaborative knowledge base, Communications of the ACM, 57, 78, 10.1145/2629489 · DOI
  100. Waitelonis, 2011, WhoKnows? – Evaluating linked data heuristics with a quiz that cleans up DBpedia, International Journal of Interactive Technology and Smart Education, 8, 236, 10.1108/17415651111189478 · DOI
  101. 10.1109/ICDM.2008.145 · DOI
  102. 10.1145/2566486.2568032 · DOI · Paper page
  103. 10.1007/978-3-319-07443-6_34 · DOI
  104. 10.1145/1401890.1401978 · DOI
  105. 10.1145/2506182.2506195 · DOI
  106. Zaveri, 2015, Quality assessment methodologies for Linked Open Data, Semantic Web Journal, 7, 63, 10.3233/SW-150175 · DOI · Paper page
  107. Zhao, 2015, Knowledge base completion by learning pairwise-interaction differentiated embeddings, Data Mining and Knowledge Discovery, 29, 1486, 10.1007/s10618-015-0430-1 · DOI
  108. A. Zimmermann, C. Gravier, J. Subercaze and Q. 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/.

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Authors
Paulheim, H.
Venue
Semantic Web
Published
2016-12-06
Volume
8
Issue
3
Pages
489-508
ISSN
  • 2210-4968
  • 1570-0844
Cited by
849
References
3

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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