Advertisement

Data Mining and Knowledge Discovery

, Volume 32, Issue 4, pp 849–884 | Cite as

Interpretation of text patterns

Article

Abstract

Patterns are used as a fundamental means to analyse data in many text mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and lack of grounded (e.g. a priori defined) semantics have made it difficult for a user to interpret and explore the patterns. An insight into the meanings of the patterns can benefit users in the process of exploring them. In this regard, this paper presents a model to automatically interpret patterns by achieving two goals: (1) providing the meanings of patterns in terms of ontology concepts and (2) providing a new method for generating and extracting features from an ontology to describe the relevant information more effectively. Taking advantage of a domain ontology and a set of relevant statistics (e.g. term frequency in a document, inverse term frequency in a domain ontology, etc.), our proposed model can give an insight into the hidden meanings of the patterns. The model is evaluated by comparing it with different baseline models on three standard datasets. The results show that the performance of the proposed model is significantly better than baseline models.

Keywords

Text mining Frequent pattern Pattern interpretation Conceptual annotation Contextual weighting Information filtering 

Notes

Acknowledgements

This paper was partially supported by Grant DP140103157 from the Australian Research Council (ARC Discovery Project). Besides, we thank Dr Yan Shen and Dr Yang Gao for their constructive comments and support on the experiments. We also thank the anonymous reviewers for their valuable comments.

References

  1. Afrati F, Gionis A, Mannila H (2004) Approximating a collection of frequent sets. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Seattle, WA, USA, pp 12–19Google Scholar
  2. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, vol 22. ACM, Washington, DC, USA, pp 207–216Google Scholar
  3. Anderson JR (1983) A spreading activation theory of memory. J Verbal Learn Verbal Behav 22(3):261–295CrossRefGoogle Scholar
  4. Banko M, Cafarella MJ, Soderland S, Broadhead M, Etzioni O (2007) Open information extraction from the web. In: Proceedings of the 20th international joint conference on artifical intelligence, vol 7. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 2670–2676Google Scholar
  5. Bayardo Jr RJ (1998) Efficiently mining long patterns from databases. In: ACM Sigmod record, vol 27. ACM, Seattle, Washington, USA, pp 85–93Google Scholar
  6. Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(Feb):1137–1155MATHGoogle Scholar
  7. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATHGoogle Scholar
  8. Bloehdorn S, Cimiano P, Hotho A (2006) Learning ontologies to improve text clustering and classification. From data and information analysis to knowledge engineering. Springer, Magdeburg, pp 334–341CrossRefGoogle Scholar
  9. Brewster C, Alani H, Dasmahapatra S, Wilks Y (2004) Data driven ontology evaluation. In: International conference on language resources and evaluation (LREC 2004). Lisbon, PortugalGoogle Scholar
  10. Buckley C, Voorhees EM (2000) Evaluating evaluation measure stability. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval. ACM, Athens, Greece, pp 33–40Google Scholar
  11. Bunescu R, Mooney RJ (2006) Subsequence kernels for relation extraction. Advances in neural information processing systems. MIT Press, Cambridge, pp 171–178Google Scholar
  12. Calders T, Goethals B (2007) Non-derivable itemset mining. Data Min Knowl Disc 14(1):171–206MathSciNetCrossRefGoogle Scholar
  13. Calegari S, Pasi G (2013) Personal ontologies: generation of user profiles based on the yago ontology. Inf Process Manag 49(3):640–658CrossRefGoogle Scholar
  14. Caropreso MF, Matwin S, Sebastiani F (2001) A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases Doc Manag Theory Pract 5478:78–102Google Scholar
  15. Chemudugunta C, Holloway A, Smyth P, Steyvers M (2008a) Modeling documents by combining semantic concepts with unsupervised statistical learning. In: International semantic web conference. Springer, Karlsruhe, pp 229–244Google Scholar
  16. Chemudugunta C, Smyth P, Steyvers M (2008b) Combining concept hierarchies and statistical topic models. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, Napa Valley, California, USA, pp 1469–1470Google Scholar
  17. Collins AM, Loftus EF (1975) A spreading-activation theory of semantic processing. Psychol Rev 82(6):407CrossRefGoogle Scholar
  18. Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, ACM, pp 160–167Google Scholar
  19. Crestani F (1997) Application of spreading activation techniques in information retrieval. Artif Intell Rev 11(6):453–482CrossRefGoogle Scholar
  20. Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407CrossRefGoogle Scholar
  21. Del Corro L, Gemulla R (2013) Clausie: clause-based open information extraction. In: Proceedings of the 22nd international conference on world wide web. ACM, pp 355–366Google Scholar
  22. Egozi O, Gabrilovich E, Markovitch S (2008) Concept-based feature generation and selection for information retrieval. In: AAAI conference on artificial intelligence, vol 8. Chicago, Illinois, pp 1132–1137Google Scholar
  23. Egozi O, Markovitch S, Gabrilovich E (2011) Concept-based information retrieval using explicit semantic analysis. ACM Trans Inf Syst (TOIS) 29(2):1–38CrossRefGoogle Scholar
  24. Fader A, Soderland S, Etzioni O (2011) Identifying relations for open information extraction. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 1535–1545Google Scholar
  25. Gabrilovich E, Markovitch S (2005) Feature generation for text categorization using world knowledge. In: Proceedings of the 19th international joint conference on artificial intelligence, vol 5. Edinburgh, Scotland, pp 1048–1053Google Scholar
  26. Gabrilovich E, Markovitch S (2007a) Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th international joint conference on artificial intelligence, vol 6. Hyderabad, India, pp 1606–1611Google Scholar
  27. Gabrilovich E, Markovitch S (2007b) Harnessing the expertise of 70, 000 human editors: knowledge-based feature generation for text categorization. J Mach Learn Res 8(10):2297–2345Google Scholar
  28. Gabrilovich E, Markovitch S (2009) Wikipedia-based semantic interpretation for natural language processing. J Artif Intell Res 34(2):443–498MATHGoogle Scholar
  29. Gallo A, De Bie T, Cristianini N (2007) Mini: mining informative non-redundant itemsets. In: European conference on principles of data mining and knowledge discovery. Springer, pp 438–445Google Scholar
  30. Gauch S, Chaffee J, Pretschner A (2003) Ontology-based personalized search and browsing. Web Intell Agent Syst 1(3):219–234Google Scholar
  31. Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 513–520Google Scholar
  32. Goutsias J, Mahler RP, Nguyen HT (2012) Random sets: theory and applications, vol 97. Springer, BerlinGoogle Scholar
  33. Grossman DA (2004) Information retrieval: algorithms and heuristics, vol 15. Springer, BerlinCrossRefMATHGoogle Scholar
  34. Guns T, Nijssen S, De Raedt L (2013) k-pattern set mining under constraints. IEEE Trans Knowl Data Eng 25(2):402–418CrossRefGoogle Scholar
  35. Han J, Wang J, Lu Y, Tzvetkov P (2002) Mining top-k frequent closed patterns without minimum support. In: IEEE international conference on data mining (ICDM), IEEE, Maebashi City, Japan, pp 211–218Google Scholar
  36. Hennig L, Umbrath W, Wetzker R (2008) An ontology-based approach to text summarization. In: IEEE/WIC/ACM international joint conference on web intelligence (WI) and intelligent agent technology (IAT), vol 3. IEEE. Sydney, NSW, Australia, pp 291–294Google Scholar
  37. Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 50–57Google Scholar
  38. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Nat Acad Sci 81(10):3088–3092CrossRefMATHGoogle Scholar
  39. Hotho A, Nürnberger A, Paaß G (2005) A brief survey of text mining. Ldv Forum 20:19–62Google Scholar
  40. Hulpus I, Hayes C, Karnstedt M, Greene D (2013) Unsupervised graph-based topic labelling using dbpedia. In: Proceedings of the sixth ACM international conference on Web search and data mining, ACM, Rome, Italy, pp 465–474Google Scholar
  41. Ingaramo D, Pinto D, Rosso P, Errecalde M (2008) Evaluation of internal validity measures in short-text corpora. In: Computational linguistics and intelligent text processing, Springer, Haifa, Israel, pp 555–567Google Scholar
  42. Karp RM (1972) Reducibility among combinatorial problems. Complexity of computer computations. Springer, Berlin, pp 85–103CrossRefGoogle Scholar
  43. Knobbe AJ, Ho EK (2006) Pattern teams. In: European conference on principles of data mining and knowledge discovery, Springer, pp 577–584Google Scholar
  44. Kriegel HP, Borgwardt KM, Kröger P, Pryakhin A, Schubert M, Zimek A (2007) Future trends in data mining. Data Min Knowl Disc 15(1):87–97MathSciNetCrossRefGoogle Scholar
  45. Kruse R, Schwecke E, Heinsohn J (1991) Uncertainty and vagueness in knowledge based systems. Springer, New York Inc., New YorkCrossRefMATHGoogle Scholar
  46. Kruse R, Schwecke E, Heinsohn J (2012) Uncertainty and vagueness in knowledge based systems: numerical methods. Springer, BerlinMATHGoogle Scholar
  47. Lau JH, Newman D, Karimi S, Baldwin T (2010) Best topic word selection for topic labelling. In: Proceedings of the 23rd international conference on computational linguistics: Posters, Association for Computational Linguistics, Beijing, China, pp 605–613Google Scholar
  48. Lau JH, Grieser K, Newman D, Baldwin T (2011) Automatic labelling of topic models. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, Portland, Oregon, USA, pp 1536–1545Google Scholar
  49. Lewis DD, Yang Y, Rose TG, Li F (2004) Rcv1: a new benchmark collection for text categorization research. J Mach Learn Res 5:361–397Google Scholar
  50. Li G, Zaki MJ (2016) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Disc 30(1):181–225MathSciNetCrossRefGoogle Scholar
  51. Li Y, Zhong N (2006) Mining ontology for automatically acquiring web user information needs. IEEE Trans Knowl Data Eng 18(4):554–568CrossRefGoogle Scholar
  52. Li Y, Algarni A, Zhong N (2010) Mining positive and negative patterns for relevance feature discovery. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Washington, DC, USA, pp 753–762Google Scholar
  53. Li Y, Algarni A, Albathan M, Shen Y, Bijaksana MA (2015) Relevance feature discovery for text mining. IEEE Trans Knowl Data Eng 27(6):1656–1669.  https://doi.org/10.1109/TKDE.2014.2373357 CrossRefGoogle Scholar
  54. Liu B, Zhao K, Benkler J, Xiao W (2006) Rule interestingness analysis using olap operations. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 297–306Google Scholar
  55. Liu J, Shang J, Wang C, Ren X, Han J (2015) Mining quality phrases from massive text corpora. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, ACM, pp 1729–1744Google Scholar
  56. Liu J, Ren X, Shang J, Cassidy T, Voss CR, Han J (2016) Representing documents via latent keyphrase inference. In: Proceedings of the 25th international conference on world wide web, International World Wide Web Conferences Steering Committee, pp 1057–1067Google Scholar
  57. Mao XL, Ming ZY, Zha ZJ, Chua TS, Yan H, Li X (2012) Automatic labeling hierarchical topics. In: Proceedings of the 21st ACM international conference on Information and knowledge management, ACM, pp 2383–2386Google Scholar
  58. Mei Q, Liu C, Su H, Zhai C (2006a) A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th international conference on world wide web, ACM, Edinburgh, Scotland, pp 533–542Google Scholar
  59. Mei Q, Xin D, Cheng H, Han J, Zhai C (2006b) Generating semantic annotations for frequent patterns with context analysis. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 337–346Google Scholar
  60. Mei Q, Shen X, Zhai C (2007a) Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, San Jose, California, USA, pp 490–499Google Scholar
  61. Mei Q, Xin D, Cheng H, Han J, Zhai C (2007b) Semantic annotation of frequent patterns. ACM Trans Knowl Discov Data (TKDD) 1(3):11:1–11:30Google Scholar
  62. Michelson M, Macskassy SA (2010) Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on analytics for noisy unstructured text data, ACM, pp 73–80Google Scholar
  63. Mielikäinen T, Mannila H (2003) The pattern ordering problem. In: European conference on principles of data mining and knowledge discovery, Springer, pp 327–338Google Scholar
  64. Mihelčić M, Šimić G, Leko MB, Lavrač N, Džeroski S, Šmuc T (2017) Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients. PLoS ONE 12:1–35.  https://doi.org/10.1371/journal.pone.0187364 Google Scholar
  65. Mikolov T (2012) Statistical language models based on neural networks. Presentation at google, mountain view, 2nd AprilGoogle Scholar
  66. Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. In: International conference on learning representations (ICLR) workshopGoogle Scholar
  67. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119Google Scholar
  68. Mikolov T, Yih Wt, Zweig G (2013c) Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 conference of the North American chapter of the Association for Computational Linguistics: human language technologies (NAACL-HLT), vol 13, pp 746–751Google Scholar
  69. Molchanov I (2006) Theory of random sets. Springer, BerlinGoogle Scholar
  70. Navigli R, Velardi P, Gangemi A (2003) Ontology learning and its application to automated terminology translation. IEEE Intell Syst 18(1):22–31CrossRefGoogle Scholar
  71. Parida L, Ramakrishnan N (2005) Redescription mining: structure theory and algorithms. In: AAAI, vol 5, pp 837–844Google Scholar
  72. Parthasarathy S, Zaki MJ, Ogihara M, Dwarkadas S (1999) Incremental and interactive sequence mining. In: Proceedings of the eighth international conference on information and knowledge management, ACM, Kansas City, Missouri, USA, pp 251–258Google Scholar
  73. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th international conference on database theory. Springer, London, UK, pp 398–416Google Scholar
  74. Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), vol 14, pp 1532–1543Google Scholar
  75. Porter MF (1980) An algorithm for suffix stripping. Program Electron Libr Inf Syst 14(3):130–137CrossRefGoogle Scholar
  76. Quillan MR (1966) Semantic memory. Technical report, DTIC DocumentGoogle Scholar
  77. Raedt LD, Zimmermann A (2007) Constraint-based pattern set mining. In: Proceedings of the 2007 SIAM international conference on data mining, SIAM, pp 237–248Google Scholar
  78. Ramakrishnan N, Kumar D, Mishra B, Potts M, Helm RF (2004) Turning cartwheels: an alternating algorithm for mining redescriptions. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 266–275Google Scholar
  79. Robertson SE, Soboroff I (2002) The trec 2002 filtering track report. In: TREC, vol 2002, Gaithersburg, Maryland, USA, pp 27–39Google Scholar
  80. Rocchio JJ (1971) Relevance feedback in information retrieval. The smart retrieval system-experiments in automatic document processing, pp 313–323Google Scholar
  81. Rose T, Stevenson M, Whitehead M (2002) The reuters corpus volume 1—from yesterday’s news to tomorrow’s language resources. In: Proceedings of the third international conference on language resources and evaluation (LREC), vol 2, Canary Islands, Spain, pp 827–832Google Scholar
  82. Ruggieri S (2010) Frequent regular itemset mining. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 263–272Google Scholar
  83. Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1MATHGoogle Scholar
  84. Salton G (1968) Automatic information organization and retrieval. McGraw-Hill, New YorkGoogle Scholar
  85. Schmitz M, Bart R, Soderland S, Etzioni O, et al (2012) Open language learning for information extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Association for Computational Linguistics, pp 523–534Google Scholar
  86. Schwenk H (2007) Continuous space language models. Comput Speech Lang 21(3):492–518CrossRefGoogle Scholar
  87. Shen Y, Li Y, Xu Y (2012) Adopting relevance feature to learn personalized ontologies. In: Australasian joint conference on artificial intelligence, Springer, Sydney, Australia, pp 457–468Google Scholar
  88. Siebes A, Vreeken J, Leeuwen Mv (2006) Item sets that compress. In: Proceedings of the 2006 SIAM international conference on data mining, SIAM, pp 395–406Google Scholar
  89. Sieg A, Mobasher B, Burke R (2007) Web search personalization with ontological user profiles. In: Proceedings of the sixteenth ACM conference on information and knowledge management, ACM, Lisbon, Portugal, pp 525–534Google Scholar
  90. Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136Google Scholar
  91. Song Y, Wang H, Wang Z, Li H, Chen W (2011) Short text conceptualization using a probabilistic knowledgebase. In: Proceedings of the twenty-second international joint conference on artificial intelligence—vol 3. AAAI Press, Barcelona, pp 2330–2336Google Scholar
  92. Spasic I, Ananiadou S, McNaught J, Kumar A (2005) Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 6(3):239–251CrossRefGoogle Scholar
  93. Sun X, Xiao Y, Wang H, Wang W (2015) On conceptual labeling of a bag of words. In: Proceedings of the 24th international conference on artificial intelligence. AAAI Press, Buenos Aires, pp 1326–1332Google Scholar
  94. Tan AH, et al (1999) Text mining: the state of the art and the challenges. In: Proceedings of the PAKDD 1999 workshop on knowledge discovery from advanced databases, vol 8, pp 65–70Google Scholar
  95. Tao X, Li Y, Zhong N (2011) A personalized ontology model for web information gathering. IEEE Trans Knowl Data Eng 23(4):496–511CrossRefGoogle Scholar
  96. Thiel K, Berthold MR (2010) Node similarities from spreading activation. In: 10th international conference on data mining (ICDM). IEEE, pp 1085–1090Google Scholar
  97. Tran T, Cimiano P, Rudolph S, Studer R (2007) Ontology-based interpretation of keywords for semantic search. The semantic web, pp 523–536Google Scholar
  98. Turney PD (2013) Distributional semantics beyond words: supervised learning of analogy and paraphrase. Trans Assoc Comput Linguist 1:353–366Google Scholar
  99. Turney PD, Pantel P (2010) From frequency to meaning: vector space models of semantics. J Artif Intell Res 37:141–188MathSciNetMATHGoogle Scholar
  100. Verma R, Chen P, Lu W (2007) A semantic free-text summarization system using ontology knowledge. In: Proceedings of document understanding conference, Citeseer, Rochester, New York, USA, p 5Google Scholar
  101. Vo DT, Bagheri E (2016) Open information extraction. Encycl Semant Comput Robot Intell.  https://doi.org/10.1142/S2425038416300032 Google Scholar
  102. Wang P, Domeniconi C (2008) Building semantic kernels for text classification using wikipedia. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Las Vegas, Nevada, USA, pp 713–721Google Scholar
  103. Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 424–433Google Scholar
  104. Weston J, Bengio S, Usunier N (2011) Wsabie: Scaling up to large vocabulary image annotation. In: Proceedings of the twenty-second international joint conference on artificial intelligence, vol 11, pp 2764–2770Google Scholar
  105. Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF (2000) Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr 72(3):690–693CrossRefGoogle Scholar
  106. Wu F, Weld DS (2010) Open information extraction using wikipedia. In: Proceedings of the 48th annual meeting of the association for computational linguistics, Association for Computational Linguistics, pp 118–127Google Scholar
  107. Wu ST (2007) Knowledge discovery using pattern taxonomy model in text mining. PhD thesis, Electrical Engineering and Computer Science, Queensland University of TechnologyGoogle Scholar
  108. Wu ST, Li Y, Xu Y (2006) Deploying approaches for pattern refinement in text mining. In: Sixth international conference on data mining, ICDM’06, IEEE, pp 1157–1161Google Scholar
  109. Xin D, Han J, Yan X, Cheng H (2005) Mining compressed frequent-pattern sets. In: Proceedings of the 31st international conference on very large data bases, VLDB Endowment, Trondheim, Norway, pp 709–720Google Scholar
  110. Xue GR, Zeng HJ, Chen Z, Yu Y, Ma WY, Xi W, Fan W (2004) Optimizing web search using web click-through data. In: Proceedings of the thirteenth ACM international conference on Information and knowledge management, ACM, pp 118–126Google Scholar
  111. Yan X, Cheng H, Han J, Xin D (2005) Summarizing itemset patterns: a profile-based approach. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, Chicago, Illinois, USA, pp 314–323Google Scholar
  112. Yi K, Chan LM (2009) Linking folksonomy to library of congress subject headings: an exploratory study. J Doc 65(6):872–900CrossRefGoogle Scholar
  113. Zaki MJ, Ramakrishnan N (2005) Reasoning about sets using redescription mining. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, pp 364–373Google Scholar
  114. Zhong N, Li Y, Wu ST (2012) Effective pattern discovery for text mining. IEEE Trans Knowl Data Eng 24(1):30–44CrossRefGoogle Scholar
  115. Zhou G, Qian L, Fan J (2010) Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Inf Sci 180(8):1313–1325MathSciNetCrossRefGoogle Scholar
  116. Zhu J, Nie Z, Liu X, Zhang B, Wen JR (2009) Statsnowball: a statistical approach to extracting entity relationships. In: Proceedings of the 18th international conference on World wide web, ACM, pp 101–110Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceQueensland University of Technology (QUT)BrisbaneAustralia

Personalised recommendations