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A hybrid-based method for Chinese domain lightweight ontology construction

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Abstract

This paper proposes a framework to automatically construct lightweight ontology from a corpus of Chinese domain Web documents. A hybrid-based method was used for domain lightweight ontology learning. Rule-based method, statistics-based method and cluster-based method were combined to complete two sub-tasks: concept extraction and taxonomic relationships extraction. Firstly, multiword terms were identified based on a set of rules as well as a Named Entity Module. Three statistic methods were employed jointly to rank the order of domain concepts. Secondly, clustering and subsumption methods were joined to construct taxonomy. Concepts were clustered into several groups through clustering method. Three similarity measures were defined to compute similarities between concepts, which aims at capturing semantic, spatial, and co-occurrence information. Subsumption method was adopted to construct taxonomic structure for each concept group, since taxonomic relations only existed between similar concepts. Thirdly, the definitions of the concepts extracted in the first step are collected from online Chinese Encyclopedia. On this collection of concept definitions, the rule-based method and a set of lexico-syntactic patterns were applied to extract taxonomic relationships and refine the taxonomy. Finally, we evaluate our method using gold-standard evaluation on domain of football games. In our evaluation, we compare our method with several classical algorithms. The experimental results show the effectiveness of our method.

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Notes

  1. LTP is provided by Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology.

  2. https://code.google.com/p/word2vec/.

References

  1. Abney S (2004) Understanding the yarowsky algorithm. Comput Linguist 30(3):365–395

    Article  MathSciNet  MATH  Google Scholar 

  2. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web: a new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Sci Am 285(5):34–43

    Article  Google Scholar 

  3. Bird S, Klein E, Loper E, Baldridge J (2008) Multi-disciplinary instruction with the natural language toolkit. In: Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics (TeachCL’08), 2008, pp 62–70

  4. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on Computational learning theory, 1998, pp 92–100

  5. Bradesko L, Dali L, Fortuna B et al (2010) Contextualized question answering. In: ITI 2010, pp 73–78

  6. Brewster C, Jupp S, Luciano J et al (2009) Issues in learning an ontology from text. BMC Bioinform 10(5):S1

    Article  Google Scholar 

  7. Buitelaar P, Magnini B (2005) Ontology learning from text: an overview. In: Buitelaar P, Cimiano P, Magnini B (eds) Ontology learning from text: methods, applications and evaluation. IOS Press, The Netherlands, pp 3–12.

    Google Scholar 

  8. Bunescu RC, Mooney RJ (2005) A shortest path dependency kernels for relation extraction. In: Proceedings of EMNLP’2005, 2005, pp 724–731

  9. Che W, Li Z, Liu T (2010) LTP: a Chinese language technology platform. In: Coling, pp 13–16

  10. Ciaramita M, Gangemi A, Ratsch E, Saric J, Rojas I (2005) Unsupervised learning of semantic relations between concepts of a molecular biology ontology. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, 2005, pp 659–664

  11. Cimiano P, Hotho A, Staab S (2005) Learning concept hierarchies from text corpora using formal concept analysis. J Artif Intell Res 24:305–339

    Article  MATH  Google Scholar 

  12. Cimiano P, Volker J (2005) Text2Onto: A framework for ontology learning and data-driven change discovery. In: NLDB, pp 227–238

  13. Colace F, Santo MD, Greco L et al (2014) Terminological ontology learning and population using latent Dirichlet allocation. J Visual Lang Comput 25:818–826

    Article  Google Scholar 

  14. Curtis J, Matthews G, Baxter D (2005) On the effective use of Cyc in a question answering system. In: IJCAI Workshop on KRAQ’05, Edinburgh, Scotland, pp 61–71.

  15. Dietz EA, Vandic D, Frasincar F (2012) TaxoLearn: a semantic approach to domain taxonomy learning. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2012, pp 58–65

  16. Doing-Harris K, Livnat Y, Meystre S (2015) Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. J Biomed Semant 6(15):1–15

    Google Scholar 

  17. Fallucchi F, Zanzotto F M (2011) Inductive probabilistic taxonomy learning using singular value decomposition. Nat Lang Eng 17(1):71–94

    Article  Google Scholar 

  18. Faure D, Poibeau T (2000) First experiments of using semantic knowledge learned by ASIUM for information extraction task using INTEX. In ECAI Workshop on Ontology Learning, pp 7–12

  19. Ferreira V H, Lopes l, Vieira R, Finatto M J (2013) Automatic extraction of domain specific non-taxonomic relations from Portuguese Corpora. In: Proceedings of 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, 2013, pp 135–138

  20. Fortuna B, Lavrac N, Velardi P (2008) Advancing Topic Ontology Learning through Term Extraction. In: PRICAI, pp 626–635

  21. Gruber T (1993) A translation approach to portable ontology specifications. Knowl Acquis 5:199–220

    Article  Google Scholar 

  22. Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora. In: COLING, vol 2, pp 539–545

  23. Heflin J, Hendler J (2000) Dynamic ontologies on the Web. In: AAAI, pp 443–449

  24. Hippisley A, Cheng D, Ahmad K (2005) The head-modifier principle and multilingual term extraction. Nat Lang Eng 11(2):129–157

    Article  Google Scholar 

  25. Kang Y, Haghigh PD, Burstein F (2016) TaxoFinder: a graph-based approach for taxonomy learning. IEEE Trans Knowl Data Eng 28(2):524–536

    Article  Google Scholar 

  26. Knijff JD, Frasincar F, Hogenboom F (2013) Domain taxonomy learning from text: the subsumption method versus hierarchical clustering. Data Knowl Eng 83(1):54–69

    Article  Google Scholar 

  27. Kozareva Z, Hovy E (2010) A semi-supervised method to learn and construct taxonomies using the web. In: EMNLP, pp 1110–1118

  28. Kozareva Z, Hovy E, Riloff E (2009) Learning and evaluating the content and structure of a term taxonomy. In: AAAI, pp 50–57

  29. Li D, Kipper-Schuler K, Savova G (2008) Conditional random fields and support vector machines for disorder named entity recognition in clinical texts. In: Proceedings of the workshop on current trends in biomedical natural language processing, 2008, pp 94–95

  30. Li J, Luong T, Jurafsky D, and Hovy E (2015) When are tree structures necessary for deep learning of representations? In: Proceedings of the 2015 EMNLP, 2015, pp 2304–2314

  31. Liu X, Song Y, Liu S, Wang H (2012) Automatic taxonomy construction from keywords. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, pp 1433–1441

  32. Lv X, Guan Y, Deng B (2014) Learning based clinical concept extraction on data from multiple sources. J Biomed Inform 52:55–64

    Article  Google Scholar 

  33. Maedche A, Staab S (2000) The text-to-onto ontology learning environment. In: Proceedings of SoftwareDemonstration at the 8th International Conference on Conceptual Structures, 2000, pp 14–18

  34. Meijer K, Frasincar F, Hogenboom F (2014) A semantic approach for extracting domain taxonomies from text. Decis Support Syst 62:78–93

    Article  Google Scholar 

  35. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: ICLR Workshop, 2013

  36. Milano M, Agopito G, Guzzi PH, Cannataro M (2016) An experimental study of information content measurement of gene ontology terms. Int J Mach Learn Cybern. doi:10.1007/s13042-015-0482-y

    Google Scholar 

  37. Navigli R, Velardi P (2004) Learning domain ontologies from document warehouses and dedicated web sites. Comput Linguist 30(2):151–179

    Article  MATH  Google Scholar 

  38. Nedellec C (2000) Corpus-based learning of semantic relations by the ILP system, Asium. In: Proceeding of Learning Language in Logic, 2000, pp 259–278

  39. Paukkeri MS, Garcia-Plaza AP, Fresno V et al (2012) Learning a taxonomy from a set of text documents. Appl Soft Comput 12:1138–1148

    Article  Google Scholar 

  40. Pennacchiotti M, Pantel P (2006) A bootstrapping algorithm for automatically harvesting semantic relations. In: Proceedings of Inference in Computational Semantics, 2006, pp 87–96

  41. Ponzetto SP, Strube M (2011) Taxonomy induction based on a collaboratively built knowledge repository. Artif Intell 75(9–10):1737–1756

    Article  MathSciNet  Google Scholar 

  42. Rehurek R, Sojka P (2010) Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 2010, pp 45–50

  43. Rios-Alvarado AB, Lopez-Arevalo I, Sosa-Sosa VJ (2013) Learning concept hierarchies from textual resources for ontologies construction. Expert Syst Appl 40(15):5907–5915

    Article  Google Scholar 

  44. Ryu P M, Choi K S (2006) Taxonomy learning using term specificity and similarity. In: Proceedings Workshop on Ontology Learning and Population, 2006, pp 41–48

  45. Salton G, McGill MJ (1986) Introduction to modern information retrieval. In: McGraw-Hill Inc. New York, USA, pp 180–198

  46. Santos CD, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd ACL and the 7th IJCNLP, 2015, pp 626–634

  47. Schutz A, Buitelaar P (2005) RelExt: a tool for relation extraction from text in ontology extension. In: Proceedings of 4th International Semantic Web Conference, 2005, pp 593–606

  48. Sclano F, Velardi P (2007) TermExtractor: a web application to learn the shared terminology of emergent web communities. Enterp Interoper. II. doi:10.1007/978-1-84628-858-6_32

    Google Scholar 

  49. Shamsfard M, Barforoush A (2004) Learning ontologies from natural language texts. Int J Hum Comput Stud 60(1):17–63

    Article  Google Scholar 

  50. Snchez D, Moreno A (2005) Web-scale taxonomy learning. In: Proceedings of workshop on extending and learning lexical ontologies using machine learning, 2005, pp 53–60

  51. Snow R, Jurafsky D, Ng A Y (2006) Semantic taxonomy induction from heterogenous evidence, In: ACL, pp 801–808

  52. Specia L, Motta E (2006) A hybrid approach for extracting semantic relations from texts. In: Proceedings of 2nd Workshop on Ontology Learning and Population, 2006, pp 57–64

  53. Suchanek FM, Ifrim G, Weikum G (2006) Combining linguistic and statistical analysis to extract relations from web documents. In: Proceeding of 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2006, pp 712–717

  54. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd ACL and the 7th IJCNLP, 2015, pp 1556–1566

  55. Thompson CA, Califf ME, Mooney RJ (1999) Active learning for natural language parsing and information extraction. In: Proceedings of the 16th International Conference on Machine Learning, Morgan Kaufmann, 1999, pp 406–414

  56. Velardi P, Cucchiarelli A, Ptit M (2007) A taxonomy learning method and its application to characterize a scientific web community. IEEE Trans Knowl Data Eng 19:180–191

    Article  Google Scholar 

  57. Velardi P, Fabriani P, Missikoff M (2001) Using text processing techniques to automatically enrich a domain ontology. In: Proceedings of the ACM Conference on Formal Ontologies in Information Systems, 2001, pp 270–284

  58. Velardi P, Faralli S, Navigli R (2013) OntoLearn reloaded: a graph-based algorithm for taxonomy induction. Comput Linguist 39(3):665–707

    Article  Google Scholar 

  59. Velardi P, Navigli R, Cucchiarelli A et al (2005) Evaluation of OntoLearn, a methodology for automatic learning of domain ontologies. In: Buitelaar P, Cimiano P, Magnini B (eds) Ontology learning from text: methods, applications and evaluation. IOS Press, Amsterdam, pp 92–106

    Google Scholar 

  60. Wang W, Mamaani Barnaghi P, Bargiela A (2010) Probabilistic topic models for learning terminological ontologies. IEEE Trans Knowl Data Eng 22(7):1028–1040

    Article  Google Scholar 

  61. Wang Y, Patrick J (2009) Cascading classifiers for named entity recognition in clinical notes. In: Proceedings of the workshop on biomedical information extraction, 2009, pp 42–49

  62. Weichselbraun A, Wohlgenannt G, Scharl A (2010) Refining non-taxonomic relation labels with external structured data to support ontology learning. J Data Knowl Eng 69(8):763–778

    Article  Google Scholar 

  63. Wong MK, Abidi SSR, Jonsen ID (2014) A multi-phase correlation search framework for mining non-taxonomic relations from unstructured text. J Knowl Inf Syst 38(3):641–667

    Article  Google Scholar 

  64. Wong W, Liu W, Bennanoun M (2012) Ontology learning from text: a look back and into the future. ACM Comput Surv 44(4):20

    Article  Google Scholar 

  65. Xu Y, Mou L, Li G, Chen Y, Peng H, Jin Z (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 EMNLP, 2015, pp 1785–1794

  66. Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3(3/1/2003):1083–1106

    MathSciNet  MATH  Google Scholar 

  67. Zhang Z (2008) Mining relational data from text: from strictly supervised to weakly supervised learning. Inf Syst 33(3):300–314

    Article  Google Scholar 

  68. Zhou G D, Su J, Zhang J, and Zhang M (2005) Exploring various knowledge in relation extraction. In: Proceedings of the ACL’2005, 2005, pp 419–444

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (61300120), partially supported by the China Postdoctoral Science Foundation (2015M582622), Colleges of Science and Technology Research Foundation in Hebei Province (YQ2013032, YQ2014036), Science and technology department of Hebei province of china (15210338), and The Open Project of Beijing Key Laboratory of IOT information security technology, Institute of Information Engineering. And we thank Harbin Institute of Technology Information Retrieval Laboratory for providing us with LTP modules.

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Qiu, J., Qi, L., Wang, J. et al. A hybrid-based method for Chinese domain lightweight ontology construction. Int. J. Mach. Learn. & Cyber. 9, 1519–1531 (2018). https://doi.org/10.1007/s13042-017-0661-0

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