Learning Context-Sensitive Domain Ontologies from Folksonomies: A Cognitively Motivated Method

Published Online:https://doi.org/10.1287/ijoc.2015.0644

References

  • Bizer C, Heath T, Berners-Lee T (2009) Linked data—The story so far. Internat. J. Semantic Web Inform. Systems 5(3):1–22.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3:993–1022.Google Scholar
  • Borth D, Chen T, Ji R, Chang S-F (2013) Sentibank: Large-scale ontology and classifiers for detecting sentiment and emotions in visual content. Proc. 2013 ACM Multimedia Conf. (ACM, Barcelona, Spain), 459–460.CrossrefGoogle Scholar
  • Brice P, Jiang W, Wan G (2011) A cluster-based context-tree model for multivariate data streams with applications to anomaly detection. INFORMS J. Comput. 23(3):364–376.LinkGoogle Scholar
  • Chun MM, Jiang Y (1998) Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psych. 36(1):28–71.CrossrefGoogle Scholar
  • Cimiano P, Hotho A, Staab S (2005) Learning concept hierarchies from text corpora using formal concept analysis. J. Artificial Intelligence Res. 24:305–339.CrossrefGoogle Scholar
  • Daud A, Li J, Zhou L, Zhang L, Ding Y, Muhammad F (2010) Modeling ontology of folksonomy with latent semantics of tags. Proc. IEEE/WIC/ACM Internat. Conf. Web Intelligence (IEEE, Toronto, Canada), 516–523.CrossrefGoogle Scholar
  • Gasevic D, Zouaq A, Torniai C, Jovanovic J, Hatala M (2011) An approach to folksonomy-based ontology maintenance for learning environments. IEEE Trans. Learn. Tech. 4(4):301–314.CrossrefGoogle Scholar
  • Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian relation of images. IEEE Trans. Pattern Anal. Machine Intelligence 6(6):721–741.CrossrefGoogle Scholar
  • Gluck M, Corter J (1985) Information, uncertainty, and the utility of categories. Proc. Seventh Annual Conf. Cognitive Sci. Soc., Berkeley, CA, 283–287.Google Scholar
  • Gruber TR (1993) A translation approach to portable ontology specifications. Knowledge Acquisition 5(2):199–220.CrossrefGoogle Scholar
  • Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora. Proc. 14th Internat. Conf. Comput. Linguistics, Nantes, France, 539–545.CrossrefGoogle Scholar
  • Heymann P, Garcia-Molina H (2006) Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical Report 2006-10, Stanford InfoLab, Stanford, CA.Google Scholar
  • Jupiter D, Şahutoğlu J, VanBuren V (2010) Treehugger: A new test for enrichment of gene ontology terms. INFORMS J. Comput. 22(2):210–221.LinkGoogle Scholar
  • Lau RYK, Song D, Li Y, Cheung CH, Hao JX (2009) Towards a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Trans. Knowledge Data Engrg. 21(6):800–813.CrossrefGoogle Scholar
  • Liu K, Fang B, Zhang W (2010) Ontology emergence from folksonomies. Proc. 19th ACM Conf. Inform. Knowledge Management (ACM, Ontario, Canada), 1109–1118.CrossrefGoogle Scholar
  • Maedche A, Staab S (2004) Ontology learning. Staab S, Studer R, eds. Handbook on Ontologies (Springer, Berlin), 173–190.CrossrefGoogle Scholar
  • Mika P (2007) Ontologies are us: A unified model of social networks and semantics. J. Web Semantics 5(1):5–15.CrossrefGoogle Scholar
  • Missikoff M, Taglino F (2004) An ontology-based platform for semantic interoperability. Staab S, Studer R, eds. Handbook on Ontologies (Springer, Berlin), 617–634.CrossrefGoogle Scholar
  • Munir MU, Javed MY, Khan SA (2012) A hierarchical k-means clustering based fingerprint quality classification. Neurocomputing 85:62–67.CrossrefGoogle Scholar
  • Panetto H, Dassisti M, Tursi A (2012) ONTO-PDM: Product-driven ONTOlogy for product data management interoperability within manufacturing process environment. Advanced Engrg. Informatics 26(2):334–348.CrossrefGoogle Scholar
  • Ponzetto SP, Strube M (2007) Deriving a large scale taxonomy from Wikipedia. Proc. Twenty-Second National Conf. Artificial Intelligence (AAAI Press/MIT Press, Cambridge, MA), 1440–1445.Google Scholar
  • Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P (1976) Basic objects in natural categories. Cognitive Psych. 8(3): 382–493.CrossrefGoogle Scholar
  • Rosen-Zvi M, Chemudugunta C, Griffiths TL, Smyth P, Steyvers M (2010) Learning author-topic models from text corpora. ACM Trans. Inform. Systems 28(1):Article no. 4.CrossrefGoogle Scholar
  • Roussinov D, Zhao JL (2003) Automatic discovery of similarity relationships through Web mining. Decision Support Systems 35(1): 149–166.CrossrefGoogle Scholar
  • Salton G (1986) Recent trends in automatic information retrieval. Proc. 9th Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 1–10.CrossrefGoogle Scholar
  • Salton G, McGill MJ (1983) Introduction to Modern Information Retrieval (McGraw-Hill, New York).Google Scholar
  • Sanderson M, Croft B (1999) Deriving concept hierarchies from text. Proc. 22nd Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 206–213.CrossrefGoogle Scholar
  • Schmitz P (2006) Inducing ontology from Flickr tags. Proc. 2006 Collaborative Web Tagging Workshop (WWW’06) (ACM, Edinburgh, Scotland).Google Scholar
  • Strohmaier M, Helic D, Benz D, Körner C, Kern R (2012) Evaluation of folksonomy induction algorithms. ACM Trans. Intelligent Systems Technol. 3(4):Article no. 74.CrossrefGoogle Scholar
  • Tanaka J, Taylor M (1991) Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psych. 23(3): 457–482.CrossrefGoogle Scholar
  • Tang J, Leung HF, Luo Q, Chen D, Gong J (2009) Towards ontology: Learning from folksonomies. Proc. 21st Internat. Joint Conf. Artificial Intelligence, Pasadena, California, 2089–2094.Google Scholar
  • Tao D, Li Y, Zhong N (2011) A personalized ontology model for Web information gathering. IEEE Trans. Knowledge Data Engrg. 23(4):496–511.CrossrefGoogle Scholar
  • Tho QT, Hui SC, Fong ACM, Cao TH (2006) Automatic fuzzy ontology generation for semantic web. IEEE Trans. Knowledge Data Engrg. 18(6):842–856.CrossrefGoogle Scholar
  • Trabelsi C, Jrad AB, Yahia SB (2010) Bridging folksonomies and domain ontologies: Getting out non-taxonomic relations. The 10th IEEE Internat. Conf. Data Mining Workshops (IEEE Computer Society, Sydney, Australia), 369–379.CrossrefGoogle Scholar
  • Wasserman S, Faust K (1999) Social Network Analysis: Methods and Applications (Cambridge University Press, Cambridge, UK).Google Scholar
  • Wei W, Barnaghi PM, Bargiela A (2010) Probabilistic topic models for learning terminological ontologies. IEEE Trans. Knowledge Data Engrg. 22(7):1028–1040.CrossrefGoogle Scholar
  • Wong W, Liu W, Bennamoun M (2012) Ontology learning from text: A look back and into the future. ACM Comput. Survey 44(4):Article no. 20.CrossrefGoogle Scholar
  • Yan X, Lau RYK, Song D, Li X, Ma J (2011) Towards a semantic granularity model for domain-specific information retrieval. ACM Trans. Inform. Systems 29(3):Article no. 15.CrossrefGoogle Scholar
  • Zavitsanos I, Paliouras G, Vouros G (2011) Gold standard evaluation of ontology learning methods through ontology transformation and alignment. IEEE Trans. Knowledge Data Engrg. 23(11):1635–1648.CrossrefGoogle Scholar
  • Zhou M, Bao S, Wu X, Yu Y (2007) An unsupervised model for exploring hierarchical semantics from social annotations. 6th Internat. Semantic Web Conf., Lecture Notes in Computer Science, Vol. 4825 (Springer, Busan, Korea), 680–693.CrossrefGoogle Scholar
  • Zouaq A, Nkambou R (2009) Evaluating the generation of domain ontologies in the knowledge puzzle project. IEEE Trans. Knowledge Data Engrg. 21(11):1559–1572.CrossrefGoogle Scholar
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