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Constraint-based Ontology Induction from Online Customer Reviews

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Abstract

We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process.

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References

  • “Epinions.com Customer Reviews”. Accessed: 5 July 2004, http://www.epinions.com/ Digital_Cameras

  • Black, P. (2005). “Dictionary of Algorithms and Data Structures,” accessed: February, 2005, http://www.nist.gov/dads

  • Borgelt, C. and R. Kruse. (2002). “Induction of Association Rules: Apriori Implementation,” 15th Conf on Computational Statistics (Compstat), Berlin, Germany

  • Brelaz D. (1979). New Methods to Color the Vertices of a Graph. Communications of the ACM 22:251–256

    Article  Google Scholar 

  • Consumer_Electronics_HQ. “Buying Your First Digital Camera: The Basics,” accessed: 19 November, 2004, http://www.digitalcamera-hq.com/hqguides/firsttime-buyer.html

  • Consumer_Union. (2005). “Digital Cameras,” Consumer Reports Buying Guide, 33–36; 237–240

  • Etzioni, O., M. Cafarella, D. Downey, S. Kok, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. (2004). “Web-scale Information extraction in KnowItAll (Preliminary Results),” WWW 2004, New York, NY

  • Finkelstein-Landau, M. and E. Morin. (1999). “Extracting Semantic Relationships between Terms: Supervised vs. Unsupervised Methods,” International Workshop on Ontological Engineering on the Global Information Infrastructure, Dagstuhl Castle, Germany

  • Ganti, V., J. Gehrke and R. Ramakrishnan. (1999). “CACTUS – Clustering Categorial Data Using Summaries,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA

  • Gibson, D., J. Kleinberg, and P. Raghavan. (1998). “Clustering Categorical Data: an Approach Based on Dynamical Systems,” 24th International Conference on Very Large Databases (VLDB)

  • Gruber, T. (1993).“Toward Principles for the Design of Ontologies used for Knowledge Sharing, Stanford KSL-93-04,” International Workshop on Formal Ontology

  • Halkidi, M., Y. Batistakis and M. Vazirigianis. (2002). “Clustering Validity Checking Methods: Part II,” SIGMOD Record 31(3), 19–27

    Google Scholar 

  • Han, J. and Y. Fu. (1994). “Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases,” AAAI 94 Workshop on Knowledge Discovery in Databases (KDD94)

  • Hearst, M. (1992). “Automatic Acquisition of Hyponyms from Large Text Corpora,” Fourteenth International Conference on Computation Linguistics (COLING), Nantes, France

  • Hu, M. and B. Liu. (2004). “Mining and Summarizing Customer Reviews”, KDD04, Seattle, WA

  • Jupitermedia (27 November 2001). “UN: E-Commerce could Bridge the Divide”, ECommerce-Guide.com, URL: www.ecommerce-guide.com/news/trends/article.php/929691

  • Kilgarriff A. (2001). Comparing Corpora. International Journal of Corpus Linguistics 6(1):97–133

    Google Scholar 

  • Maedche, A. and S. Staab. (2000). “Semi-automatic Engineering of Ontologies from Text,” Twelfth International Conference on Software Engineering and Knowledge Engineering (SEKE’2000), Chicago

  • Maedche, A. and S. Staab. (2001). “Ontology Learning for the Semantic Web,” IEEE Intelligent Systems, 16(2), 72–79

    Google Scholar 

  • Maedche A., Neumann G., Staab S. (2002). Bootstrapping an Ontology-based Information Extraction System. In: Szczepaniak P., Segovia J., Kacprzyk J., Zadeh L. (eds), Intelligent Exploration of the Web. Heidelberg, Springer/Physica Verlag

    Google Scholar 

  • Missikoff, M. and R. Navigli. (2002). “Integrated approach to Web ontology learning and engineering,” IEEE Computer, 54–57

  • Modica, G., A. Gal and H. Jamil. (2001). “The Use of Machine-Generated Ontologies in Dynamic Information Seeking,” CoopIS 2001, Trento, Italy

  • Nasukawa, T. and J. Yi. (2003). “Sentiment Analysis: Capturing Favorability Using Natural Language Processing,” K-CAP’03, Sanibel Island, Florida

  • Omelayenko, B. (2001). “Learning of Ontologies for the Web: the Analysis of Existent Approaches”, ICDT 01 International Workshop on Web Dynamics, London, UK

  • Peacock, I. (October 1999). “E-Commerce in Europe”, Exploit Interactive, issue 3, URL: http://www.exploit-lib.org/issue3/ecommerce/

  • Popescu, A.-M., A. Yates, and O. Etzioni. (2004). “Class Extraction from the World Wide Web,” AAAI 2004 Workshop on Adaptive Text Extraction and Mining (ATEM)

  • Salton G., McGill M. (1983). Introduction to Modern Information Retrieval. New York, McGraw-Hill

    Google Scholar 

  • Skiena S. (1998). The Algorithm Design Manual. New York, TELOS, Springer-Verlag

    Google Scholar 

  • Suryanto, H. and P. Compton. (2000). “Learning Classification Taxonomies from a Classification Knowledge based System,” ECAI 2000 Workshop on Ontology Learning, Berlin

  • Tan P., Steinbach M., Kumar V. (2006). Introduction to Data Mining. Pearson Education Inc., Boston

    Google Scholar 

  • Ullman J. (1988). Principles of Database and Knowledge-Base Systems, Vol 1. Computer Science Press, Rockville, MD

    Google Scholar 

  • Zhao, Y. and G. Karypis. (2002). “Criterion Functions for Document Clustering: Experiments and Analysis,” Technical report, Univ of Minnesota, Dept. of Computer Science, Minneapolis

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Authors and Affiliations

  1. Department of Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Thomas Lee

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  1. Thomas Lee
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Correspondence to Thomas Lee.

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Lee, T. Constraint-based Ontology Induction from Online Customer Reviews. Group Decis Negot 16, 255–281 (2007). https://doi.org/10.1007/s10726-006-9065-3

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