Comprehensive review reading list for Fall 2017 and Spring 2018 Artificial Intelligence/Knowledge Management



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COMPREHENSIVE REVIEW READING LIST for Fall 2017 and Spring 2018

Artificial Intelligence/Knowledge Management


  1. McCarthy, J., and Hayes, P.(1969) Some Philosophical Problems from the

Standpoint of Artificial Intelligence. Machine Intelligence. Vol. 4, p.463-502.

  1. Tversky, A. and D. Kahneman (1974). Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124-1131.



  1. Stanfill, C. and Waltz, D. (1986) Toward Memory-Based Reasoning, Communications of the ACM, 29(12):1213-1228, December.



  1. Fahlman, S. and Hinton,G. (1987) Connectionist Architectures for Artificial Intelligence, IEEE Computer 20(1):100-109.



  2. L.B. Booker, D.E. Goldberg, J.H. Holland (1989). Classifier Systems and Genetic Algorithms. Artificial Intelligence, Volume 40, Issues 1–3, September 1989, Pages 235–282



  3. Alavi, M. and D. E. Leidner (2001). Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly 25(1): 107-136.



  4. Schultze, U. and D. E. Leidner (2002). Studying Knowledge Management in Information Systems Research: Discourses and Theoretical Assumptions. MIS Quarterly 26(3): 213-242.



  1. Woodridge, M., & Jennings, N. R. (1995). Intelligent Agents: Theory and Practice. The Knowledge Engineering Review,10(2), 115-152.



  1. Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd (1998). The PageRank citation ranking: Bringing order to the Web. 1998. Available at http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf



  1. Wang, F-Y., Zeng, D., Carley, K. M., & Mao, W. (2007). Social Computing: From Social Informatics to Social intelligence. IEEE Intelligent Systems, 22(2), 79-83.



  1. Gueorgi Kossinets and Duncan J. Watts (2006), Empirical Analysis of an Evolving Social Network, Science, 311 (5757), 88-90.



  2. Adomavicius, G. and Tuzhillin, A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering. 17(6), June 2005, 734-749.




  1. P Resnick, N Iacovou, M Suchak, P Bergstrom, J Riedl (1994). GroupLens: an open architecture for collaborative filtering of Netnews. Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work (CSCW’94).



  1. Sung-Pil Choi and Sung-HyonMyaeng (2012) Terminological Paraphrase Extraction from Scientific Literature Based on Predicate Argument Tuples. Journal of Information Science, 38(6) 593–611.




  1. S Deerwester, S T Landauer T K Dumais, G W Furnas, R A Harshman (1990). Indexing by Latent Semantic Analysis. Journal of the Society for Information Science. 41(6). Available at http://www.cob.unt.edu/itds/faculty/evangelopoulos/dsci5910/LSA_Deerwester1990.pdf.


  2. A L Berger, S A Della Pietra, V J Della Pietra (1996). A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics, 1996.



  1. M. Simpson, and D. Demner-Fushman (2012) Biomedical Text Mining: A Survey of Recent Progress. C.C. Aggarwal and C.X. Zhai (eds.). Mining Text Data, Springer.




  1. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton (2015). Deep Learning, Nature 521( 7553), 436-444.




  1. C J C Burges (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 2, p. 121-167. Available at http://research.microsoft.com/pubs/67119/svmtutorial.pdf

  2. Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of the Tenth European Conference on Machine Learning. Available at http://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf



  3. Ghahramani, Z., & Jordan, M. I. (1997). Factorial Hidden Markov models, Machine Learning, 29, pp. 245–273



  4. Demetrios Zeinalipour-Yazti, Christos Laoudias, Constandinos Costa, Michail Vlachos, Maria I. Andreou, Dimitrios Gunopulos (2013). Crowd sourced Trace Similarity with Smartphones, IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 6, pp. 1240-1253, June 2013



  5. Anind K. Dey, Daniel Salber and Gregory D. Abowd (2001). A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-aware Applications. Human Computer Interaction. 16 (2-4), pp. 97-166




  1. Philemon Brakel and Dirk Stroobandt and Benjamin Schrauwen, Training Energy-Based Models for Time-Series Imputation, Journal of Machine Learning Research, pp. 2771-2797, 2013



  2. Abbasi, Ahmed; Zhang, Zhu; Zimbra, David; Chen, Hsinchun; and Nunamaker, Jay F. Jr.. (2010). "Detecting Fake Websites: The Contribution of Statistical Learning Theory," MIS Quarterly, (34: 3) pp.435-461.



  3. Hevner, A.R., March, S.T., Jinsoo Park, J. and Ram, S. (2004).Design Science in Information Systems Research. MIS Quarterly. 28(1), 75-105.





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