Why is Information Retrieval a Scientific Discipline?

• Al-Maskari, A., Sanderson, M., & Clough, P. (2008). Relevance judgments between TREC and non-TREC assessors. In Proceedings of the 31st ACM SIGIR conference (pp. 683–684).

• Azzopardi, L., & Roelleke, T. (2007). Explicitly considering relevance within the language modeling framework. In Proceedings of the 1st international conference on theory of information retrieval (pp. 125–134).

• Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454.

  Article  Google Scholar 

• Basat, R. B., Tennenholtz, M., & Kurland, O. (2015). The probability ranking principle is not optimal in adversarial retrieval settings. In Proceedings of ICTIR’15 (pp. 51–60).

• Cartwright, N. (1995). False idealization: A philosophical threat to the scientific method. Philosophical Studies, 77(2–3), 339–352.

  Article  Google Scholar 

• Cerf, V. G. (2012). Where is the science in computer science? Communications of the ACM, 55(10), 5.

  Article  Google Scholar 

• Chalmers, A. F. (2013). What is this thing called science?. Maidenhead: Open University Press.

  Google Scholar 

• Cleland, C. E. (2001). Historical science, experimental science and the scientific method. Geology, 29(11), 987–990.

  Article  Google Scholar 

• Cooper, W. S. (1995). Some inconsistencies and misidentified modeling assumptions in probabilistic information retrieval. ACM Transactions on Information Systems, 13(1), 100–111.

  Article  Google Scholar 

• Costa, A., & Roda, F. (2011). Recommender systems by means of information retrieval. In Proceedings of WIMS’11, Article no. 57.

• Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice. Upper Saddle River, NJ: Pearson Addison-Wesley.

  Google Scholar 

• Damessie, T. T., Nghiem, T. P., Scholer, F., & Culpeper, J. S. (2017). Gauging the quality of relevance assessments using inter-rater agreement. In Proceedings of the 40th ACM SIGIR conference (pp. 1089–1092).

• Dang, E. K. F., Wu, H. C., Luk, R. W. P., & Wong, K. F. (2009). Building a framework for the probability ranking principle by a family of expected weighted rank. ACM Transactions on Information Systems, 27, 4.

  Article  Google Scholar 

• Denning, P. J. (2005). Is computer science science? Communications of the ACM, 48(4), 27–31.

  Article  Google Scholar 

• Denning, P. J. (2007). Computing is a natural science. Communications of the ACM, 50(7), 13–18.

  Article  Google Scholar 

• Denning, P. J. (2013). The science in computer science. Communications of the ACM, 56(5), 35–38.

  Article  Google Scholar 

• Feyeraband, P. (2011). The tyranny of science. London: Polity Press.

  Google Scholar 

• Fuhr, N. (2008). A probability ranking principle for interactive information retrieval. Information Retrieval, 11(3), 251–265.

  Article  Google Scholar 

• Fuhr, N. (2012). Salton award lecture information retrieval as an engineering science. ACM SIGIR Forum, 46(2), 19.

  Article  Google Scholar 

• Fuhr, N. (2017). Some common mistakes in IR evaluation, and how they can be avoided. ACM SIGIR Forum, 51(3), 32–41.

  Article  Google Scholar 

• Gonzalo, G. (2010). Is computer science truly scientific? Communications of the ACM, 53(7), 37–39.

  Article  Google Scholar 

• Greiff, W. R. (1998). A theory of term weighting based on exploratory data analysis. In Proceedings of the 21st ACM SIGIR conference (pp. 11–19).

• Huston, S., & Croft, W. B. (2014). A comparison of retrieval models using term dependencies. In Proceedings of the 23rd ACM CIKM conference (pp. 111–120).

• Indri. (2013). INDRI: Language modeling meets inference networks. The Lemur Project. Retrieved June 27, 2020 from http://lemurproject.org/indri/.

• Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information System, 20(4), 422–446.

  Article  Google Scholar 

• Kosso, P. (2007). Scientific understanding. Foundations of Science, 12(2), 119–130.

  Article  Google Scholar 

• Lafferty, J., & Zhai, C. X. (2001). Probabilistic relevance models based on document and query generation. In B. Croft & J. Lafferty (Eds.), Language modeling for information retrieval (pp. 1–10). Dordrecht: Springer.

  Google Scholar 

• Lavrenko, V. (2009). A Generative Theory of Relevance. Berlin: Springer.

  Google Scholar 

• Lin, J. (2018). The neural hype and comparison against weak baselines. ACM SIGIR Forum, 52(2), 40–51.

  Article  Google Scholar 

• Luk, R. W. P. (2008). On event space and rank equivalence between probabilistic retrieval models. Information Retrieval, 11, 539–561.

  Article  Google Scholar 

• Luk, R. W. P. (2010). Understanding scientific study via process modeling. Foundations of Science, 15(1), 49–78.

  Article  Google Scholar 

• Luk, R. W. P. (2017). A theory of scientific study. Foundations of Science, 22(1), 11–38.

  Article  Google Scholar 

• Luk, R. W. P. (2018). To explain or to predict: Which one is mandatory? Foundations of Science, 23(2), 411–414.

  Article  Google Scholar 

• Maron, M. E., & Kuhns, J. L. (1960). On relevance, probabilistic indexing and information retrieval. Journal of the ACM, 7(3), 216–244.

  Article  Google Scholar 

• Paik, J. H. (2013). A novel TF-IDF weighting scheme for effective ranking. In Proceedings of the 36th ACM SIGIR conference (pp. 343–352).

• Popper, K. (1959). The logic of scientific discovery. London: Hutchinson.

  Google Scholar 

• Rapaport, W. J. (2019). Philosophy of computer science. Retrieved March 25, 2019 from http://cse.buffalo.edu/~rapaport/Papers/phics.pdf.

• Raza, K. (2014). Is the discipline “computer science” a “natural science”? Retrieved June 27, 2020 from https://www.researchgate.net/post/Is_the_discipline_Computer_Science_a_Natural_Science2.

• Reiss, J., & Sprenger, J. (2017). Scientific objectivity. In E. N. Zalta (Eds.), The Stanford encyclopedia of philosophy (Winter 2017 Edition). Retrieved June 27, 2020 from https://plato.stanford.edu/archives/win2017/entries/scientific-objectivity.

• Robertson, S. E. (1977). The probability ranking principle in IR. Journal of Documentation, 33, 294–304.

  Article  Google Scholar 

• Robertson, S. E. (2006). On GMAP: And other transformations. In Proceedings of the 15th ACM CIKM conference (pp. 78–83).

• Saracevic, T. (1975). Relevance: A review of and a framework for the thinking on the notion in information science. Journal of the Association for Information Science and Technology, 26(6), 321–343.

  Google Scholar 

• Singhal, A., Buckley, C., & Mitra, M. (1996). Pivoted document length normalization. In Proceedings of the 19th ACM SIGIR conference (pp. 21–29).

• Sordoni, A., Nie, J.-Y., & Bengio, Y. (2013). Modeling term dependencies with quantum language models for IR. In Proceedings of the 36th ACM SIGIR conference (pp. 653–662).

• Spärck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.

  Article  Google Scholar 

• Terrier. (2019). Terrier v5.1. University of Glasgow. Retrieved July 3, 2019 from http://terrier.org.

• Van Fraassen, B. (1980). The scientific image. Oxford: Clarendon Press.

  Book  Google Scholar 

• Van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworths.

  Google Scholar 

• Van Rijsbergen, C. J. K. (2006). Quantum haystacks. In Proceedings of the 29th ACM SIGIR conference (pp. 1–2).

• Wong, K. F., Song, D., Bruza, P., & Chen, C.-H. (2001). Application of aboutness to functional benchmarking in information retrieval. ACM Transactions on Information Systems, 19(4), 337370.

  Article  Google Scholar 

• Wu, H. C., Luk, R. W. P., Wong, K. F., & Kwok, K. L. (2008). Interpreting TF-IDF weights as making relevance decisions. ACM Transactions on Information Systems, 26, 3.

  Article  Google Scholar 

• Yang, P, & Feng, H. (2016). A reproducibility study of information retrieval models. In Proceedings of ICTIR’16 (pp. 77–86).

• Zamani, H., Croft, W. B., & Culpepper, J. S. (2018). Neural query performance prediction using weak supervision from multiple signals. In Proceedings of the 41st ACM SIGIR conference (pp. 105–114).

• Zhai, C. X. (2011). Axiomatic analysis and optimization of information retrieval models. In Proceedings of ICTIR 2011 conference (p. 1).

• Zhai, C. X., & Lafferty, J. (2004). A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems, 22(2), 179–214.

  Article  Google Scholar 

• Zobel, J. (2017). What we talk about when we talk about information retrieval. ACM SIGIR Forum, 51(3), 18–26.

  Article  Google Scholar 

• Zuccon, G., Azzopardi, L. A., & van Rijsbergen, C. J. K. (2009). The quantum probability ranking principle for information retrieval. In Proceedings of the ICTIR ‘09 (pp. 232–240).

• Zuo, J., Wang, M., Wan, J., Wu, G., & Wu, S. (2012). Modified information retrieval model based on Markov network. In Proceedings of international conference on network computing and information security (pp. 307–314).

Download references