Discovery by scent: Discovery browsing system based on the Information Foraging Theory

Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012

Volumn , Issue , 2012, Pages 232-239

  Goodwin, J Caleb ^a   Cohen, Trevor ^a   Rindflesch, Thomas ^b  

^a UNIVERSITY OF TEXAS   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

discovery browsing; Information foraging theory; Literature based discovery; spreading activation

Indexed keywords

BROWSING SYSTEMS; DISCOVERY BROWSING; INFORMATION FORAGING; INFORMATION SEEKING BEHAVIORS; LITERATURE-BASED DISCOVERIES; SPREADING ACTIVATIONS; THEORETICAL FOUNDATIONS; WORLD WIDE WEBS (WWW);

BIOINFORMATICS; INFORMATION RETRIEVAL; MATHEMATICAL MODELS; WORLD WIDE WEB;

WEB BROWSERS;

EID: 84870476662     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBMW.2012.6470309     Document Type: Conference Paper

References (43)

1. Wilson, P., Unused relevant information in research and development. Journal of the American Society for Information Science, 1995. 46(1): p. 45-51.
2. Swanson, D.R., Fish-oil, Raynaud's Syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 1986.30(1): p. 7-18.
3. Swanson, D.R., Undiscovered public knowledge. Library Quarterly, 1986.56(2): p. 103-118.
4. Kilicoglu, H., et al., Semantic MEDllNE: A web application for managing the results of PubMed Searches, in Proceedings of the Third International Symposium for Semantic Mining in Biomedicine. 2008.
5. Rindflesch, T.C. and M. Fiszman, The interaction ofdomain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 2003.36(6): p. 462-77.
6. Wilkowski, B., et al., Discovery browsing with semantic predications and graph theory. AMIA Annu Symp Proc. 2011, 2011.
7. Miller, C.M., et al., A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. Sleep, 2012.35(2): p. 279-285.
8. Freeman, L.C., Centrality in social networks: Conceptual clarification. Social Networks, 1978. 1: p. 215-239.
9. Pirolli, P. and S. Card, Information foraging, 1999: Oxford University Press.
10. Anderson, 1., How can the human mind occur in the physical universe?, 2007, New York, NY: Oxford University Press.
11. Pirolli, P., Rational analyses of information foraging on the Web. Cognitive Science, 2005. 29: p. 343-373.
12. Crestani, F., Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 1997. 11: p. 453-482.
13. Weeber, M., et al., Using concepts in literature-based discovery: Simulating Swanson's raynaud-fish oil and migraine-magnesium discoveries. Journal of the American Society for Information Science, 2001. 52(7): p. 548-557.
14. Bruza, P. and M. Weeber, Literature-based discovery, 2008: SpringerVerlag.
15. Hristovski, D., et al., Exploring semantic relations for literature-based discovery, in AJv!IASnnu Symp Proc 2006, 2006.
16. Ahlers, C.B., et al., Using the literature-based discovery paradigm to investigate drug mechanisms, AJv!IAAnnu Symp Proc 2007,2007. p. 6-10.
17. Hristovski, D., et al., Combining semantic relations and DNA microarray data for novel hypothesis generation, in Linking literature, information, and knowledge for biology, 2010, SpringerLink.
18. Cohen, T., et al, Finding schizophrenia's prozac emergent relational similarity in predication space. in Quantum Interaction. 2011.
19. Cohen, T., et al., Logical leaps and quantum connectives: Forging paths through predication space, in AAAI Fall 2010 Symposium on Quantum Informatics for cognitve, social, and semantic processes, 2010.
20. Cohen, T., R. Schvaneveldt, and T. Rindflesch. Predication-based semantic indexing: Permutations as a means to encode predications in semantic space. in AJv!IAAnnu Symp Proc. 2009.
21. Bell, C.G. and A. Newell, Computer Structures: Readings and Examples, 1971, New York: McGraw-Hill.
22. Anderson, 1.R. and G.H. Bower, Human associative memory, 1973, Washington, DC: Winston &Sons.
23. Anderson, 1.R., Reflections of the environment in memory. Psychological Science, 1991(2): p. 396-408.
24. Anderson, 1.R., The adaptive nature of human categorization. Psychological Review, 1991.98: p. 409-429.
25. Schooler, LJ. and 1.R. Anderson, The role ofprocess in the rational analysis ofhuman memory. Cognitive Psychology, 1997.32: p. 219-250.
26. Anderson, J.R.,et al., A central circuit ofthe mind Trends in Cognitive Sciences, 2008. 12 (136-143).
27. Anderson, 1.R. and R. Milson, Human memory: An adaptive perspective. Psychological Review, 1989.96: p. 703-719.
28. Goodwin, 1.C., et al., Predicting biomedical document access as a function ofpast use. JAmMed Inform Assoc., 2011.
29. Recker, M.M. and 1.E. Pitkow, Predicting document access in large multimedia repositories. ACM transactions on Computer-Human Interaction (TOCHI), 1996.3(4).
30. Van Maanen, L., et al., Personal publication assistant: abstract recommendation by a cognitive model. Cognitive Systems Research, 2009.
31. Steyvers, M. and T.L. Griffiths, Rational analysis as a link between human memory and information retrieval, in The Probabilistic Mind: Prospects from Rational Modles of Cognition, 2010, Oxford University Press.
32. Mei, Q., D. Zhang, and C.X. Zhai. A general optimization framework for smoothing language models on graph structures. in In SIGIR '08: Proceedings of the 31st annaul international ACM SIGIR conference on research and development in information retrieval. 2008. USA, New York, NY: ACM.
33. Opsahi, T., F. Agneessens, and J. Skvoretz, Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 2010. 32(3): p. 245-251.
34. Brown, P.F., et al., A statistical approach to machine translation. Computation Linguistics, 1990. 16(2): p. 79-85.
35. Jelinek, F., Statistical methods for speech recognition, 1997: MIT Press.
36. Ponte, 1. and W.B. Croft. A language modeling approach to infromation retrieval. in In Proceedings oftheACMSIGIR 98. 1998.
37. Singhal, A., C. Buckley, and M. Mitra. Pivoted document length normalization. In Proceedings of the 1996 ACM SIGIR Conference on Research andDevelopment in Information Retrieval. 1996.
38. Hiemstra, D. and W. Kraaij. Twenty-one at TREC-7: Ad-hoc and cross language track. in In Proceedings ofSeventh Text Retriveal Conference (TREC-7). 1998.
39. Hiemstra, D., A probabilistic justification for using if x idf term weighting in information retrieval. International Journal on Digital Libraries, 2000.3: p. 131-139.
40. Lavrenko, V. and W.B. Croft. Relevance-based language models. In Proc. 24th ACMSIGIR Con! on Research and Development in Information Retrieval. 2001.
41. Kintsch, W., Comprehension: A paradigm for cognition, 1998: Cambridge University Press.
42. Widdows, D. and T. Cohen, The semantic vectors package: New algorithms and public tools for distributional semantics, in Fourth IEEE International Conference on Semantic Computing (IEEE ICSC2010) 2010: Carnegie Mellon University, Pittsburgh, Pennsylvania.
43. Rose, D.E. and D. Levinson. Understanding user goals in Web search. In Proc WWW. 2004.