Discovering relational knowledge from two disjoint sets of literatures using inductive logic programming

2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings

Volumn , Issue , 2009, Pages 283-290

  Thaicharoen, Supphachai ^a   Altman, Tom ^a   Gardiner, Katheleen ^b   Cios, Krzysztof J ^c  

^a The University of Colorado Denver   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c VIRGINIA COMMONWEALTH UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL POLARITY; DISCOVERY MODEL; DISJOINT SETS; DOWN SYNDROME; FISH OIL; FREQUENT PATTERNS; HYPOTHESIS GENERATION; INDUCTIVE LOGIC PROGRAMMING; TEXT MINING;

ARTIFICIAL INTELLIGENCE; CELL MEMBRANES; LOGIC PROGRAMMING; MINING;

DATA MINING;

EID: 67650463009     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2009.4938661     Document Type: Conference Paper

References (23)

1. D. R. Swanson, Fish oil, raynaud's syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, vol. 30, no. 1, pp. 7-18, 1986.
2. R. K. Lindsay and M. D. Gordon, Literature-based discovery by lexical statistics, Journal of American Society for Information Science (JASIST), vol. 50, no. 7, pp. 574-587, 1999.
3. W. Pratt and M. Yetisgen-Yildiz, Litlinker: capturing connections across the biomedical literature, in K-CAP '03: Proceedings of the 2nd international conference on Knowledge capture. NewYork,NY, USA: ACM, 2003, pp. 105-112.
4. P. Srinivasan, Text mining: generating hypotheses from medline, Journal of the American Society for Information Science and Technology, vol. 55, no. 5, pp. 396-413, 2004.
5. W. Jin and R. K. Srihari, Knowledge discovery across documents through concept chain queries, in ICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining-Workshops. Washington, DC, USA: IEEE Computer Society, 2006, pp. 448-452.
6. D. Hristovski, B. Peterlin, J. A. Mitchell, and S. M. Humphrey, Improving literature based discovery support by genetic knowledge integration. Stud Health Technol Inform., vol. 95, pp. 68-73, 2003.
7. J. Stegmann and G. Grohmann, Hypothesis generation guided by coword clustering, Scientometrics, vol. 56, no. 1, pp. 111-135, 2003. (Pubitemid 39583172)
8. J. D. Wren, Extending the mutual information measure to rank inferred literature relationships, BMC Bioinformatics, vol. 5, no. 145, 2004. (Pubitemid 40195362)
9. S. Muggleton and L. De Raedt, Inductive logic programming: Theory and methods, Journal of Logic Programming, vol. 19/20, pp. 629-'679, 1994.
10. S. Muggleton, Inverse entailment and progol, New Generation Computing, Special issue on Inductive Logic Programming, vol. 13, no. 3-4, pp. 245-286, 1995.
11. J. R. Quinlan, Learning logical definitions from relations, Mach. Learn., vol. 5, no. 3, pp. 239-266, 1990.
12. L. Dehaspe and H. Toivonen, Discovery of frequent datalog patterns, Data Min. Knowl. Discov., vol. 3, no. 1, pp. 7-36, 1999.
13. L. Dehaspe and H. T. T. Toivonen, Discovery of relational association rules, in Relational data mining. Springer-Verlag, 2001, pp. 189-212.
14. M. Liakata and S. Pulman, Learning theories from text, in COLING '04: Proceedings of the 20th international conference on Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2004, p. 183.
15. J. Blatak, First-order frequent patterns in text mining, Artificial intelligence, 2005. epia 2005. portuguese conference on, pp. 344-350, Dec. 2005.
16. R. D. King, A. Srinivasan, and L. Dehaspe, Warmr: a data mining tool for chemical data, Journal of Computer-Aided Molecular Design, vol. 15, no. 2, pp. 173-181, 2001. (Pubitemid 32124509)
17. R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases. in SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 1993, pp. 207-216.
18. D. R. Swanson and N. R. Smalheiser, An interactive system for finding complementary literatures: a stimulus to scientific discovery, Artificial Intelligence, vol. 91, no. 2, pp. 183-203, 1997. (Pubitemid 127371600)
19. D. R. Swanson, Complementary structures in disjoint science literatures, in SIGIR '91: Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM, 1991, pp. 280-289.
20. Y. Tsuruoka, Y. Tateishi, J.-D. Kim, T. Ohta, J. McNaught, S. Ananiadou, and J. Tsujii, Developing a robust part-of-speech tagger for biomedical text, in Advances in Informatics -10th Panhellenic Conference on Informatics, 2005, pp. 382-392.
21. Y. Tsuruoka and J. Tsujii, Bidirectional inference with the easiest-first strategy for tagging sequence data, in Proceedings of HLT/EMNLP, 2005, pp. 467-474.
22. J. D. Wren, R. Bekeredjian, J. A. Stewart, R. V. Shohet, and H. R. Garner, Knowledge discovery by automated identification and ranking of implicit relationships, Bioinformatics, vol. 20, no. 3, pp. 389-398, 2004, bibtex is from ACM. (Pubitemid 38262771)
23. H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele, Improving the efficiency of inductive logic programming through the use of query packs, Journal of Artificial Intelligence Research, vol. 16, pp. 135-166, 2002. (Pubitemid 43064838)