TY - GEN
T1 - Discovering relational knowledge from two disjoint sets of literatures using inductive logic programming
AU - Thaicharoen, Supphachai
AU - Altman, Tom
AU - Gardiner, Katheleen
AU - Cios, Krzysztof J.
PY - 2009
Y1 - 2009
N2 - Literature-based discovery for hypothesis generation is a subarea of text mining that aims to discover novel or previously-unknown knowledge from two complementary but disjoint (CBD) sets of literatures. The discovery approach is based on Swanson's discovery models where indirect connections between two disjoint sets of literatures A and C could be found through a set of common terms B extracted from A and C. In this paper, we report an application of an Inductive Logic Programming (ILP), specifically the WARMR algorithm, to the field of literature-based discovery. The application extends Swanson's closed discovery model to uncover potentially meaningful knowledge in forms of relational frequent patterns that may exist after the connections between the two sets of literatures are found. We conducted an experiment between two pairs of topics: Raynaud's disease and fish oils, and Down syndrome and cell polarity. The experimental results demonstrate that our method can be used to enhance a literature-based discovery approach by providing potentially meaningful knowledge in addition to the indirect connections.
AB - Literature-based discovery for hypothesis generation is a subarea of text mining that aims to discover novel or previously-unknown knowledge from two complementary but disjoint (CBD) sets of literatures. The discovery approach is based on Swanson's discovery models where indirect connections between two disjoint sets of literatures A and C could be found through a set of common terms B extracted from A and C. In this paper, we report an application of an Inductive Logic Programming (ILP), specifically the WARMR algorithm, to the field of literature-based discovery. The application extends Swanson's closed discovery model to uncover potentially meaningful knowledge in forms of relational frequent patterns that may exist after the connections between the two sets of literatures are found. We conducted an experiment between two pairs of topics: Raynaud's disease and fish oils, and Down syndrome and cell polarity. The experimental results demonstrate that our method can be used to enhance a literature-based discovery approach by providing potentially meaningful knowledge in addition to the indirect connections.
UR - https://www.scopus.com/pages/publications/67650463009
U2 - 10.1109/CIDM.2009.4938661
DO - 10.1109/CIDM.2009.4938661
M3 - Conference contribution
AN - SCOPUS:67650463009
SN - 9781424427659
T3 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
SP - 283
EP - 290
BT - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
PB - IEEE Computer Society
T2 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, Part of the 2009 IEEE Symposium Series on Computational Intelligence, SSCI 2009
Y2 - 30 March 2009 through 2 April 2009
ER -