TY - JOUR
T1 - Heuristics for automated knowledge source integration and service composition
AU - Bless, Patrick N.
AU - Klabjan, Diego
AU - Chang, Soo Y.
N1 - Funding Information:
The funding for this project is provided by the National Science Foundation under Grant NSF DMI-00-04226. We are also extremely thankful to an anonymous referee for providing constructive comments, which lead to a substantially improved manuscript.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008/4
Y1 - 2008/4
N2 - The NP-hard component set identification problem is a combinatorial problem arising in the context of knowledge discovery, information integration, and knowledge source/service composition. Considering a granular knowledge domain consisting of a large number of individual bits and pieces of domain knowledge (properties) and a large number of knowledge sources and services that provide mappings between sets of properties, the objective of the component set identification problem is to select a minimum cost combination of knowledge sources that can provide a joint mapping from a given set of initially available properties (initial knowledge) to a set of initially unknown properties (target knowledge). We provide a general framework for heuristics and consider construction heuristics that are followed by local improvement heuristics. Computational results are reported on randomly generated problem instances.
AB - The NP-hard component set identification problem is a combinatorial problem arising in the context of knowledge discovery, information integration, and knowledge source/service composition. Considering a granular knowledge domain consisting of a large number of individual bits and pieces of domain knowledge (properties) and a large number of knowledge sources and services that provide mappings between sets of properties, the objective of the component set identification problem is to select a minimum cost combination of knowledge sources that can provide a joint mapping from a given set of initially available properties (initial knowledge) to a set of initially unknown properties (target knowledge). We provide a general framework for heuristics and consider construction heuristics that are followed by local improvement heuristics. Computational results are reported on randomly generated problem instances.
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U2 - 10.1016/j.cor.2006.08.012
DO - 10.1016/j.cor.2006.08.012
M3 - Article
AN - SCOPUS:34548642199
SN - 0305-0548
VL - 35
SP - 1292
EP - 1314
JO - Computers and Operations Research
JF - Computers and Operations Research
IS - 4
ER -