Semantic translation for rule-based knowledge in data mining

Authors:
Dejing Dou;Han Qin;Haishan Liu
Affiliations:
Computer and Information Science Department, University of Oregon, Eugene, Oregon;Computer and Information Science Department, University of Oregon, Eugene, Oregon;Computer and Information Science Department, University of Oregon, Eugene, Oregon
Venue:
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Year:
2011

Citing 12
Cited 0

Inductive Policy: The Pragmatics of Bias Selection

Machine Learning - Special issue on bias evaluation and selection
The Clio project: managing heterogeneity

ACM SIGMOD Record
Deriving axioms across ontologies

AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge transfer via multiple model local structure mapping

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-resolution learning for knowledge transfer

AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Text categorization with knowledge transfer from heterogeneous data sources

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Discovering executable semantic mappings between ontologies

OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
Markov Logic: An Interface Layer for Artificial Intelligence

Markov Logic: An Interface Layer for Artificial Intelligence
Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous, distributed information sources

ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Towards theory translation

DALT'06 Proceedings of the 4th international conference on Declarative Agent Languages and Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

Considering data size and privacy concerns in a distributed setting, it is neither desirable nor feasible to translate data from one resource to another in data mining. Rather, it makes more sense to first mine knowledge from one data resource and then translate the discovered knowledge (models) to another for knowledge reuse. Although there have been successful research efforts in knowledge transfer, the knowledge translation problem in the semantically heterogenous scenario has not been addressed adequately. In this paper, we first propose to use Semantic Web ontologies to represent rule-based knowledge to make the knowledge computer "translatable". Instead of an inductive learning approach, we treat knowledge translation as a deductive inference. We elaborate a translation method with both the forward and backward chaining to address the asymmetry of translation. We show the effectiveness of our knowledge translation method in decision tree rules and association rules mined from sports and gene data respectively. In a more general context, this work illustrates the promise of a novel research which leverages ontologies and Semantic Web techniques to extend the knowledge transfer in data mining to the semantically heterogeneous scenario.