Skip to Main Content
This paper presents a novel multi-relational data mining (MRDM) approach from a perspective of considering higher-order inductive logic programming to dealing with the representation formalism problems of existing multi-relational data mining approaches. In our approach, examples, background knowledge,hypotheses and target concepts are represented in Escher, a higher-order logic programming language.Escher can describe semantically complicated data and patterns, and explicitly supports a variety of data types, including graph. Moreover, our approach explores and exploits the techniques of HILP based on Escher to efficiently construct search space and proposal a novel methodology of MRDM.Furthermore, we present an architecture for efficiency and scalability of MRDM based on HILP. We believe that our approach based on higher-order inductive logic programming will has a key role to play in the growth of MRDM while several major call for algorithms that explicitly exploit the semantically complicated and topological substructures of data.
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on (Volume:2 )
Date of Conference: 19-21 May 2009