NDLI: Multi-Relational Data Mining Based on Higher-Order Inductive Logic Programming

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang
Copyright Year	2009
Abstract	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.
Starting Page	453
Ending Page	458
File Size	293060
Page Count	6
File Format	PDF
ISBN	9780769535715
DOI	10.1109/GCIS.2009.289
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Logic programming Law Scalability Relational databases Machine learning multi-relational data mining Workstations Data mining Intelligent systems Legal factors higher-order inductive logic programming
Content Type	Text
Resource Type	Article

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