Policy-based memoization for ILP-based concept discovery systems

Date

2016-02-01

Author

Mutlu, Alev
Karagöz, Pınar

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Inductive Programming Logic (ILP)-based concept discovery systems aim to find patterns that describe a target relation in terms of other relations provided as background knowledge. Such systems usually work within first order logic framework, build large search spaces, and have long running times. Memoization has widely been incorporated in concept discovery systems to improve their running times. One of the problems that memoization brings to such systems is the memory overhead which may be a bottleneck. In this work we propose policies that decide what types of concept descriptors to store in memotables and for how long to keep them. The proposed policies have been implemented as extensions to a concept discovery system called Tabular CRIS wEF, and the resulting system is named Policy-based Tabular CRIS. Effects of the proposed policies are evaluated on several datasets. The experimental results show that the proposed policies greatly improve the memory consumption while preserving the benefits introduced by memoization.

Subject Keywords

Multi-relational data mining, Inductive logic programming, Concept discovery, Memoization, Memory consumption, Scalability

URI

https://hdl.handle.net/11511/33317

Journal

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

DOI

https://doi.org/10.1007/s10844-015-0354-7

Collections

Department of Computer Engineering, Article

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Citation Formats

A. Mutlu and P. Karagöz, “Policy-based memoization for ILP-based concept discovery systems,” JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, pp. 99–120, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33317.