Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A Counting-Based Heuristic for ILP-Based Concept Discovery Systems
Date
2013-09-13
Author
Mutlu, Alev
Karagöz, Pınar
Kavurucu, Yusuf
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
204
views
0
downloads
Cite This
Concept discovery systems are concerned with learning definitions of a specific relation in terms of other relations provided as background knowledge. Although such systems have a history of more than 20 years and successful applications in various domains, they are still vulnerable to scalability and efficiency issues - mainly due to large search spaces they build. In this study we propose a heuristic to select a target instance that will lead to smaller search space without sacrificing the accuracy. The proposed heuristic is based on counting the occurrences of constants in the target relation. To evaluate the heuristic, it is implemented as an extension to the concept discovery system called (CD)-D-2. The experimental results show that the modified version of (CD)-D-2 builds smaller search space and performs better in terms of running time without any decrease in coverage in comparison to the one without extension.
Subject Keywords
Inductive logic programming
,
Concept discovery
,
Search space
,
Counting
URI
https://hdl.handle.net/11511/54738
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A Graph-Based Concept Discovery Method for n-Ary Relations
Abay, Nazmiye Ceren; MUTLU, ALEV; Karagöz, Pınar (2015-09-04)
Concept discovery is a multi-relational data mining task for inducing definitions of a specific relation in terms of other relations in the data set. Such learning tasks usually have to deal with large search spaces and hence have efficiency and scalability issues. In this paper, we present a hybrid approach that combines association rule mining methods and graph-based approaches to cope with these issues. The proposed method inputs the data in relational format, converts it into a graph representation, and...
Policy-based memoization for ILP-based concept discovery systems
Mutlu, Alev; Karagöz, Pınar (2016-02-01)
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. I...
Improving scalability and efficiency of ILP-based and graph-based concept discovery systems
Mutlu, Alev; Karagöz, Pınar; Department of Computer Engineering (2013)
Concept discovery is the problem of finding definitions of target relation in terms or other relation given as a background knowledge. Inductive Logic Programming (ILP)-based and graph-based approaches are two competitors in concept discovery problem. Although ILP-based systems have long dominated the area, graph-based systems have recently gained popularity as they overcome certain shortcomings of ILP-based systems. While having applications in numerous domains, ILP-based concept discovery systems still su...
A study on the relation between logic and information
Beygu, Tankut; Grünberg, David; Department of Philosophy (2003)
Gaining prominence at first as a technological concept, information has found a significant role in the study of various problems in philosophy as well as in the diverse fields of science and technology.Though widely employed and studied, information is a complex notion, as it were, resisting a compact description of its characteristics, appearing non-uniformly in such miscellaneous ways as an organizing principle, a form of knowledge, and a connotation of entropy. Apparently, there is a need for further st...
A statistical unified framework for rank-based multiple classifier decision combination
Saranlı, Afşar (2001-04-01)
This study presents a theoretical investigation of the rank-based multiple classifier decision combination problem, with the aim of providing a unified framework to understand a variety of such systems. The combination of the decisions of more than one classifiers with the aim of improving overall system performance is a concept of general interest in pattern recognition, as a viable alternative to designing a single sophisticated classifier. The problem of combining the classifier decisions in the raw form...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Mutlu, P. Karagöz, and Y. Kavurucu, “A Counting-Based Heuristic for ILP-Based Concept Discovery Systems,” 2013, vol. 8073, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54738.