Policy-based memoization for ILP-based concept discovery systems

Mutlu, Alev
Karagöz, Pınar
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.


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 Counting-Based Heuristic for ILP-Based Concept Discovery Systems
Mutlu, Alev; Karagöz, Pınar; Kavurucu, Yusuf (2013-09-13)
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 p...
Improving Hit Ratio of ILP-based Concept Discovery System with Memoization
Mutlu, Alev; Karagöz, Pınar (2014-01-01)
Although Inductive Logic Programming (ILP)-based concept discovery systems have applications in a wide range of domains, they still suffer from scalability and efficiency issues. One of the reasons for the efficiency problem is the high number of query executions necessary in the concept discovery process. Owing to the refinement operator of ILP-based concept discovery systems, these queries repeat frequently. In this work, we propose a method to improve the look-up table hit ratio for repeating queries of ...
Time Constrained Temporal Logic Control of Multi Affine Systems
Aydın Göl, Ebru (2012-01-01)
In this paper, we consider the problem of controlling a dynamical system such that its trajectories satisfy a temporal logic property in a given amount of time. We focus on multiaffine systems and specifications given as syntactically co-safe linear temporal logic formulas over rectangular regions in the state space. The proposed algorithm is based on the estimation of time bounds for facet reachability problems and solving a time optimal reachability problem on the product between a weighted transition sys...
Deterministic modeling and inference of biological systems
Seçilmiş, Deniz; Purutçuoğlu Gazi, Vilda; Department of Bioinformatics (2017)
The mathematical description of biological networks can be performed mainly by stochastic and deterministic models. The former gives more information about the system, whereas, it needs very detailed measurements. On the other hand, the latter is relatively less informative, but, the collection of their data is easier than the stochastic ones, rendering it a more preferable modeling approach. In this study, we implement the deterministic modeling of biological systems due to the underlying advantage. Among ...
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.