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Outline

Title
Abstract
Background and Motivation
Background in Multi-Relational Data Mining
Background in Multi-Instance Learning
Worst-Case Execution Time Analysis
Static Analysis
Measurement-Based Analysis
Hybrid Analysis
Suitability of Ilp and Related Methods
Limitations of the Naive Loop Bound Learner
Related Work -Ilp for Branch Prediction
Datasets
Systems and Methodology
Experimental Results
Experimental Methodology
Experimental Setting
Conclusions and Outlook
Slotchain Data (Synthetic)
Randomised Local Search Procedure
Materials
Method
Results and Discussion
Ontologies and Relational Data
Testing the Hypothesis Coverage of Observations
Structuring the Hypothesis Space
Searching the Hypothesis Space
Disjunctive Datalog
Hypothesis
Experiment Design
Data Sets Dataset 1 -Whole Set
Dataset 2 -Training Data Grouped by Length
Background Knowledge (BK)
Dataset NPP-Middles
Dataset NPP-Middles-(Short,Medium,Long)
Analysis of Induced Grammars
Background
Conclusion and Future Work
Experimental Setup
Conclusions and Future Work
Representing Structured Data
Relational Joins for Structured Data
Exact Relational Join for Structured Data
Approximate Relational Join for Structured Data
First Order Logic Background
Experimental Evaluation
Evaluation Measures and Protocol
Chess Domain Overview
Results
Discussion
Related Work
Future Work
Related Work and Discussion
Conclusions
Introduction and Context
Conclusion
Introduction
References

DL-FOIL concept learning in description logics

Profile image of Floriana EspositoFloriana EspositoProfile image of Esposito FlorianaEsposito FlorianaProfile image of Claudia d'AmatoClaudia d'Amato

2008

Abstract

In this paper we focus on learning concept descriptions expressed in Description Logics. After stating the learning problem in this context, a FOIL-like algorithm is presented that can be applied to general DL languages, discussing related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. Subsequently we present an experimental evaluation of the implementation of this algorithm performed on some real ontologies in order to empirically assess its performance.

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A bisimulation-based method of concept learning for knowledge bases in description logics
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Exact Learning of Lightweight Description Logic Ontologies
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We investigate the complexity of learning description logic ontologies in Angluin et al.'s framework of exact learning via queries posed to an oracle. We consider membership queries of the form "is individual a a certain answer to a data retrieval query q in a given ABox and the unkown target TBox?" and equivalence queries of the form "is a given TBox equivalent to the unknown target TBox?".We show that (i) DL-Lite TBoxes with role inclusions and ELI concept expressions on the right-hand side of inclusions and (ii) EL TBoxes without complex concept expressions on the right-hand side of inclusions can be learned in polynomial time. Both results are proved by a non-trivial reduction to learning from subsumption examples.We also show that arbitrary EL TBoxes cannot be learned in polynomial time.

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Explicitly Using Default Knowledge in Concept Learning: An Extended Description Logics Plus Strict and Default Rules Veronique Vent os
henry soldano

2003

This work concerns the use of default knowledge in concept learning from positive and negative examples. Two connectives are added to a description logics, C-CLASSIC, previously defined for concept learning. The new connectives (δ and) allow to express the idea that some properties of a given concept definition are default properties, and that some properties that should belong to the concept definition actually do not (these are excepted properties). When performing concept learning both hypotheses and examples are expressed in this new description logics but prior to learning, a saturation process using default and non default rules has to be applied to the examples in order to add default and excepted properties to their definition. As in the original C-CLASSIC, disjunctive learning is performed using a standard greedy set covering algorithm whose generalization operator is the Least Common Subsumer operator of C-CLASSIC δ. We exemplify concept learning using default knowledge in this framework and show that explicitly expressing default knowledge may result in simpler concept definitions. 1 Note that comparing the expressive power of DLs and restrictions of First Order Logic used in ILP is still an open issue [7].

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