Concept Induction in Description Logics Using Information-Theoretic Heuristics

Semantic Web ◽  
2013 ◽  
pp. 97-118
Author(s):  
Nicola Fanizzi
Keyword(s):  
Semantic Web ◽  
Experimental Evaluation ◽  
Learning Algorithm ◽  
Description Logics ◽  
Learning Problem ◽  
Information Theoretic ◽  
Concept Induction ◽  
Formal Ontologies ◽  
Refinement Operators ◽  
Theoretical Foundations

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.

Concept Induction in Description Logics Using Information-Theoretic Heuristics

2011 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Nicola Fanizzi
Keyword(s):  
Semantic Web ◽  
Experimental Evaluation ◽  
Learning Algorithm ◽  
Description Logics ◽  
Learning Problem ◽  
Information Theoretic ◽  
Concept Induction ◽  
Formal Ontologies ◽  
Refinement Operators ◽  
Theoretical Foundations

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Chip Attach Scheduling in Semiconductor Assembly

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Kaishun Hu ◽  
Shuai Li ◽  
Huiyu Huang ◽  
Shaoyong Zhao
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Learning Algorithm ◽  
Parallel Machine Scheduling ◽  
Volume Index ◽  
Production Volume ◽  
Learning Problem ◽  
Reward Function ◽  
Unrelated Parallel Machine Scheduling ◽  
Target Production

Chip attach is the bottleneck operation in semiconductor assembly. Chip attach scheduling is in nature unrelated parallel machine scheduling considering practical issues, for example, machine-job qualification, sequence-dependant setup times, initial machine status, and engineering time. The major scheduling objective is to minimize the total weighted unsatisfied Target Production Volume in the schedule horizon. To apply Q-learning algorithm, the scheduling problem is converted into reinforcement learning problem by constructing elaborate system state representation, actions, and reward function. We select five heuristics as actions and prove the equivalence of reward function and the scheduling objective function. We also conduct experiments with industrial datasets to compare the Q-learning algorithm, five action heuristics, and Largest Weight First (LWF) heuristics used in industry. Experiment results show that Q-learning is remarkably superior to the six heuristics. Compared with LWF, Q-learning reduces three performance measures, objective function value, unsatisfied Target Production Volume index, and unsatisfied job type index, by considerable amounts of 80.92%, 52.20%, and 31.81%, respectively.

scholarly journals Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mohamed Idhammad ◽  
Karim Afdel ◽  
Mustapha Belouch
Keyword(s):  
Random Forest ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Detection System ◽  
Cloud Services ◽  
Detection Accuracy ◽  
Cloud Environment ◽  
Ddos Attacks ◽  
Information Theoretic ◽  
Computing Services

Cloud Computing services are often delivered through HTTP protocol. This facilitates access to services and reduces costs for both providers and end-users. However, this increases the vulnerabilities of the Cloud services face to HTTP DDoS attacks. HTTP request methods are often used to address web servers’ vulnerabilities and create multiple scenarios of HTTP DDoS attack such as Low and Slow or Flooding attacks. Existing HTTP DDoS detection systems are challenged by the big amounts of network traffic generated by these attacks, low detection accuracy, and high false positive rates. In this paper we present a detection system of HTTP DDoS attacks in a Cloud environment based on Information Theoretic Entropy and Random Forest ensemble learning algorithm. A time-based sliding window algorithm is used to estimate the entropy of the network header features of the incoming network traffic. When the estimated entropy exceeds its normal range the preprocessing and the classification tasks are triggered. To assess the proposed approach various experiments were performed on the CIDDS-001 public dataset. The proposed approach achieves satisfactory results with an accuracy of 99.54%, a FPR of 0.4%, and a running time of 18.5s.

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

A Review of Fuzzy Models for the Semantic Web

2011 ◽  
pp. 63-77
Author(s):  
Hailong Wang ◽  
Zongmin Ma ◽  
Li Yan ◽  
Jingwei Cheng
Keyword(s):  
Computational Complexity ◽  
Semantic Web ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Description Logics ◽  
Expressive Power ◽  
Semantic Knowledge ◽  
Context Information ◽  
Semantic Knowledge Base

In the Semantic Web context, information would be retrieved, processed, shared, reused and aligned in the maximum automatic way possible. Our experience with such applications in the Semantic Web has shown that these are rarely a matter of true or false but rather procedures that require degrees of relatedness, similarity, or ranking. Apart from the wealth of applications that are inherently imprecise, information itself is many times imprecise or vague. In order to be able to represent and reason with such type of information in the Semantic Web, different general approaches for extending semantic web languages with the ability to represent imprecision and uncertainty has been explored. In this chapter, we focus our attention on fuzzy extension approaches which are based on fuzzy set theory. We review the existing proposals for extending the theoretical counterpart of the semantic web languages, description logics (DLs), and the languages themselves. The following statements will include the expressive power of the fuzzy DLs formalism and its syntax and semantic, knowledge base, the decidability of the tableaux algorithm and its computational complexity etc. Also the fuzzy extension to OWL is discussed in this chapter.

Integrated Semantic-Based Composition of Skills and Learning Needs in Knowledge-Intensive Organisations

2007 ◽  
pp. 266-298
Author(s):  
Simona Colucci ◽  
Tommaso Di Noia ◽  
Eugenio Di Sciascio ◽  
Francesco Maria Donini ◽  
Azzurra Ragone
Keyword(s):  
Semantic Web ◽  
Competitive Advantage ◽  
Management System ◽  
Description Logics ◽  
Learning Needs ◽  
Learning Modules ◽  
Current Availability ◽  
E Learning ◽  
Knowledge Intensive ◽  
Skill Management

Holding and creating competences is one of the most strategic activities in organizations, especially in knowledge intensive ones. An organization dealing with a task to perform will firstly check for the required skills among the available personnel. If such a search process leads to discover lacking competencies, organizations may hire external personnel or encourage internal personnel to learn new competencies on the unavailable skills. The current availability of several, well-organized, e-learning modules, makes such possibility appealing and enonomically advantageous. A skill management system performing both the processes of searching among available skills and facilitating the creation of missing ones can hence be a noteworthy source of competitive advantage for a knowledge-intensive organization. We present here an approach and a system for such purpose, which exploits recent advances in semantic-based inference services and technologies.The proposed approach employs Description Logics formalism and reasoning services and is fully in the Semantic Web initiative mainstream.

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