scholarly journals A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure

2021 ◽  
Author(s):  
Julião Braga ◽  
Francisco Regateiro ◽  
Joaquim L. R. Dias ◽  
Itana Stiubiener
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Intelligent Agents ◽  
Autonomous Systems ◽  
Implicit Knowledge ◽  
Domain Ontology ◽  
The Internet ◽  
Internet Infrastructure ◽  
Learning And Collaboration

This paper describes the creation of a domain ontology to represent knowledge to populate a knowledge base to be used by agents, in the environment of Internet Infrastructure routing domains. Protégé 5 was used, which produces results suitable for both software-developed agents and humans. The knowledge created with Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are presented and the ontology is made available for public use.The content produced with Protégé 5 will be used to populate the knowledge base of the Structure for Knowledge Acquisition, Use, Learning and Collaboration (SKAU), an environment to support intelligent agents over Internet Autonomous Systems domains.

scholarly journals On Intelligent, Autonomous and Collaborative Agents to Manage Internet Routing Domains

2019 ◽  
Author(s):  
Julião Braga ◽  
Joao Silva ◽  
Patricia Endo ◽  
Nizam Omar
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Autonomous System ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Use Case ◽  
Test Bed ◽  
Internet Routing ◽  
Internet Infrastructure ◽  
Unstructured Databases

This article describes an environment for knowledge acquisition, learning, use and collaboration inter agents over Internet Infrastructure. Four agent types are used in a previously applied fourtier model, such as the use case on the Internet Routing Registry. This model, which can be implemented in each Autonomous System domain of the Internet infrastructure, is integrated into an environment with (a) capturing information from unstructured databases, (b) creating and updating training bases appropriate to machine learning algorithms and (c) creation and feeding of a knowledge base. Such resources become readily available to agents in each domain and to agents in all other domains with the aim of making them autonomous. The agents collaborate and interact with each other, through individual blockchain structures that also take care of operational security and integration aspects. In addition, a test bed to validate the entire model, including the functionalities of the agents, is also proposed and characterized.

On a Novel Cognitive Knowledge Base (CKB) for Cognitive Robots and Machine Learning

2014 ◽  
Vol 6 (2) ◽  
pp. 41-62 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Cognitive Learning ◽  
Knowledge Bases ◽  
Formal Concept ◽  
Human Knowledge ◽  
Cognitive Knowledge ◽  
Cognitive Robots ◽  
Concept Network

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.

Capturing Evolutional Knowledge Using Time Interval Tracing

2007 ◽  
Vol 11 (4) ◽  
pp. 373-380
Author(s):  
Shun-Chieh Lin ◽  
◽  
Chia-Wen Teng ◽  
Shian-Shyong Tseng ◽  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Time Domain ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Time Interval ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Over Time

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

scholarly journals False Intel Detection In Crowd Source Knowledge Base

2021 ◽  
Vol 10 (3) ◽  
pp. 2158-2164
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Knowledge Base ◽  
Large Scale ◽  
Learning Task ◽  
The Internet ◽  
Context Information ◽  
New Machine

Wikidata is widely considered as the biggest Encyclopaedia on the internet and it is the new large-scale knowledge base of the WikimediaFoundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems imposing high demands on its integrity. Wikidata, it can be edited by anyone and as a result, unfortunately it frequently gets vandalized exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper a new machine learning based approach to detect vandalism in wikidata is presented. We propose sector 47 features that exploit both content and context information and we report on 4 classifiers as of increasing effectiveness tailored to this learning task.

The Surfing Coaching: Sources of Knowledge Acquisition

2018 ◽  
Vol 5 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Marco Catarino Espada Estêvão Correia ◽  
Rachael Bertram
Keyword(s):  
Higher Education ◽  
Physical Education ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
The Internet ◽  
Additional Information ◽  
Sources Of Knowledge ◽  
The Subject ◽  
Use Of The Internet

A career as a surfing coach is a relatively recent profession, and has not yet been the subject of extensive research. The aim of the present study was to investigate the specific sources of knowledge acquisition of surfing coaches. Individual semi-structured open-ended interviews were conducted with 11 expert surfing coaches. Results revealed that their knowledge acquisition was similar in many ways. Their formal higher education provided them with training in sport sciences and physical education pedagogy, as well as their athletes’ surfing experiences. Their knowledge base was further developed by acquiring additional information through surfing coaching courses, books, and the use of the Internet.

scholarly journals A set of data bases to support intelligent agents in Internet Infrastructure routing domains

2019 ◽  
Author(s):  
Julião Braga ◽  
Joao Silva ◽  
Nizam Omar
Keyword(s):  
Machine Learning ◽  
Programming Language ◽  
Intelligent Agents ◽  
Machine Learning Techniques ◽  
Web Page ◽  
Data Bases ◽  
Data Format ◽  
Internet Infrastructure ◽  
Learning Techniques ◽  
Restricted Domains

This paper presents a set of three data bases that make up the In- ternet Infrastructure Data Base (IIDB). IIDB has three data bases – iidb.rfc, iidb.person, and iidb.acronym – that are key pieces to support the development of machine learning techniques by the intelligent elements of the Autonomous Architecture Over Restricted Domains (A2RD). The data contained in iidb.rfc and iidb.person were created after processing the contents available at the RFC Index web page. While the data contained in the iidb.acronym was created after processing the contents of the files available at the Request for Comments (RFC) repository, produced and maintained by the RFC Editor. The data format of IIDB data is JavaScript Object Notation (JSON), whose templates are avail- able in the same site where the data bases are deposited, making them accessible through any programming language.

scholarly journals Ambiente para Aquisicao de Conhecimento por Agentes em Dominios Restritos na Infraestrutura da Internet

2020 ◽  
Author(s):  
Juliao Braga
Keyword(s):  
Machine Learning ◽  
Graduate Education ◽  
Autonomous System ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Use Case ◽  
Ministry Of Education ◽  
Internet Routing ◽  
Internet Infrastructure ◽  
Unstructured Databases

This project establishes an environment for knowledge acquisition, learning, use and collaboration inter-agents over Internet Infrastructure. Four agent types are used in a previously applied four-tier model (A2RD), such as the use case on the Internet Routing Registry. This model, which can be implemented in each Autonomous System domain of the Internet infrastructure, is integrated into an environment with (a) capturing information from unstructured databases, (b) creating and updating training bases appropriate to machine learning algorithms and (c) creating and feeding of a knowledge base. Such resources become readily available to agents in each domain and to agents in all other domains with the aim of making them autonomous. The agents collaborate and interact with each other, through individual blockchain structures that also take care of operational security and integration aspects. In addition, a testbed to validate the entire model, including the functionalities of the agents, is also proposed and characterized.Acnowledge: This work is supported by CAPES -- Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Brazil’s Ministry of Education, and is also supported by national funds through FCT with reference UID/CEC/50021/2019, and is supported by MackenziPesquisa from Universidade Presbiteriana Mackenzie..

scholarly journals Ensemble based Machine Learning using Ontology Information Extraction for Information Retrieval

2019 ◽  
Vol 9 (2) ◽  
pp. 3952-3959
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Language Processing ◽  
Data Retrieval ◽  
Extraction Process ◽  
Domain Ontology ◽  
Internet Technology ◽  
The Internet ◽  
Automatic Process ◽  
Selection Of

The movement of internet technology enhanced the speed and accuracy of data retrieval over the internet. The retrieval of data over the internet needs some automatic process of information extraction and query retrieval. The information extraction gives the process of the predefined structure of the concept to a particular domain of knowledge. The process of information extraction proceeds in two steps one is preprocessing of data and post-processing of data. In preprocessing of data used the concept of the glowworm optimization algorithm. The glowworm algorithm is a family of kits a gives the better selection of information in constraints of similarity. The selection of similarity based on the process of lubrification. The optimization of glowworm removed the unwanted noise of data and filtered it. For the extraction of information used ensemblebased information extraction. The ensemble-based information extraction proceeds with constraints function that function is called mapper constraints. The mapper constraints map the process of ontology with guided domain ontology. The ensemblebased information extraction process used the concept of machine learning for the binding of process. The goals of this work are the development of an OBIE for the domain of different fields of data retrieval such as news agencies, hotel industries and sports. The proposed model combines with the use of ontology, POS and language processing tools and constraintsbased mapper with domain ontology.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

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