Ontologies and E-Learning

2005 ◽  
pp. 345-356
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontologies and E-Learning

2011 ◽  
pp. 322-331 ◽  
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

scholarly journals A New Hypred Improved Method for Measuring Concept Semantic Similarity in WordNet

2019 ◽  
Vol 17 (4) ◽  
pp. 433-439
Author(s):  
Xiaogang Zhang ◽  
Shouqian Sun ◽  
Kejun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Semantic Similarity ◽  
Language Processing ◽  
Similarity Measures ◽  
Improved Method ◽  
Hierarchical Tree ◽  
Semantic Computation

Computing semantic similarity between concepts is an important issue in natural language processing, artificial intelligence, information retrieval and knowledge management. The measure of computing concept similarity is a fundament of semantic computation. In this paper, we analyze typical semantic similarity measures and note Wu and Palmer’s measure which does not distinguish the similarities between nodes from a node to different nodes of the same level. Then, we synthesize the advantages of measure of path-based and IC-based, and propose a new hybrid method for measuring semantic similarity. By testing on a fragment of WordNet hierarchical tree, the results demonstrate the proposed method accurately distinguishes the similarities between nodes from a node to different nodes of the same level and overcome the shortcoming of the Wu and Palmer’s measure

scholarly journals Ontología y Procesamiento de Lenguaje Natural

2018 ◽  
Vol 3 (1) ◽  
pp. 492
Author(s):  
Denis Cedeño Moreno ◽  
Miguel Vargas Lombardo
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Management Model ◽  
Ontological Engineering ◽  
Design And Implementation ◽  
The World

At present, the convergence of several areas of knowledge has led to the design and implementation of ICT systems that support the integration of heterogeneous tools, such as artificial intelligence (AI), statistics and databases (BD), among others. Ontologies in computing are included in the world of AI and refer to formal representations of an area of knowledge or domain. The discipline that is in charge of the study and construction of tools to accelerate the process of creation of ontologies from the natural language is the ontological engineering. In this paper, we propose a knowledge management model based on the clinical histories of patients (HC) in Panama, based on information extraction (EI), natural language processing (PLN) and the development of a domain ontology.Keywords: Knowledge, information extraction, ontology, automatic population of ontologies, natural language processing.

scholarly journals Introduction to the Integrated Domain Modeling Toolset

2014 ◽  
Vol 16 (1) ◽  
pp. 13-18
Author(s):  
Armands Slihte ◽  
Juan Manuel Cueva Lovelle
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
System Analysis ◽  
Knowledge Engineering ◽  
Domain Modeling ◽  
Model Driven ◽  
Modeling Task

Abstract This paper describes the Integrated Domain Modeling approach and introduces the supporting toolset as a solution to the complex domain-modeling task. This approach integrates artificial intelligence (AI) and system analysis by exploiting ontology, natural language processing (NLP), use cases and model-driven architecture (MDA) for knowledge engineering and domain modeling. The IDM toolset provides the opportunity to automatically generate the initial AS-IS model from the formally defined domain knowledge. In this paper, we describe in detail the scope, architecture and implementation of the toolset.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

On the Generation of E-Learning Resources Using Business Process, Natural Language Processing, and Web Services

2021 ◽  
Vol 23 (2) ◽  
pp. 40-44
Author(s):  
Olivia Fragoso-Diaz ◽  
Vitervo Lopez Caballero ◽  
Juan Carlos Rojas-Perez ◽  
Rene Santaolaya-Salgado ◽  
Juan Gabriel Gonzalez-Serna
Keyword(s):  
Natural Language Processing ◽  
Web Services ◽  
Natural Language ◽  
Language Processing ◽  
Business Process ◽  
Learning Resources ◽  
E Learning

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

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