Semantic Knowledge Base: Quantifiers and Multiplicity in Extended Semantic Networks Module

AbstractThe process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

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Introduction to Semantic Knowledge Base: Multilanguage Support of Linguistic Module

2016 Third European Network Intelligence Conference (ENIC) ◽

10.1109/enic.2016.035 ◽

2016 ◽

Cited By ~ 2

Author(s):

Marek Krotkiewicz ◽

Marcin Jodlowiec ◽

Krystian Wojtkiewicz

Keyword(s):

Knowledge Base ◽

Semantic Knowledge ◽

Semantic Knowledge Base

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Semantic Knowledge Base in Support of Activity Recognition in Smart Home Environments

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.27.22482 ◽

2018 ◽

Vol 7 (4.27) ◽

pp. 67

Author(s):

Abdul Syafiq Abdull Sukor ◽

Ammar Zakaria ◽

Norasmadi Abdul Rahim ◽

Rossi Setchi

Keyword(s):

Knowledge Base ◽

Activity Recognition ◽

Smart Home ◽

Semantic Knowledge ◽

Experimental Results ◽

Semantic Knowledge Base ◽

Main Components ◽

Use Of Knowledge ◽

Smart Home Environments ◽

Home Systems

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify activity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main components: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%.

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A Semantic Framework for Cloud Learning Environments

Cloud Computing for Teaching and Learning ◽

10.4018/978-1-4666-0957-0.ch002 ◽

2012 ◽

pp. 17-31 ◽

Cited By ~ 11

Author(s):

Alexander Mikroyannidis

Keyword(s):

Knowledge Base ◽

Learning Environments ◽

Collaborative Study ◽

Semantic Knowledge ◽

Open Educational Resources ◽

Educational Services ◽

Traditional Learning ◽

Semantic Framework ◽

Management Techniques ◽

Semantic Knowledge Base

Cloud Learning Environments (CLEs) are gradually gaining ground over traditional Learning Management Systems (LMS) by facilitating the lone or collaborative study of user-chosen blends of content and courses from heterogeneous sources, including Open Educational Resources (OER). This chapter describes the use of ontologies for modelling various aspects of the learning process within such an environment. In particular, the author considers a semantic knowledge base as the core of the learning environment, facilitating learners in finding educational services on the cloud. He describes how different stakeholder clusters are involved in the creation and maintenance of this knowledge base, through collaborative ontology management techniques. Finally, the chapter defines the mechanisms for the evolution of this knowledge base and the constant updating of the associated cloud learning services.

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