Discovery of Lexical Entries for Non-taxonomic Relations in Ontology Learning

2004 ◽  
pp. 249-256 ◽  
Author(s):  
Martin Kavalec ◽  
Alexander Maedche ◽  
Vojtěch Svátek
Keyword(s):  
Ontology Learning ◽  
Taxonomic Relations

Learning Non-Taxonomic Relations of Ontologies

2021 ◽  
Vol 17 (1) ◽  
pp. 97-122
Author(s):  
Mohamed Hassan Mohamed Ali ◽  
Said Fathalla ◽  
Mohamed Kholief ◽  
Yasser Fouad Hassan
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Research Work ◽  
Semantic Knowledge ◽  
Future Research ◽  
Automatic Extraction ◽  
Ontology Learning ◽  
Comprehensive Understanding ◽  
Specific Domain ◽  
Taxonomic Relations

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

scholarly journals Ontology Learning Part One — on Discovering Taxonomic Relations from the Web

2003 ◽  
pp. 301-319 ◽  
Author(s):  
Alexander Maedche ◽  
Viktor Pekar ◽  
Steffen Staab
Keyword(s):  
Ontology Learning ◽  
Taxonomic Relations ◽  
The Web

scholarly journals A modular framework for ontology learning from text in Portuguese

2020 ◽  
Vol 3 (3) ◽  
pp. 37-42
Author(s):  
Norton Coelho Guimarães ◽  
Cedric Luiz De Carvalho
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Ontology Learning ◽  
Computational Framework ◽  
Learning From Text ◽  
Learning From Texts ◽  
Processing Techniques ◽  
Taxonomic Relations

Research on ontology learning has been carried out in many knowledge areas, especially in Artificial Intelligence. Semi-automatic or automatic ontology learning can contribute to the field of knowledge representation. Many semi-automatic approaches to ontology learning from texts have been proposed. Most of these proposals use natural language processing techniques. This paper describes a computational framework construction for semi-automated ontology learning from texts in Portuguese. Axioms are not treated in this paper. The work described here originated from the Philipp Cimiano’s proposal along with text standardization mechanisms, natural language processing, identification of taxonomic relations and techniques for structuring ontologies. In this work, a case study on public security domain was also done, showing the benefits of the developed computational framework. The result of this case study is an ontology for this area.

Deaf Adolescents’ Representations of Taxonomic Relations

2009 ◽  
Vol 40 (11) ◽  
pp. 1178-1189 ◽  
Author(s):  
Ji-Jia ZHANG ◽  
De-Gao LI ◽  
Xue-Yun WU
Keyword(s):  
Taxonomic Relations

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment

2011 ◽  
Vol 23 (11) ◽  
pp. 1635-1648 ◽  
Author(s):  
Elias Zavitsanos ◽  
George Paliouras ◽  
George A. Vouros
Keyword(s):  
Gold Standard ◽  
Ontology Learning ◽  
Learning Methods
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