A log ontology learning approach to discover non-taxonomic relationships between events

2015 ◽  
pp. 457-460
Keyword(s):  
Learning Approach ◽  
Ontology Learning ◽  
Taxonomic Relationships

Supporting the discovery and labeling of non-taxonomic relationships in ontology learning

2009 ◽  
Vol 36 (7) ◽  
pp. 10288-10294 ◽  
Author(s):  
J. Villaverde ◽  
A. Persson ◽  
D. Godoy ◽  
A. Amandi
Keyword(s):  
Ontology Learning ◽  
Taxonomic Relationships

scholarly journals REVIEWING THE PROBLEM OF LEARNING NON-TAXONOMIC RELATIONSHIPS OF ONTOLOGIES FROM TEXT

2012 ◽  
Vol 06 (04) ◽  
pp. 491-507 ◽  
Author(s):  
IVO SERRA ◽  
ROSARIO GIRARDI ◽  
PAULO NOVAIS
Keyword(s):  
State Of The Art ◽  
The State ◽  
Ontology Learning ◽  
Taxonomic Relationships ◽  
Generic Process

Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. This article discusses the problem of Learning Non-Taxonomic Relationships of ontologies and proposes a generic process for approaching it. Some techniques representing the state of the art of this field are discussed along with their advantages and limitations. Finally, a framework for Learning Non-Taxonomic Relationships being developed by the authors is briefly discussed. This framework intends to be a customizable solution to reach good effectiveness in the process of extraction of non-taxonomic relationships according to the characteristics of the corpus.

scholarly journals Ontology learning algorithm using weak functions

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 910-916 ◽  
Author(s):  
Linli Zhu ◽  
Gang Hua ◽  
Adnan Aslam
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Learning Algorithm ◽  
Point Of View ◽  
Learning Approach ◽  
Theoretical Point ◽  
Ontology Learning ◽  
Machine Learning Approach ◽  
Similarity Calculation ◽  
Weak Ontology

AbstractOntology is widely used in information retrieval, image processing and other various disciplines. This article discusses how to use machine learning approach to solve the most essential similarity calculation problem in multi-dividing ontology setting. The ontology function is regarded as a combination of several weak ontology functions, and the optimal ontology function is obtained by an iterative algorithm. In addition, the performance of the algorithm is analyzed from a theoretical point of view by statistical methods, and several results are obtained.

SEMCON

2016 ◽  
Vol 12 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Zenun Kastrati ◽  
Ali Shariq Imran ◽  
Sule Yildirim-Yayilgan
Keyword(s):  
Semantic Information ◽  
Hybrid Learning ◽  
Experimental Results ◽  
Learning Approach ◽  
Statistical Features ◽  
Ontology Learning ◽  
Learning Techniques ◽  
Novel Concept ◽  
Linguistic Ontology ◽  
First Time

This paper presents a novel concept enrichment objective metric combining contextual and semantic information of terms extracted from the domain documents. The proposed metric is called SEMCON which stands for semantic and contextual objective metric. It employs a hybrid learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The metric also introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. Subjective and objective experiments are conducted in various domains. Experimental results (F1) from computer domain show that SEMCON achieved better performance in contrast to tf*idf, and LSA methods, with 12.2%, 21.8%, and 24.5% improvement over them respectively. Additionally, an investigation into how much each of contextual and semantic components contributes to the overall task of concept enrichment is conducted and the obtained results suggest that a balanced weight gives the best performance.

EXTRACTING KNOWLEDGE FROM ENGLISH TRANSLATED QURAN USING NLP PATTERN

2015 ◽  
Vol 77 (19) ◽  
Author(s):  
Rohana Ismail ◽  
Zainab Abu Bakar ◽  
Nurazzah Abd. Rahman
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Formal Semantics ◽  
Ontology Development ◽  
Learning Approach ◽  
Ontology Learning ◽  
Initial Experiment ◽  
Knowledge Based ◽  
Easy Integration

Ontology is able to represent knowledge from an abstract view into formal semantics. It is essential for the success of knowledge-based systems because it has been used to share vocabulary, discover new knowledge, flexible access of knowledge and easy integration of knowledge. Currently, Ontology from Quran is not complete and most of the development is done manually. Manual development of ontology is time consuming and labor intensive task. Hence, the automatic or semi-automatic ontology development which is a field of Ontology Learning is needed to efficiently extract knowledge and transform it into Ontology. Current techniques employed in Ontology Learning are based on statistical and Natural Language Processing. This paper provides result from an experiment to extract knowledge using the existing Natural Language Processing (NLP) Pattern based on the Ontology Learning approach. Initial experiment shows that the pattern could be used to extract knowledge in terms of relations that exist in English translated Quran. In addition, NLP could also use to identify new pattern that can be further explored.

Sign in / Sign up

Export Citation Format

Share Document