scholarly journals Using Ontologies in Semantic Data Mining with SEGS and g-SEGS

2011 ◽  
pp. 165-178 ◽  
Author(s):  
Nada Lavrač ◽  
Anže Vavpetič ◽  
Larisa Soldatova ◽  
Igor Trajkovski ◽  
Petra Kralj Novak
Keyword(s):  
Data Mining ◽  
Semantic Data ◽  
Semantic Data Mining

Semantic Data Mining of Financial News Articles

2013 ◽  
pp. 294-307 ◽  
Author(s):  
Anže Vavpetič ◽  
Petra Kralj Novak ◽  
Miha Grčar ◽  
Igor Mozetič ◽  
Nada Lavrač
Keyword(s):  
Data Mining ◽  
Financial News ◽  
Semantic Data ◽  
Semantic Data Mining

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

Semantic data mining of short utterances

2005 ◽  
Vol 13 (5) ◽  
pp. 672-680 ◽  
Author(s):  
Lee Begeja ◽  
H. Drucker ◽  
D. Gibbon ◽  
P. Haffner ◽  
Zhu Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Semantic Data ◽  
Semantic Data Mining ◽  
Short Utterances

Semantic Data Mining

2008 ◽  
pp. 3524-3530
Author(s):  
Protima Banerjee ◽  
Xiaohua Hu ◽  
Illhio Yoo
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Transaction Processing ◽  
Data Sets ◽  
Ordered Trees ◽  
Semantic Data ◽  
Symbolic Sequences ◽  
Semantic Data Mining ◽  
Enterprise Data Warehouse ◽  
Mining Algorithms

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

Pattern Based Feature Construction in Semantic Data Mining

2016 ◽  
pp. 823-864
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

An Ontology-Based Semantic Data Mining in Mobile Environment

2017 ◽  
Vol 23 (10) ◽  
pp. 10241-10245
Author(s):  
Mi-Sug Gu ◽  
Jeong-Hee Hwang
Keyword(s):  
Data Mining ◽  
Mobile Environment ◽  
Semantic Data ◽  
Semantic Data Mining

An Environmental Personalized Tag Recommendation Model

2013 ◽  
Vol 380-384 ◽  
pp. 1358-1361
Author(s):  
Xiao Wang ◽  
Chi Zhang ◽  
Peng Zhou Zhang
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Social Tagging ◽  
Tag Recommendation ◽  
Character Analysis ◽  
Semantic Data ◽  
Semantic Data Mining

Social tagging has been widely used in Web2.0 applications. An optimized tagging recommendation model is showed in this article which can be divided into four layers. They are respectively based on essential data, character analysis, semantic data mining and user advanced behavior analysis. By using this model, flexible tag recommendation services can be provided to fit different application circumstances.

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