scholarly journals Learning from Ontology Streams with Semantic Concept Drift

2017 ◽  
Author(s):  
Jiaoyan Chen ◽  
Freddy Lecue ◽  
Jeff Z. Pan ◽  
Huajun Chen
Keyword(s):  
Semantic Web ◽  
Data Stream ◽  
Concept Drift ◽  
Data Distribution ◽  
Accurate Prediction ◽  
Knowledge Structures ◽  
Semantic Concept ◽  
Web Data ◽  
Semantic Inference

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

scholarly journals An ensemble based on neural networks with random weights for online data stream regression

2019 ◽  
Vol 24 (13) ◽  
pp. 9835-9855 ◽  
Author(s):  
Ricardo de Almeida ◽  
Yee Mey Goh ◽  
Radmehr Monfared ◽  
Maria Teresinha Arns Steiner ◽  
Andrew West
Keyword(s):  
Data Stream ◽  
Prediction Accuracy ◽  
Concept Drift ◽  
Learning Algorithms ◽  
Data Distribution ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Online Data ◽  
Data Prediction ◽  
Random Weights

Abstract Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on neural network with random weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm’s response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift, and much faster updating times compared to the existing available approach. Additionally, the use of design of experiments as an effective tool for hyperparameter tuning is demonstrated.

Concepts Seeds Gathering and Dataset Updating Algorithm for Handling Concept Drift

2015 ◽  
Vol 7 (2) ◽  
pp. 29-57 ◽  
Author(s):  
Nabil M. Hewahi ◽  
Ibrahim M. Elbouhissi
Keyword(s):  
Classification Accuracy ◽  
Data Stream ◽  
Concept Drift ◽  
Data Distribution ◽  
Classification Model ◽  
Classification Models ◽  
Comparison Study ◽  
New Approach ◽  
New Concepts ◽  
Traditional Classification

In data mining, the phenomenon of change in data distribution over time is known as concept drift. In this research, the authors introduce a new approach called Concepts Seeds Gathering and Dataset Updating algorithm (CSG-DU) that gives the traditional classification models the ability to adapt and cope with concept drift as time passes. CSG-DU is concerned with discovering new concepts in data stream and aims to increase the classification accuracy using any classification model when changes occur in the underlying concepts. The proposed approach has been tested using synthetic and real datasets. The experiments conducted show that after applying the authors' approach, the classification accuracy increased from low values to high and acceptable ones. Finally, a comparison study between CSG-DU and Set Formation for Delayed Labeling algorithm (SFDL) has been conducted; SFDL is an approach that handles sudden and gradual concept drift. CSG-DU results outperforms SFDL in terms of classification accuracy.

Research and Development on Semantic Web Data Management

2009 ◽  
Vol 20 (11) ◽  
pp. 2950-2964 ◽  
Author(s):  
Xiao-Yong DU ◽  
Yan WANG ◽  
Bin LÜ
Keyword(s):  
Semantic Web ◽  
Research And Development ◽  
Data Management ◽  
Web Data ◽  
Web Data Management

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification

Ontologien in der Praxis

2021 ◽  
Vol 71 (4) ◽  
pp. 319-337
Author(s):  
Stefan Münnich
Keyword(s):  
Semantic Web ◽  
Digital Age ◽  
Knowledge Structures ◽  
Basic Concepts

The processing, dissemination, classification and verifiability of "resilient" knowledge represents some of the most pressing challenges facing society as a whole in this still young digital age. Information-theoretical ontologies that are integrated into the vision of the so-called semantic web can be of great use here. On the basis of examples of applications as well as of the explanation of basic concepts, mechanisms and challenges for the systematization and modelling of knowledge structures the current possibilities for a "semantic" digital musicology are shown.

scholarly journals Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data

2007 ◽  
pp. 228-246 ◽  
Author(s):  
Matthew Perry ◽  
Amit P. Sheth ◽  
Farshad Hakimpour ◽  
Prateek Jain
Keyword(s):  
Semantic Web ◽  
Web Data
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