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Using Machine Learning to Support Continuous Ontology Development [chapter]

Maryam Ramezani, Hans Friedrich Witschel, Simone Braun, Valentin Zacharias
2010 Lecture Notes in Computer Science  
This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social  ...  These results point to novel possibilities to apply machine learning technologies to support social semantic applications.  ...  Acknowledgments This work was supported by the MATURE Project co-funded by the European Commission.  ... 
doi:10.1007/978-3-642-16438-5_28 fatcat:a3nss6edczevlpr4acpgkq5ohm

Using Machine Learning to Support Continuous Ontology Development [article]

Maryam Ramezani, Hans Friedrich Witschel, Simone Braun, Valentin Zacharias, Fachhochschule Nordwestschweiz FHNW, Fachhochschule Nordwestschweiz FHNW, N.N.
2017
This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social  ...  These results point to novel possibilities to apply machine learning technologies to support social semantic applications.  ...  Acknowledgments This work was supported by the MATURE Project co-funded by the European Commission.  ... 
doi:10.26041/fhnw-2804 fatcat:snvitwewuna6jeg4h4vyonoc4y

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

Hamed Hassanzadeh, MohammadReza Keyvanpour
2011 International journal of Web & Semantic Technology  
To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized  ...  For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation  ...  Supporting Ontology Semi- automatic General EOAAC [29] Using association rule mining to extract co- occurrences of concepts Supporting and enhancing Ontology Semi- automatic Domain dependent  ... 
doi:10.5121/ijwest.2011.2203 fatcat:p2wc236rpnckxj5bnl5xypvfga

TELESUP - Textual Self-Learning Support Systems

Sebastian Furth, Joachim Baumeister
2014 Lernen, Wissen, Daten, Analysen  
In this paper, we report on an ongoing project that aims for a methodology and tool for ontology development in a self-improving manner.  ...  The approach makes heavy use of methods known in natural language processing and information extraction.  ...  Acknowledgments The work described in this paper is supported by the Bundesministerium für Wirtschaft und Energie (BMWi) under the grant ZIM KF2959902BZ4 "SELE-SUP -SElf-LEarning SUPport Systems".  ... 
dblp:conf/lwa/FurthB14 fatcat:crbg4ar7uvdq7moruqn7ygzs7q

An Ontology-based Decision Support System for Multi-objective Prediction Tasks

Touria Hamim, Faouzia Benabbou, Nawal Sael
2021 International Journal of Advanced Computer Science and Applications  
The proposed system uses Decision Tree algorithm (C5.0), but other machine learning models can be added if they prove to be more efficient.  ...  Student profile modeling is a topic that continues to attract the interest of both academics and researchers because of its crucial role in the development of predictive or decision support systems.  ...  The authors also proposed a system that combines the proposed ontology with machine learning, using an algorithm based on decision trees and SWRL rules to achieve several objectives such as prediction  ... 
doi:10.14569/ijacsa.2021.0121224 fatcat:subdzd7x6jgehd4vq5wxwmemv4

Introduction to the Special Issue on Software Engineering Methods, Tools and Products Improvement and Evaluation

Lech Madeyski, Miroslaw Ochodek
2018 Foundations of Computing and Decision Sciences  
Stefanowski, Foundations of Computing and Decision Sciences Editor-in-Chief, and Irmina Mas lowska, Managing Editor of the journal, for continuous support in managing the review process and preparing this  ...  Acknowledgment We would like to thank the authors and reviewers for their efforts to successfully complete this special section.  ...  They show different applications of vastly understood data analytics, machine learning, and knowledge engineering to support software development processes.  ... 
doi:10.1515/fcds-2018-0013 fatcat:meeangte55hgtiffja4jydjvue

VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning

2018 IEEE Transactions on Visualization and Computer Graphics  
While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes.  ...  It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML.  ...  ACKNOWLEDGMENTS We wish to thank Martin Schall for his feedback and also thank the dbvis support for hosting/deploying the websites.  ... 
doi:10.1109/tvcg.2018.2864838 pmid:30130221 fatcat:pmfmsct2ufdovo6bw7qvcwynha

Ontology Based System for Prediction of Diseases

Pallavi Laxmikant Chavan, Mandar S. Karyakarte
2020 International Journal of Scientific Research in Science Engineering and Technology  
The detection of disease is done by using prediction algorithm. Here, machine-learning algorithm is has been used to find the accuracy.  ...  In this proposed system, we provides prediction of various diseases that occurs through using machine learning that will be effective.  ...  With the growing availability of data, the research focuses on machine-learning approaches that will continue to find accurate results.  ... 
doi:10.32628/ijsrset207365 fatcat:s2eb7msjhvb6lbpf53goi6uwhe

A model of multitutor ontology-based learning environments

Antonija Mitrovic, Vladan Devedzic
2004 International Journal of Continuing Engineering Education and Life-Long Learning  
The ontologies are used by ontology processors to decide which tutors might benefit a student who needs to learn new concepts.  ...  Continuing Engineering Education and Lifelong Learning, Vol. x, No. x, pp.xxx-xxx.  ...  Acknowledgements The authors thank the Erskine Fund of the University of Canterbury, for funding the visit of the second author to New Zealand.  ... 
doi:10.1504/ijceell.2004.004971 fatcat:xpcpsix57jdfzhnizfhrodhvcm

Implementing E-Learning Ontology to Scale For Provenance

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The paper defines an ontology for semantic structuring, semantic rendering and applies provenance on suggested ontology to achieve authentic results.  ...  The defined ontogoly is suitable for consumption of both man and machine in the context of the e-learning and Semantic data rendering Web Keywords  ...  Acknowledgement also extends to various researchers and scientists, who through there research articles enable us to author this paper.  ... 
doi:10.35940/ijitee.i7735.078919 fatcat:v64foh4nvzfzxjvr2z3jgzu4ku

Ontology Driven Continuous Learning Approach

Dakshi T. K. Geeganage, Asoka. S. Karunananda
2015 International Journal of Knowledge Engineering-IACSIT  
Thus it is possible to model the learning process as ontology modelling process and the research has been conducted to develop an automated ontological solution for continuous learning.  ...  This system can be used as the main learning process of any application.  ...  classes by the ontology driven continuous learning approach is greater than or equal to 0.6.  ... 
doi:10.7763/ijke.2015.v1.6 fatcat:mt3b3y5rwfgilboprodp7z6l3u

On the Semantics of Big Earth Observation Data (talk)

Gilberto Camara
2020 Zenodo  
Since petabytes of imagery are accessible, researchers can now track changes continuously. To work with big Earth observation data, scientists are developing data-driven and theory-limited methods.  ...  In this talk, we argue that current ontologies and descriptive schemas used in image analysis cannot capture the complexity of landscape dynamics unveiled by big data.  ...  "these trends require a radical paradigm shift in ontology engineering (…) to a high number of local ontologies that are driven by application needs and developed bottom-up out of observation data" (Janowicz  ... 
doi:10.5281/zenodo.3873142 fatcat:k76ob52ot5efhloaovabuyzxyi

Ontology based Semantic e-Learning Model– A Review

Vaishali B., S. A., Mukta Dhopeshwarkar
2018 International Journal of Computer Applications  
This show ontology takes an important place in elearning system. Ontology is used to classify the things which are needed in e-learning system.  ...  Ontology formally represent knowledge as a set of concept within domain, and the relationship between pairs of concepts. It can be used to model a domain and support reasoning about concepts.  ...  Upper ontology: concepts supporting development of an ontology, meta-ontology. 2.  ... 
doi:10.5120/ijca2018918209 fatcat:yhojjr7tzjdljaivdqh7loecau

SemML: Reusable ML Models for Condition Monitoring in Discrete Manufacturing

Yulia Svetashova, Baifan Zhou, Stefan Schmid, Tim Pychynski, Evgeny Kharlamov
2020 International Semantic Web Conference  
In this demo we present our SemML system that addresses these challenges by enhancing machine learning with semantic technologies: by capturing domain and ML knowledge in ontologies and ontology templates  ...  The main challenges include the exhausting effort of data integration and lacking of generalisability of developed ML pipelines to diverse data variants, sources, and domain processes.  ...  -Machine Learning Visualizer and Interpreter uses information about the feature engineering algorithms and engineered features to facilitate the visualisation of the machine learning modelling and select  ... 
dblp:conf/semweb/SvetashovaZSPK20 fatcat:psekm25bozcdxe6qmt2ffa3o7q

Ontology learning: state of the art and open issues

Lina Zhou
2007 Journal of Special Topics in Information Technology and Management  
The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development.  ...  Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development  ...  One of the major challenges lies in ontology acquisition [11, 12] . Ontology learning refers to the automatic discovery and creation of ontological knowledge using machine learning techniques.  ... 
doi:10.1007/s10799-007-0019-5 fatcat:4hyq6hnxkncw7lrp2ll5l5r5im
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