Use of ontological knowledge in machine learning methods for intelligent analysis of Big Data
The paper discusses problems related to the processing of Big Data in order to acquire implicit knowledge from them. Machine learning (ML) methods oriented on these tasks can be combined with elements of the Semantic Web technologies and Artificial Intelligence (AI), which deals with intelligent be...
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| Date: | 2019 |
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| Main Author: | |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
PROBLEMS IN PROGRAMMING
2019
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| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/336 |
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| Journal Title: | Problems in programming |
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Problems in programming| Summary: | The paper discusses problems related to the processing of Big Data in order to acquire implicit knowledge from them. Machine learning (ML) methods oriented on these tasks can be combined with elements of the Semantic Web technologies and Artificial Intelligence (AI), which deals with intelligent behavior, learning and adaptation in computational systems. We analyse challenges and opportunities background knowledge using to improve ML results, the role of ontologies and other resources of domain knowledge. Domain knowledge could improve the quality of ML results by using reasoning techniques to select learning models and prepare the training and test data. We propose some examples demonstrated the use of ontologies and semantic Wiki markup for improving the efficiency of machine learning are considered deal with functional posibilities of the portal version of the Great Ukrainian Encyclopedia. Ontological model of this informational resource is considered as a domain knowledge base. Groupping of examples is based on high-level ontological classes, and semantic properties and their relations are used for construction of space of attributes.Problems in programming 2018; 4: 69-81 |
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