Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization
Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analy...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/136172 |
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| author | Asmaa M. El-Said Ali I. Eldesoky Hesham A. Arafat |
| author_facet | Asmaa M. El-Said Ali I. Eldesoky Hesham A. Arafat |
| author_sort | Asmaa M. El-Said |
| collection | DOAJ |
| description | Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and Q-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach. |
| format | Article |
| id | doaj-art-548db69845ca4fb08851d22a34cc0e20 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-548db69845ca4fb08851d22a34cc0e202025-08-20T02:05:46ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/136172136172Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts OrganizationAsmaa M. El-Said0Ali I. Eldesoky1Hesham A. Arafat2Department of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, EgyptDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, EgyptDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, EgyptTremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and Q-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach.http://dx.doi.org/10.1155/2015/136172 |
| spellingShingle | Asmaa M. El-Said Ali I. Eldesoky Hesham A. Arafat Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization The Scientific World Journal |
| title | Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization |
| title_full | Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization |
| title_fullStr | Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization |
| title_full_unstemmed | Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization |
| title_short | Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization |
| title_sort | exploiting semantic annotations and q learning for constructing an efficient hierarchy graph texts organization |
| url | http://dx.doi.org/10.1155/2015/136172 |
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