| Type of Material: | Thesis |
| Title: | Analyze the Impact of Emerging Terms in Semantic Similarity for Effective Data Mining Applications |
| Researcher: | KARPAGAM, P |
| Guide: | SIVASUBRAMANIAN, S |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2019 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2019; D13CS015 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aKARPAGAM, P|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aAnalyze the Impact of Emerging Terms in Semantic Similarity for Effective Data Mining Applications |
| 260 | __ | |aChennai|bBharath University, Chennai|c2019 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2019|oD13CS015 |
| 520 | __ | |aDue to the emerging web searching process and the requirement of fast and efficient result provisioning, the semantic similarity becomes a significant objective in Information Retrieval (IR). Use of static lexical resources leads many conventional semantic similarity measurement techniques to ignore temporal aspects of concepts, resulting in an inconsistent semantic measure and IR. In reality, the semantic processing or data mining technique is applied to automatically extract the relevant information from lexical ontologies that contains the list of semantically related terms of a particular term. This research work delivers two significant contributions in evolving terms that impact the applications that depend on the semantic similarity measure. The first contribution of this work exploits the sources of disease concepts captured across biomedical resources and automatically identifies the medical terms to extend the Disease Ontology. The Extending disease ontology with newly evaluated terms to improve s |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aSIVASUBRAMANIAN, S|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/311279|yShodhganga |
| 905 | __ | |afromsg |
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