TIIS (한국인터넷정보학회)
Current Result Document : 2 / 2
| 한글제목(Korean Title) |
Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology |
| 영문제목(English Title) |
Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology |
| 저자(Author) |
Byoungman An
Youngseop Kim
Nayoung Ko
Myeonggi Hong
Jeeseon Hwang
Jeonghyeon Chang
EuiGab Hwang
Chuanrong Wu
Ning Tan
Zhi Lu
Xiaoming Yang
Mark E. McMurtrey
Baoshu Li
Xin Zhou
Ping Dong
Selvalakshmi B
Subramaniam M
Sathiyasekar K
|
| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3102 ~ 3119 (2021. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved. |
| 키워드(Keyword) |
Audio Video Bridge
AVB
video streaming
Autonomous
In-Vehicle Network
Online and Offline delinquency
Opportunity factors
Routine Activity theory
Self-Control Theory
Situational Action Theory
Knowledge transaction
two-part tariff pricing
lump-sum pricing
royalty pricing
subscription pricing
pay-per-use pricing
Power Failure Sensitivity Analysis
L1/2 Sparsity
Logistic Regression
Alternating Directions of Multipliers
Document Clustering
web search
Social Networks
semantic ontology
Information Retrieval
Semantic Conceptual Relational Similarity
Query Relational Semantic Score
|
| 파일첨부 |
PDF 다운로드
|
