摘要
Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. In this paper, we develop a fuzzy search engine, called Fuzzy-Go. First, a fuzzy ontology is constructed by using fuzzy logic to capture the similarities of terms in the ontology, which offering appropriate semantic distances between terms to accomplish the semantic search of keywords. The Fuzzy-Go search engine can thus automatically retrieve web pages that contain synonyms or terms similar to keywords. Second, users can input multiple keywords with different degrees of importance based on their needs. The totally satisfactory degree of keywords can be aggregated based on their degrees of importance and degrees of satisfaction. Third, the domain classification of web pages offers users to select the appropriate domain for searching web pages, which excludes web pages in the inappropriate domains to reduce the search space and to improve the search results.
| 原文 | English |
|---|---|
| 主出版物標題 | FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings |
| 頁面 | 2684-2689 |
| 頁數 | 6 |
| DOIs | |
| 出版狀態 | Published - 2011 九月 27 |
| 事件 | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan 持續時間: 2011 六月 27 → 2011 六月 30 |
出版系列
| 名字 | IEEE International Conference on Fuzzy Systems |
|---|---|
| ISSN（列印） | 1098-7584 |
Other
| Other | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 |
|---|---|
| 國家 | Taiwan |
| 城市 | Taipei |
| 期間 | 11-06-27 → 11-06-30 |
指紋
All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
引用此文
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Developing a fuzzy search engine based on fuzzy ontology and semantic search. / Lai, Lien-Fu; Wu, Chao-Chin; Lin, Pei Ying; Huang, Liang Tsung.
FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. 2011. p. 2684-2689 6007378 (IEEE International Conference on Fuzzy Systems).研究成果: Conference contribution
TY - GEN
T1 - Developing a fuzzy search engine based on fuzzy ontology and semantic search
AU - Lai, Lien-Fu
AU - Wu, Chao-Chin
AU - Lin, Pei Ying
AU - Huang, Liang Tsung
PY - 2011/9/27
Y1 - 2011/9/27
N2 - Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. In this paper, we develop a fuzzy search engine, called Fuzzy-Go. First, a fuzzy ontology is constructed by using fuzzy logic to capture the similarities of terms in the ontology, which offering appropriate semantic distances between terms to accomplish the semantic search of keywords. The Fuzzy-Go search engine can thus automatically retrieve web pages that contain synonyms or terms similar to keywords. Second, users can input multiple keywords with different degrees of importance based on their needs. The totally satisfactory degree of keywords can be aggregated based on their degrees of importance and degrees of satisfaction. Third, the domain classification of web pages offers users to select the appropriate domain for searching web pages, which excludes web pages in the inappropriate domains to reduce the search space and to improve the search results.
AB - Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. In this paper, we develop a fuzzy search engine, called Fuzzy-Go. First, a fuzzy ontology is constructed by using fuzzy logic to capture the similarities of terms in the ontology, which offering appropriate semantic distances between terms to accomplish the semantic search of keywords. The Fuzzy-Go search engine can thus automatically retrieve web pages that contain synonyms or terms similar to keywords. Second, users can input multiple keywords with different degrees of importance based on their needs. The totally satisfactory degree of keywords can be aggregated based on their degrees of importance and degrees of satisfaction. Third, the domain classification of web pages offers users to select the appropriate domain for searching web pages, which excludes web pages in the inappropriate domains to reduce the search space and to improve the search results.
UR - http://www.scopus.com/inward/record.url?scp=80053064144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053064144&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2011.6007378
DO - 10.1109/FUZZY.2011.6007378
M3 - Conference contribution
AN - SCOPUS:80053064144
SN - 9781424473175
T3 - IEEE International Conference on Fuzzy Systems
SP - 2684
EP - 2689
BT - FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
ER -