A component search engine model on Internet

2002 ◽  
Author(s):  
Ruan Tong ◽  
Sheng Haolin
Keyword(s):  
Search Engine ◽  
Engine Model

Development of a Novel Compressed Index-Query Web Search Engine Model

2011 ◽  
Vol 6 (3) ◽  
pp. 39-56 ◽  
Author(s):  
Hussein Al-Bahadili ◽  
Saif Al-Saab
Keyword(s):  
Data Compression ◽  
Search Engine ◽  
Web Search ◽  
Server Side ◽  
Engine Model ◽  
Web Search Engine ◽  
Index Index ◽  
Compression Layer ◽  
Compressed Index ◽  
Space Requirements

In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.

Research of Intelligent Search Engine Based on Multi-Ontology

2012 ◽  
Vol 241-244 ◽  
pp. 1659-1663
Author(s):  
Shu Dong Zhang ◽  
Can Zhang ◽  
Jing Wang
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Domain Knowledge ◽  
Recall Rate ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Engine Model ◽  
Cross Domain ◽  
User Query ◽  
Primary Means

With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.

Development of a Novel Compressed Index-Query Web Search Engine Model

2014 ◽  
pp. 275-293
Author(s):  
Hussein Al-Bahadili ◽  
Saif Al-Saab
Keyword(s):  
Data Compression ◽  
Search Engine ◽  
Web Search ◽  
Server Side ◽  
Engine Model ◽  
Web Search Engine ◽  
Index Index ◽  
Compression Layer ◽  
Compressed Index ◽  
Space Requirements

In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.

Effective and efficient classification on a search-engine model

2007 ◽  
Vol 16 (2) ◽  
pp. 129-154 ◽  
Author(s):  
Aris Anagnostopoulos ◽  
Andrei Broder ◽  
Kunal Punera
Keyword(s):  
Search Engine ◽  
Engine Model
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