SOAF: Semantic Indexing System Based on Collaborative Tagging

10.28945/371 ◽  
2008 ◽  
Vol 4 ◽  
pp. 137-149 ◽  
Author(s):  
Doina Ana Cernea ◽  
Esther Del Moral-Pérez ◽  
Jose E. Labra Gayo
Keyword(s):  
Semantic Indexing ◽  
Collaborative Tagging ◽  
Indexing System

scholarly journals IDSIS: Intelligent Document Semantic Indexing System

2002 ◽  
pp. 307-310
Author(s):  
Zhongzhi Shi ◽  
Bin Wu ◽  
Qing He ◽  
Xiujun Gong ◽  
Shaohui Liu ◽  
...  
Keyword(s):  
Semantic Indexing ◽  
Indexing System

PSIS: Parallel Semantic Indexing System - Preliminary Experiments

2013 ◽  
pp. 133-140
Author(s):  
Flora Amato ◽  
Francesco Gargiulo ◽  
Vincenzo Moscato ◽  
Fabio Persia ◽  
Antonio Picariello
Keyword(s):  
Semantic Indexing ◽  
Indexing System

Intelligence Chinese Document Semantic Indexing System

2003 ◽  
Vol 02 (03) ◽  
pp. 407-424 ◽  
Author(s):  
Zhongzhi Shi ◽  
Qing He ◽  
Ziyan Jia ◽  
Jiayou Li
Keyword(s):  
New Technologies ◽  
Information Gain ◽  
Information Organization ◽  
The Internet ◽  
Semantic Indexing ◽  
Clustering Method ◽  
Web Documents ◽  
Classification Approach ◽  
Interactive Retrieval ◽  
Indexing System

With the rapid growth of the Internet, how to get information from this huge information space becomes an even more important problem. In this paper, An Intelligence Chinese Document Semantic Indexing System; ICDSIS, is proposed. Some new technologies are integrated in ICDSIS to obtain good performance. ICDSIS is composed of four key procedures. A parallel, distributed and configurable Spider is used for information gather; a multi-hierarchy document classification approach combining the information gain initially processes gathered web documents; a swarm intelligence based document clustering method is used for information organization; a concept-based retrieval interface is applied for user interactive retrieval. ICDSIS is an all-sided solution for information retrieval on the Internet.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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