The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
Chengzheng Sun - One of the best experts on this subject based on the ideXlab platform.
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EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines' immense power of information collection and pre-processing and human users' knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
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IUI - Supporting exploratory information seeking by epistemology-based social Search
Proceedings of the 15th international conference on Intelligent user interfaces - IUI '10, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Formulating proper keywords and evaluating Search results are common difficulties in exploratory information seeking. Reusing and refining others' successful Searches are pragmatic directions to tackle these difficulties. In this paper, we present a novel epistemology-based social Search solution, where Search epistemologies are effectively shared, reused, and refined by others with the same or similar Search interests through novel user interfaces. We have developed a prototype system Baijia and experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine in supporting exploratory information seeking.
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HCIS - EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
IFIP Advances in Information and Communication Technology, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines’ immense power of information collection and pre-processing and human users’ knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
Yuqing Mao - One of the best experts on this subject based on the ideXlab platform.
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EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines' immense power of information collection and pre-processing and human users' knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
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IUI - Supporting exploratory information seeking by epistemology-based social Search
Proceedings of the 15th international conference on Intelligent user interfaces - IUI '10, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Formulating proper keywords and evaluating Search results are common difficulties in exploratory information seeking. Reusing and refining others' successful Searches are pragmatic directions to tackle these difficulties. In this paper, we present a novel epistemology-based social Search solution, where Search epistemologies are effectively shared, reused, and refined by others with the same or similar Search interests through novel user interfaces. We have developed a prototype system Baijia and experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine in supporting exploratory information seeking.
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HCIS - EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
IFIP Advances in Information and Communication Technology, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines’ immense power of information collection and pre-processing and human users’ knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
Marie Christine Ho Ba Tho - One of the best experts on this subject based on the ideXlab platform.
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Knowledge-based personalized Search Engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics
Journal of biomedical informatics, 2012Co-Authors: Tien Tuan Dao, Tuan Nha Hoang, Marie Christine Ho Ba ThoAbstract:Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from Conventional Search Engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized Search Engine was developed. Our Search Engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based Search Engine, a Conventional Search Engine and a semantic Search Engine showed the originality and the robustness of our knowledge-based personalized Search Engine. In fact, our knowledge-based personalized Search Engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes.
Haifeng Shen - One of the best experts on this subject based on the ideXlab platform.
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EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines' immense power of information collection and pre-processing and human users' knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
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IUI - Supporting exploratory information seeking by epistemology-based social Search
Proceedings of the 15th international conference on Intelligent user interfaces - IUI '10, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Formulating proper keywords and evaluating Search results are common difficulties in exploratory information seeking. Reusing and refining others' successful Searches are pragmatic directions to tackle these difficulties. In this paper, we present a novel epistemology-based social Search solution, where Search epistemologies are effectively shared, reused, and refined by others with the same or similar Search interests through novel user interfaces. We have developed a prototype system Baijia and experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine in supporting exploratory information seeking.
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HCIS - EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking
IFIP Advances in Information and Communication Technology, 2010Co-Authors: Yuqing Mao, Haifeng Shen, Chengzheng SunAbstract:Search Engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory Search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of Searchers who share common or relevant Search interests/goals). In this paper, we present an epistemology-based social Search framework for supporting exploratory information seeking, which makes the best of both Search Engines’ immense power of information collection and pre-processing and human users’ knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social Search system outperforms a Conventional Search Engine for most exploratory information seeking tasks.
Aymen Elkhlifi - One of the best experts on this subject based on the ideXlab platform.
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Using Social Conversational Context For Detecting Users Interactions on Microblogging Sites
Revue des Nouvelles Technologies de l'Information, 2015Co-Authors: Rami Belkaroui, Rim Faiz, Aymen ElkhlifiAbstract:In the current era, microblogging services like Twitter, gives people the ability to communicate, interact, collaborate with each other, reply to messages from others and create conversations. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works have proposed tools for tweets Search focused only to retrieve relevant tweets. Therefore, users are unable to explore the results or retrieve more relevant tweets based on the content, and may get lost or become frustrated by the information overload. In this paper, we propose a new method to retrieve conversation on microblog-ging sites particularly Twitter. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to Conventional Search Engine. The proposed method has been implemented and evaluated by comparing it to Google and Twitter Search Engines and we obtained very promising results.
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Social Users Interactions Detection Based on Conversational Aspects
2015Co-Authors: Rami Belkaroui, Rim Faiz, Aymen ElkhlifiAbstract:Last years, people are becoming more communicative through expansion of services and multi-platform applications such as blogs, forums and social networks which establishes social and collabo-rative backgrounds. These services like Twitter, which is the main domain used in our work can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works have proposed tools for tweets Search focused only to retrieve the most recent but relevant tweets that address the information need. Therefore, users are unable to explore the results or retrieve more relevant tweets based on the content and may get lost or become frustrated by the information overload. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on mi-croblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to Conventional Search Engine. The proposed method has been implemented and evaluated by comparing it to Google and Twitter Search Engines and we obtained very promising results.
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Conversation Analysis on Social Networking Sites
2014Co-Authors: Rami Belkaroui, Rim Faiz, Aymen ElkhlifiAbstract:With the explosion of Web 2.0, people are becoming more communicative through expansion of services and multi-platform applications such as microblogs, forums and social networks which establishes social and collabora-tive backgrounds. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works focused only to retrieve separate tweets or those sharing same hashtags, but, it is not powerful enough if the goal of the Search is to retrieve relevant tweets based on content. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on microblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to Conventional Search Engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword Search on twitter and google. We have evaluated our method on collected social network corpus related to specific subjects, and we obtained good results.
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SITIS - Conversation Analysis on Social Networking Sites
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014Co-Authors: Rami Belkaroui, Rim Faiz, Aymen ElkhlifiAbstract:With the explosion of Web 2.0, people are becoming more communicative through expansion of services and multi-platform applications such as micro blogs, forums and social networks which establishes social and collaborative backgrounds. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works focused only to retrieve separate tweets or those sharing same hash tags, but, it is not powerful enough if the goal of the Search is to retrieve relevant tweets based on content. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on micro blogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to Conventional Search Engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword Search on twitter and Google. We have evaluated our method on collected social network corpus related to specific subjects, and we obtained good results.