Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open sour...Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.展开更多
With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital re...With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital resources, and have attracted a lot of users to annotate the resources with tags and bookmarks, which result in a large scale of tag data. Due to the exponential increase of social annotations, all the users are facing the same problem: How can we explore the desired resources efficiently in such a large tag dataset? Since the traditional methods such as tag cloud view and annotation match work well only in small annotation dataset, this paper studies the relationships of tag-tag, tag-resource and resource-resource through the co-occurrences and proposes a new efficient way for users to organize and explore the literature resources. Our research mainly focuses on two aspects:1) The hidden semantic relationships of popular tags and their relevant literature resources;2) the computing of literature resources similarity given a specific literature. A prototype system named PKUSpace is implemented and shows promising results.展开更多
基金the National Natural Science Foundation of China under Grant Nos.60673174 90412010 (国家自然科学基金)+1 种基金the National High-Tech Research and Development Plan of China under Grant Nos.2006AA02Z347 2006AA01A115 (国家高技术研究发展计划(863))
文摘提出了一种医学图像网格MedImGrid(medical image grid)基于语义的信息集成方法.基于HL7RIM(health level 7 referenced information model)生成父本体(HL7-RIM ontology),采用混合方式(hybridmeans)建立MedImGrid全局和局部本体的分级结构.结合代理和中间件技术开发了HL7(health level 7)Grid中间件,实现了具有医疗语义解析功能的HL7智能代理,以支持对异构数据源的Grid Service封装与统一访问.基于本体标记表达异构数据模式的语义模型在本体层的相关关联,参照MedImGrid各级本体实现数据源间的语义解析和映射.MedImGrid原型系统基于CGSP2(China grid support platform v2.0),采用了局部与全局语义映射松耦合的构架,其特有的层次结构使得网格环境下跨系统/医院的信息集成更加有效.
文摘Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20070001073)the National Natural Science Foundation of China(Grant Nos.90412010 and60773162)
文摘With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital resources, and have attracted a lot of users to annotate the resources with tags and bookmarks, which result in a large scale of tag data. Due to the exponential increase of social annotations, all the users are facing the same problem: How can we explore the desired resources efficiently in such a large tag dataset? Since the traditional methods such as tag cloud view and annotation match work well only in small annotation dataset, this paper studies the relationships of tag-tag, tag-resource and resource-resource through the co-occurrences and proposes a new efficient way for users to organize and explore the literature resources. Our research mainly focuses on two aspects:1) The hidden semantic relationships of popular tags and their relevant literature resources;2) the computing of literature resources similarity given a specific literature. A prototype system named PKUSpace is implemented and shows promising results.
