Xiaohua Sun

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Xiaohua Sun is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Motivating Wiki-based collaborative learning by increasing awareness of task conflict: A design science approach Collaborative learning
Task conflict
Lecture Notes in Computer Science English 2014 Wiki system has been deployed in many collaborative learning projects. However, lack of motivation is a serious problem in the collaboration process. The wiki system is originally designed to hide authorship information. Such design may hinder users from being aware of task conflict, resulting in undesired outcomes (e.g. reduced motivation, suppressed knowledge exchange activities). We propose to incorporate two different tools in wiki systems to motivate learners by increasing awareness of task conflict. A field test was executed in two collaborative writing projects. The results from a wide-scale survey and a focus group study confirmed the utility of the new tools and suggested that these tools can help learners develop both extrinsic and intrinsic motivations to contribute. This study has several theoretical and practical implications, it enriched the knowledge of task conflict, proposed a new way to motivate collaborative learning, and provided a low-cost resolution to manage task conflict. 0 0
A semantic approach to recommending text advertisements for images Crossmedia mining
Semantic matching
Visual contextual advertising
RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems English 2012 In recent years, more and more images have been uploaded and published on the Web. Along with text Web pages, images have been becoming important media to place relevant advertisements. Visual contextual advertising, a young research area, refers to finding relevant text advertisements for a target image without any textual information (e.g., tags). There are two existing approaches, advertisement search based on image annotation, and more recently, advertisement matching based on feature translation between images and texts. However, the state of the art fails to achieve satisfactory results due to the fact that recommended advertisements are syntactically matched but semantically mismatched. In this paper, we propose a semantic approach to improving the performance of visual contextual advertising. More specifically, we exploit a large high-quality image knowledge base (ImageNet) and a widely-used text knowledge base (Wikipedia) to build a bridge between target images and advertisements. The image-advertisement match is built by mapping images and advertisements into the respective knowledge bases and then finding semantic matches between the two knowledge bases. The experimental results show that semantic match outperforms syntactic match significantly using test images from Flickr. We also show that our approach gives a large improvement of 16.4% on the precision of the top 10 matches over previous work, with more semantically relevant advertisements recommended. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM). 0 0
Towards effective short text deep classification Classification
Large scale hierarchy
Short text
SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval English 2011 Recently, more and more short texts (e.g., ads, tweets) appear on the Web. Classifying short texts into a large taxonomy like ODP or Wikipedia category system has become an important mining task to improve the performance of many applications such as contextual advertising and topic detection for micro-blogging. In this paper, we propose a novel multi-stage classification approach to solve the problem. First, explicit semantic analysis is used to add more features for both short texts and categories. Second, we leverage information retrieval technologies to fetch the most relevant categories for an input short text from thousands of candidates. Finally, a SVM classifier is applied on only a few selected categories to return the final answer. Our experimental results show that the proposed method achieved significant improvements on classification accuracy compared with several existing state of art approaches. 0 0
Zhishi.me - Weaving Chinese linking open data Lecture Notes in Computer Science English 2011 Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia. Finally, we also introduce three Web access entries namely SPARQL endpoint, lookup interface and detailed data view, which conform to the principles of publishing data sources to LOD. 0 0
Dandelion: supporting coordinated, collaborative authoring in Wikis Awareness
Collaborative authoring
Conference on Human Factors in Computing Systems English 2010 0 1