Hejie Chen

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Hejie Chen 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
Investigating the determinants of contribution value in Wikipedia Contributor type
Critical mass
International Journal of Information Management English 2013 The recent prevalence of wiki applications has demonstrated that wikis have high potential in facilitating knowledge creation, sharing, integration, and utilization. As wikis are increasingly adopted in contexts like business, education, research, government, and the public, how to improve user contribution becomes an important concern for researchers and practitioners. In this research, we focus on the quality aspect of user contribution: contribution value. Building upon the critical mass theory and research on editing activities in wikis, this study investigates whether user interests and resources can increase contribution value for different types of users. We develop our research model and empirically test it using survey method and content analysis method in Wikipedia. The results demonstrate that (1) for users who emphasize substantive edits, depth of interests and depth of resources play more influential roles in affecting contribution value; and (2) for users who focus on non-substantive edits, breadth of interests and breadth of resources are more important in increasing contribution value. The findings suggest that contribution value develops in different patterns for two types of users. Implications for both theory and practice are discussed. 0 0
DartWiki: A semantic wiki for ontology-based knowledge integration in the biomedical domain Domain ontology
Integrated medicine
Knowledge management
Semantic web
Semantic wiki
Traditional chinese medicine
Current Bioinformatics English 2012 Semantic Web languages and technologies can be used for the annotation, classification, and organization of knowledge assets and digital artifacts based on biomedical ontologies. In this paper, we present a semantic wiki, named DartWiki, to build ontology-based digital encyclopedia for the biomedicine domain. DartWiki provides a Web-based interface for accessing knowledge artifacts in both per-artifact and per-concept mode. In the per-artifact mode, users can access these artifacts, and annotate them in both short texts and logical statements in terms of domain ontologies. In the concept-based mode, users can navigate a graph of concepts, and review and edit the synthesized page about a selected concept, which contains meaningful information about the concept, and also its related concepts and artifacts. Smooth transitions between the two modes are achieved through semantic links. As a use case of the DartWiki, we provide an open platform for the management and maintenance of digital artifacts in Integrated Medicine. This system provides medical practitioners with relevant and trustworthy knowledge artifacts, and also means to input artifacts, to clarify their meaning, and to check and improve their quality, which encourages the inclusion and participation of users, and effectively creates an online community around knowledge sharing. 0 0
Semantic Web for current healthcare and bioinformatics Domain ontology
Integrated medicine
Knowledge management
Semantic web
Semantic wiki
Current Bioinformatics English 2012 The decade since the publication of the Semantic Web article in Scientific American in 2001 has witnessed a multitude of novel healthcare and bioinformatics applications that builds upon the open integration capability of the Semantic Web. This theme issue illustrates how the semantically enriched information has both enhanced our knowledge and expanded the impact on biomedical research in terms of scientific knowledge modeling and integration, linked data publication and interlinking, and decision support systems. Five papers have been selected and included, serving as typical examples of Semantic Web adoption in both healthcare and bioinformatics. 0 0
TongKey at entity track TREC 2011: Related entity finding NIST Special Publication English 2011 This paper presents our work done for the TREC 2011 Entity track. A retrieval model was proposed for the task of related entity finding. This model consists of several parts: In order to get more accurate document collection, query analysis method was utilized to format the narrative of each query. Then, our dataset was generated by using ClueWeb09 API2. Moreover, we employed the NER tools and text pattern recognition to extract entities from this processed dataset. In particular, the types of target entities are not so well-defined as last year. Therefore, a specific classifier trained by employing Wikipedia titles and category was utilized to identify the categories of target entities. To find related entity homepages and supporting documents, a set of feature-based methods were applied. 0 0
A multiple-stage framework for related entity finding: FDWIM at TREC 2010 entity track NIST Special Publication English 2010 This paper describes a multiple-stage retrieval framework for the task of related entity finding on TREC 2010 Entity Track. In the document retrieval stage, search engine is used to improve the retrieval accuracy. In the entity extraction and filtering stage, we extract entity with NER tools, Wikipedia and text pattern recognition. Then stoplist and other rules are employed to filter entity. Deep mining of the authority pages is proved to be effective in this stage. In entity ranking stage, many factors including keywords from narrative, page rank, combined results of corpus-based association rules and search engine are considered. In the final stage, an improved feature-based algorithm is proposed for the entity homepage detection. 0 0
Text-based requirements preprocessing using nature language processing techniques Domain ontology
Nature language processing
Non-technical stakeholder
Requirements preprocessing
2010 International Conference on Computer Design and Applications, ICCDA 2010 English 2010 In a distributed environment, non-technical stakeholders are required to write down requirement statements by themselves. Nature language is the first choice for them. In order to alleviate the burden of reading free-text requirement documents by requirements engineers, we extract goals and relevant stakeholders from requirement statements automatically by a computer-assisted way. In this paper, requirements are divided into system level requirements and instance level requirements. Methods are proposed to solve two types of requirements by analyzing the characteristics of requirement expressions, and combining techniques of nature language processing with semantic web. Semantic-enhanced segment and domain sentence pattern are two novel techniques utilized in our methods. Our approach accelerates goal extraction from text-based requirements and alleviates the burden of requirements engineers significantly. 0 0
An investigation into contribution I-intention and we-intention in open web-based encyclopedia: Roles of joint commitment and mutual agreement I-intention
Joint commitment
Knowledge contribution
Mutual agreement
Social cognitive theory
Wiki community
ICIS 2009 Proceedings - Thirtieth International Conference on Information Systems English 2009 In the current study, knowledge contribution in open web-based encyclopedia is conceptualized as a group-referent intentional social action, and we-intention, which reflects one's perception of the group acting as a unit, has been employed. The motivation of this study thus is to better understand antecedents and consequences of contribution I-intention and we-intention in open web-based encyclopedia. A research model was developed and empirically examined with 202 knowledge contributors in two most famous wiki communities in Mainland China. The results demonstrated that personal outcome expectations exert significant effects on both intentions. Joint commitment, mutual agreement and community-related outcome expectations are significantly related to we-intention to contribute, but not related to I-intention. In addition, we-intention has a statistically significant positive effect on contribution behavior. However, I-intention negatively relates to contribution behavior. We believe this study will serve as a starting point for furthering our limited understanding of the intentional social action in knowledge management research. 0 0
Stairs: Towards efficient full-text filtering and dissemination in a DHT environment Proceedings - International Conference on Data Engineering English 2009 Nowadays contents in Internet like weblogs, wikipedia and news sites become "live". How to notify and provide users with the relevant contents becomes a challenge. Unlike conventional Web search technology or the RSS feed, this paper envisions a personalized full-text content filtering and dissemination system in a highly distributed environment such as a Distributed Hash Table (DHT). Users can subscribe to their interested contents by specifying some terms and threshold values for filtering. Then, published contents will be disseminated to the associated subscribers.We propose a novel and simple framework of filter registration and content publication, STAIRS. By the new framework, we propose three algorithms (default forwarding, dynamic forwarding and adaptive forwarding) to reduce the forwarding cost and false dismissal rate; meanwhile, the subscriber can receive the desired contents with no duplicates. In particular, the adaptive forwarding utilizes the filter information to significantly reduce the forwarding cost. Experiments based on two real query logs and two real datasets show the effectiveness of our proposed framework. 0 0
Wiki-based Collaborative Learning: Incorporating Self-Assessment Tasks Computer assisted assessment
Formative assessment
Item model
Wiki-based collaborative learning
WikiSym English 2008 0 1
Wiki-based collaborative learning: Incorporating self-assessment tasks Computer assisted assess-ment
Formative assessment
Item model
Wiki-based collaborative learning
WikiSym 2008 - The 4th International Symposium on Wikis, Proceedings English 2008 When assigning technological articles as the collaborative writing task, students may find that the available knowledge repositories leave little room for them to contribute and therefore write nothing. To provide guidelines for students to discover topics, as well as tools to practice problem solving skills, we integrated a computer assisted assessment module into the Mediawiki and employ self-tests as the collaborative tasks. In these task, item models are used to automatically generate test questions. The items deriving from a same model share a common structure; however, the randomly initialized parameters of the model make them differ from each other. These differences result in that the answers of an item are usually inapplicable to other items deriving from the same model. Therefore, examinees have to solve these generated items on a case by case basis. Further, how to solve questions deriving from certain models can be served as the topics about which students write articles. The wiki self-assessment system was used in a course on Computer Networks offered to junior students majored in computer science. Five self-test tasks were assigned to 98 students, and they were encouraged to write wiki pages to explain their solution methods. Evidence from this preliminary application indicates that the presented approach has a positive effect on learning outcomes. 0 1