Jilin Chen

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Jilin Chen is an author.

Publications

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
Exploiting web features in Chinese relation extraction Co-occurrence
Relation extraction
Web feature
Wikipedia
CSAE 2012 - Proceedings, 2012 IEEE International Conference on Computer Science and Automation Engineering English 2012 Relation extraction is a form of information extraction, which finds predefined relations between pairs of entities in text. A Chinese relation extraction approach exploiting web features is proposed. Four web features are extracted from the web and the Wikipedia website. Experiments on the ACE 2005 Corpus show that the web features are effective, and high-quality websites generate more effective features. 0 0
Searching for the goldilocks zone: Trade-offs in managing online volunteer groups Online volunteer group
Productivity
Trade-off
Wikipedia
Withdrawal
English 2012 Dedicated and productive members who actively contribute to community efforts are crucial to the success of online volunteer groups such as Wikipedia. What predicts member productivity? Do productive members stay longer? How does involvement in multiple projects affect member contribution to the community? In this paper, we analyze data from 648 WikiProjects to address these questions. Our results reveal two critical trade-offs in managing online volunteer groups. First, factors that increase member productivity, measured by the number of edits on Wikipedia articles, also increase likelihood of withdrawal from contributing, perhaps due to feelings of mission accomplished or burnout. Second, individual membership in multiple projects has mixed effects. It decreases the amount of work editors contribute to both the individual projects and Wikipedia as a whole. It increases withdrawal for each individual project yet reduces withdrawal from Wikipedia. We discuss how our findings expand existing theories to fit the online context and inform the design of new tools to improve online volunteer work. 0 0
ITEM: Extract and integrate entities from tabular data to RDF knowledge base Entity Extraction
RDF Knowledge Base
Schema Mapping
Lecture Notes in Computer Science English 2011 Many RDF Knowledge Bases are created and enlarged by mining and extracting web data. Hence their data sources are limited to social tagging networks, such as Wikipedia, WordNet, IMDB, etc., and their precision is not guaranteed. In this paper, we propose a new system, ITEM, for extracting and integrating entities from tabular data to RDF knowledge base. ITEM can efficiently compute the schema mapping between a table and a KB, and inject novel entities into the KB. Therefore, ITEM can enlarge and improve RDF KB by employing tabular data, which is assumed of high quality. ITEM detects the schema mapping between table and RDF KB only by tuples, rather than the table's schema information. Experimental results show that our system has high precision and good performance. 0 0
The effects of diversity on group productivity and member withdrawal in online volunteer groups Diversity
Online volunteer group
Performance
Wikipedia
Conference on Human Factors in Computing Systems - Proceedings English 2010 The "wisdom of crowds" argument emphasizes the importance of diversity in online collaborations, such as open source projects and Wikipedia. However, decades of research on diversity in offline work groups have painted an inconclusive picture. On the one hand, the broader range of insights from a diverse group can lead to improved outcomes. On the other hand, individual differences can lead to conflict and diminished performance. In this paper, we examine the effects of group diversity on the amount of work accomplished and on member withdrawal behaviors in the context of WikiProjects. We find that increased diversity in experience with Wikipedia increases group productivity and decreases member withdrawal - up to a point. Beyond that point, group productivity remains high, but members are more likely to withdraw. Strikingly, no such diminishing returns were observed for differences in member interest, which increases productivity and decreases member withdrawal in a linear fashion. Our results suggest that the low visibility of individual differences in online groups may allow them to harvest more of the benefits of diversity while bearing less of the cost. We discuss how our findings can inform further research of online collaboration. 0 0
A web recommender system based on dynamic sampling of user information access behaviors Data mining
Dynamic sampling
Gradual adaption
Information recommendation
Wikipedia
Proceedings - IEEE 9th International Conference on Computer and Information Technology, CIT 2009 English 2009 In this study, we propose a Gradual Adaption Model for a Web recommender system. This model is used to track users' focus of interests and its transition by analyzing their information access behaviors, and recommend appropriate information. A set of concept classes are extracted from Wikipedia. The pages accessed by users are classified by the concept classes, and grouped into three terms of short, medium and long periods, and two categories of remarkable and exceptional for each concept class, which are used to describe users' focus of interests, and to establish reuse probability of each concept class in each term for each user by Full Bayesian Estimation as well. According to the reuse probability and period, the information that a user is likely to be interested in is recommended. In this paper, we propose a new approach by which short and medium periods are determined based on dynamic sampling of user information access behaviors. We further present experimental simulation results, and show the validity and effectiveness of the proposed system. 0 0
An approach to deep web crawling by sampling Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 English 2008 Crawling deep web is the process of collecting data from search interfaces by issuing queries. With wide availability of programmable interface encoded in web services, deep web crawling has received a large variety of applications. One of the major challenges crawling deep web is the selection of the queries so that most of the data can be retrieved at a low cost. We propose a general method in this regard. In order to minimize the duplicates retrieved, we reduced the problem of selecting an optimal set of queries from a sample of the data source into the well-known set-covering problem and adopt a classical algorithm to resolve it. To verify that the queries selected from a sample also produce a good result for the entire data source, we carried out a set of experiments on large corpora including Wikipedia and Reuters. We show that our sampling-based method is effective by empirically proving that 1) The queries selected from samples can harvest most of the data in the original database; 2) The queries with low overlapping rate in samples will also result in a low overlapping rate in the original database; and 3) The size of the sample and the size of the terms from where to select the queries do not need to be very large. 0 0
Using semantic wikis for user information management in e-business Artificial psychology
E-business
Semantic wiki
User information management
Web personalization
2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop Proceedings, KAM 2008 English 2008 The application of semantic wikis for user information management in e-business for purpose of web personalization is investigated in this paper. It presents an enhanced semantic wiki, named UIMwiki, aiming to combine user annotation and artificial psychology in a semantic wiki system. Wiki users in UIMwiki are classified into three groups: end users, expert users and ontologists. There are two ways to maintain user information in UIMwiki for an end user: executing a cognitive query or adding semantic annotations. A user model based on artificial psychology can be built from the cognitive query files by a user model generator. The user model generator gives user model suggestions based on psychology knowledge. The Wiki generator and semantic annotation are also described. UIMwiki considers user's preference information or implicit knowledge in the annotation process, and it makes the personalization more effectively too. 0 0
Creating, Destroying, and Restoring Value in Wikipedia Wikipedia Department of Computer Science and Engineering University of Minnesota 2007 Wikipedia's brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that an overwhelming majority of the viewed words were written by frequent editors and that this majority is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings. 0 12
Creating, destroying, and restoring value in Wikipedia English 2007 Wikipedia's brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that an overwhelming majority of the viewed words were written by frequent editors and that this majority is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings. 0 12