Lei Zhang

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Lei Zhang 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
An initial analysis of semantic wikis RDF
Semantic web
Semantic wiki
Social knowledge collection
International Conference on Intelligent User Interfaces, Proceedings IUI English 2013 Semantic wikis augment wikis with semantic properties that can be used to aggregate and query data through reasoning. Semantic wikis are used by many communities, for widely varying purposes such as organizing genomic knowledge, coding software, and tracking environmental data. Although wikis have been analyzed extensively, there has been no published analysis of the use of semantic wikis. We carried out an initial analysis of twenty semantic wikis selected for their diverse characteristics and content. Based on the number of property edits per contributor, we identified several patterns to characterize community behaviors that are common to groups of wikis. 0 0
Evaluating article quality and editor reputation in Wikipedia Editor reputation
Factor graph
Quality evaluation
Communications in Computer and Information Science English 2013 We study a novel problem of quality and reputation evaluation for Wikipedia articles. We propose a difficult and interesting question: How to generate reasonable article quality score and editor reputation in a framework at the same time? In this paper, We propose a dual wing factor graph(DWFG) model, which utilizes the mutual reinforcement between articles and editors to generate article quality and editor reputation. To learn the proposed factor graph model, we further design an efficient algorithm. We conduct experiments to validate the effectiveness of the proposed model. By leveraging the belief propagation between articles and editors, our approach obtains significant improvement over several alternative methods(SVM, LR, PR, CRF). 0 0
The democratization of semantic properties: An analysis of semantic wikis Knowledge capture
Semantic web
Semantic wiki
Social knowledge collection
Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013 English 2013 Semantic wikis augment wikis with semantic properties that can be used to aggregate and query data through reasoning. Semantic wikis are used by many communities, for widely varying purposes such as organizing genomic knowledge, coding software, and tracking environmental data. Although wikis have been analyzed extensively, there has been no published analysis of the use of semantic wikis. In this paper, we analyze twenty semantic wikis selected for their diverse characteristics and content. We analyze the property edits and compare to the total number of edits in the wiki. We also show how semantic properties are created over the lifetime of the wiki. 0 0
A framework to represent and mine knowledge evolution from Wikipedia revisions Expired data detection
Knowledge evolution extraction
Wikipedia revision
WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion English 2012 State-of-the-art knowledge representation in semantic web employs a triple format (subject-relation-object). The limitation is that it can only represent static information, but cannot easily encode revisions of semantic web and knowledge evolution. In reality, knowledge does not stay still but evolves over time. In this paper, we first introduce the concept of "quintuple representation" by adding two new fields, state and time, where state has two values, either in or out, to denote that the referred knowledge takes effective or becomes expired at the given time. We then discuss a twostep statistical framework to mine knowledge evolution into the proposed quintuple representation. Utilizing extracted quintuple properly, it not only can reveal knowledge changing history but also detect expired information. We evaluate the proposed framework on Wikipedia revisions, as well as, common web pages currently not in semantic web format. Copyright is held by the author/owner(s). 0 0
Infinite topic modelling for trend tracking hierarchical dirichlet process approaches with wikipedia semantic based method Hierarchical dirichlet process
News
Temporal analysis
Topic modelling
Wikipedia
KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval English 2012 The current affairs people concern closely vary in different periods and the evolution of trends corresponds to the reports of medias. This paper considers tracking trends by incorporating non-parametric Bayesian approaches with temporal information and presents two topic modelling methods. One utilizes an infinite temporal topic model which obtains the topic distribution over time by placing a time prior when discovering topics dynamically. In order to better organize the event trend, we present another progressive superposed topic model which simulates the whole evolutionary processes of topics, including new topics' generation, stable topics' evolution and old topics' vanishment, via a series of superposed topics distribution generated by hierarchical Dirichlet process. Both of the two approaches aim at solving the real-world task while avoiding Markov assumption and breaking the number limitation of topics. Meanwhile, we employ Wikipedia based semantic background knowledge to improve the discovered topics and their readability. The experiments are carried out on the corpus of BBC news about American Forum. The results demonstrate better organized topics, evolutionary processes of topics over time and model effectiveness. Copyright 0 0
Adoption of social software for collaboration Blogs
Collaboration
IT adoption
Social software
Wiki
Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES'10 English 2010 This doctoral research explores how social software can be used to support work collaboration. A case study approach with mixed methods is adopted in this study. Social network analysis and statistical analysis provide complementary support to qualitative analysis. The UK public sector was chosen as the research context. Users are individuals who are knowledge workers in distributed and cross-boundary groups. The asynchronous social software applications studied are blogs and wikis. This paper first describes the major contributions made in the research findings. Next, it identifies the implications of this study for the adoption theory, mixed methodology and for practice. Finally, having taken into consideration the limitations of the study, some recommendations are proposed for further research. Copyright 0 0
Catriple: Extracting triples from wikipedia categories Lecture Notes in Computer Science English 2008 As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence. 0 0
Semplore: An IR approach to scalable hybrid query of Semantic Web data Lecture Notes in Computer Science English 2007 As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information. 0 0