Wikipedia Data Research Articles

Generating metadata for cyberlearning resources through information retrieval and meta‐search

The goal of this study was to propose novel cyberlearning resource‐based scientific referential metadata for an assortment of publications and scientific topics, in order to enhance the learning experiences of students and scholars in a cyberinfrastructure‐enabled learning environment. By using information retrieval and meta‐search approaches, different types of referential metadata, such as related Wikipedia pages, data sets, source code, video lectures, presentation slides, and (online) tutorials for scientific publications and scientific topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment to validate the quality of the metadata for each scientific keyword and publication and resource‐ranking algorithm. Evaluation results show that the cyberlearning referential metadata retrieved via meta‐search and statistical relevance ranking can help students better understand the essence of scientific keywords and publications.

Rules and Roles vs. Consensus

Deliberative mass collaboration systems, such as Wikipedia, are characterized as undisciplined, unstructured social spaces where individuals participate in collective action. However, examination of Wikipedia reveals that it contains a bureaucratic structure, which ensures that collective goals are primary drivers of that collective action. To support large-scale activity, deliberative mass collaboration systems must provide ways of reconciling the tension between individual agency and collective goals. Wikipedia’s unusual policy, ignore all rules (IAR), serves as this tension release mechanism. IAR supports individual agency when positions taken by participants might conflict with those reflected in established rules. Hypotheses are tested with Wikipedia data regarding individual agency, bureaucratic processes, and IAR invocation during the content exclusion process. Findings indicate that in Wikipedia each utterance matters in deliberations, rules matter in deliberations, and IAR citation magnifies individual influence but also reinforces bureaucracy.

EachWiki

Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.

Implementing crowdsourcing-based relevance experimentation: an industrial perspective

Crowdsourcing has emerged as a viable platform for conducting different types of relevance evaluation. The main reason behind this trend is that it makes possible to conduct experiments extremely fast, with good results at a low cost. However, like in any experiment, there are several implementation details that would make an experiment work or fail. To gather useful results, clear instructions, user interface guidelines, content quality, inter-rater agreement metrics, work quality, and worker feedback are important characteristics of a successful crowdsourcing experiment. Furthermore, designing and implementing experiments that require thousands or millions of labels is different than conducting small scale research investigations. In this paper we outline a framework for conducting continuous crowdsourcing experiments, emphasizing aspects that should be of importance for all sorts of tasks. We illustrate the value of characteristics that can impact the overall outcome using examples based on TREC, INEX, and Wikipedia data sets.

Human dynamics analysis in online collaborative writing

Investigating the human online behavior has become a central issue for understanding human dynamics in recent years. In this paper we analyze the temporal and content-updating statistical properties of online collaborative writing based on Wikipedia data. Online collaborative writing is one of the important and widespread human online behaviors, which is of great apphication. Empirical result shows that the distribution of inter-event time in collaborative writing is on the multi-scale. That is to say, two time intervals that range from 1 min to 30 min and 30 min to 24 h both obey power-law distribution with exponents equal to 1.62 and 1.16 respectively, while the interval larger than 24 h obeys a distribution whose cumulative form is F()－b－alog(). More investigatons show successive updating behavior and mutual updating behavior working together to lead to the multi-scale distribution of inter-event time. Successive updating behavior leads to the power-law distribution with an exponent 1.62 of interval within 30 min while mutual updating behavior leads to the power-law distribution with an exponent 1.16 of interval ranging from 30 min to 24 h. Furthermore, we find that reverse updating repeats frequently in collaborative writing. The proportions of reversing updating and the updating size are strongly relatively reflect that the updating size is a main reason leading to the relevant content to be preserved. The bigger the updating size, the harder it would be preserved. More statistical analyses imply that watching dog and edit war exist in Wikipedia editing. Those results are very helpful to deepen the understanding of the human collective behavior, especially of the collaborative developing behavior.

A Unique Perspective on Data Coding and Decoding

The concept of a loss-less data compression coding method is proposed, and a detailed description of each of its steps follows. Using the Calgary Corpus and Wikipedia data as the experimental samples and compared with existing algorithms, like PAQ or PPMstr, the new coding method could not only compress the source data, but also further re-compress the data produced by the other compression algorithms. The final files are smaller, and by comparison with the original compression ratio, at least 1% redundancy could be eliminated. The new method is simple and easy to realize. Its theoretical foundation is currently under study. The corresponding Matlab source code is provided in the Appendix.

BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs

Dynamic authority-based keyword search algorithms, such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases, and the Web. Conceptually, these algorithms require a query-time PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of precomputed results for some or all keywords involves very expensive preprocessing. We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. We materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the subgraphs. BinRank generates the subgraphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores. The intuition is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing high-quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 10^8 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Our experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank.

Modeling user reputation in wikis

AbstractCollaborative systems available on the Web allow millions of users to share information through a growing collection of tools and platforms such as wikis, blogs, and shared forums. By their very nature, these systems contain resources and information with different quality levels. The open nature of these systems, however, makes it difficult for users to determine the quality of the available information and the reputation of its providers. Here, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user's reputation. Finally, we validate these models using ground‐truth Wikipedia data associated with vandals and administrators. When used as a classifier, the best model produces an area under the receiver operating characteristic (ROC) curve (AUC) of 0.98. Furthermore, we assess the reputation predictions generated by the models on other users, and show that all three models can be used efficiently for predicting user behavior in Wikipedia. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 126‐139, 2010

On the Design and Exploitation of User′s Personal and Public Information for Semantic Personal Digital Photograph Annotation

Automating the process of semantic annotation of digital personal photographs is a crucial step towards efficient and effective management of this increasingly high volume of content. However, this is still a highly challenging task for the research community. This paper proposes a novel solution. Our solution integrates all contextual information available to and from the users, such as their daily emails, schedules, chat archives, web browsing histories, documents, online news, Wikipedia data, and so forth. We then analyze this information and extract important semantic terms, using them as semantic keyword suggestions for their photos. Those keywords are in the form of named entities, such as names of people, organizations, locations, and date/time as well as high frequency terms. Experiments conducted with 10 subjects and a total of 313 photos proved that our proposed approach can significantly help users with the annotation process. We achieved a 33% gain in annotation time as compared to manual annotation. We also obtained very positive results in the accuracy rate of our suggested keywords.

Analyzing and visualizing the semantic coverage of Wikipedia and its authors

AbstractThis article presents a novel analysis and visualization of English Wikipedia data. Our specific interest is the analysis of basic statistics, the identification of the semantic structure and the age of the categories in this free online encyclopedia, and the content coverage of its highly productive authors. © 2007 Wiley Periodicals, Inc. Complexity: 12: 30–40, 2007