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2007: gen-apr/mag-ago/set-dic · 2008: gen-apr/mag-ago/set-dic · 2009 · Scientific articles
Sorted: The Register
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- In traditional text classification approaches, the semantic meanings of the classes are described by the labeled documents. Since labeling documents is often time consuming and expensive, it; is a promising idea that asking users to provide some keywords to depict the classes, instead of labeling any documents. However, short pieces of keywords may not contain enough information and therefore may lead to unreliable classifier. Fortunately, there are large amount of public data easily available in web directories, such as ODP, Wikipedia, etc. We are interested in exploring the enormous crowd intelligence contained in such public data to enhance text classification. In this paper, we propose a novel text; classification framework called "Knowledge Supervised Learning" (KSL), which utilizes the knowledge in keywords and the crowd intelligence to learn the classifier without any labeled documents. We design a two-stage risk minimization (TSRM) approach for the KSL problem. It can optimize the expected prediction risk and build the high quality classifier. Empirical results verify our claim: our algorithm can achieve above 0.9 on Micro-F1 on average, which is much better than baselines and even comparable against SVM classifier supervised by labeled documents.
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- Information systems researchers that apply critical social perspectives frequently emphasize the potential for information technology to serve as a mechanism for increased rationalization, domination, and control. Such theorists often overlook or discount the liberating aspects of information systems. In this study, we apply the ideal of rational discourse developed by Jurgen Habermas to the phenomenon of Wikipedia in an effort to explore empirically the emancipatory potential of information systems. We contend that Wikipedia embodies an approximation of the necessary conditions for rational discourse. While several challenges persist, the example of Wikipedia illustrates the positive potential of information systems in supporting the emergence of more emancipatory forms of communication. The corresponding implications for researchers and design professionals alike are discussed.
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- The World Wide Web continues to grow closer to achieving the vision of becoming the repository of all human knowledge, as features and applications that support user-generated content become more prevalent. Wikipedia is fast becoming an important resource for news and information. It is an online information source that is increasingly used as the first, and sometimes only, stop for online encyclopedic information. Using a method employed by Tankard and Royal to judge completeness of Web content, completeness of information on Wikipedia is assessed. Some topics are covered more comprehensively than others, and the predictors of these biases include recency, importance, population, and financial wealth. Wikipedia is more a socially produced document than a value-free information source. It reflects the viewpoints, interests, and emphases of the people who use it.
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- Named entity (NE) translation plays an important role in many applications, such as information retrieval and machine translation, In this paper, we focus on translating NEs from Korean to Chinese in order to improve Korean-Chinese cross-language information retrieval (KCIR). The ideographic nature of Chinese makes NE translation difficult because one syllable may map to several Chinese characters. We propose a hybrid NE translation system. First, we integrate two online databases to extend the coverage of our bilingual dictionaries. We use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. We also use Naver.com's people search engine to find a query name's Chinese or English translation. The second component of our system is able to learn Korean-Chinese (K-C), Korean-English (K-E), and English-Chinese (E-C) translation patterns from the web. These patterns can be used to extract K-C, K-E and E-C pairs from Google snippets. We found KCIR performance using this hybrid configuration over five times better than that a dictionary-based configuration using only Naver people search. Mean average precision was as high as 0.3385 and recall reached 0.7578. Our method can handle Chinese, Japanese. Korean. and non-CJK NE translation and improve performance of KCIR substantially. (C) 2008 Elsevier Ltd. All rights reserved.
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- HARDY, C. R. AND N. W. HARDY (James C. Parks Herbarium, Departments of Biology and Computer Science, Millersville University, Millersville, PA 17551). Simple biodiversity mashups for non-tech-savvy biologists: a demonstration using the liana flora of Pennsylvania, USA. J. Torrey Bot. Soc. 135: 585-594. 2008.-A step-by-step procedure is described and demonstrated using the liana flora of Pennsylvania, USA, for the creation of simple biodiversity informatics applications in the form of Web-mashups. The target audience is non-tech-savvy biologists. For each liana species, the Web-based Liana Atlas of Pennsylvania creates Species Pages by aggregating information or direct links to information on taxonomy, distribution, morphology, genetics, its well as photographs and general encyclopedic or bibliographic information, "live" from third-party websites including the Global Biodiversity Information Facility, the USDA PLANTS database, Wikipedia (R), and GenBank (R). The result is an original, in formation-rich, fully illustrated application providing more up-to-date information on Pennsylvanian lianas in one place than had previously been available elsewhere. More importantly, the procedure described and files available at the demonstration website are portable and customizable to many other biogeographic regions or taxa, and its application requires only basic knowledge of Web navigation and text editors. We hope that this procedure will encourage others, including biologists with limited IT knowledge or at smaller institutions with limited resources, to contribute their own informatics applications valuable to local or regional interest-groups such as educators, students, and amateurs.
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- Many realistic user tasks involve the retrieval of specific entities instead of just any type of documents. Examples of information needs include 'Countries where one can pay with the euro' or 'Impressionist art museums in The Netherlands'. The Initiative for Evaluation of XML Retrieval (INEX) started the XML Entity Ranking track (INEX-XER) to create a test collection for entity retrieval in Wikipedia. Entities are assumed to correspond to Wikipedia entries. The goal of the track is to evaluate how well systems can rank entities in response to a query; the set of entities to be ranked is assumed to be loosely defined either by a generic category (entity ranking) or by some example entities (list completion). This track overview introduces the track setup, and discusses the implications of the new relevance notion for entity ranking in comparison to ad hoc retrieval.
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- Entity ranking on Web scale datasets is still an open challenge. Several resources, as for example Wikipedia-based ontologies, can be used to improve the quality of the entity ranking produced by a system. In this paper we focus on the Wikipedia corpus and propose algorithms for finding entities based on query relaxation using category information. The main contribution is a methodology for expanding the user query by exploiting the semantic structure of the dataset. Our approach focuses on constructing queries using not only keywords from the topic, but also information about relevant categories. This is done leveraging on a highly accurate ontology which is matched to the character strings of the topic. The evaluation is performed using the INEX 2007 Wikipedia collection and entity ranking topics. The results show that our approach performs effectively, especially for early precision metrics.
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- This paper describes the participation of the INRIA group in the INEX 2007 XML entity ranking and ad hoc tracks. We developed a system for ranking Wikipedia entities in answer to a query. Our approach utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the examples (when provided) to improve the effectiveness of entity ranking. Our experiments on both the training and the testing data sets demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity retrieval effectiveness. We also use our system for the ad hoc tasks by inferring target categories from the title of the query. The results were worse than when using a full-text search engine, which confirms our hypothesis that ad hoc retrieval and entity retrieval are two different tasks.
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- The purpose of this research is to examine how search differs according to selected task variables. Three types of task information goals and two types of task structures were explored. This mixed within- and between-subjects designed study had 96 participants complete three of 12 tasks in a laboratory setting using a specialized search system based on Lucene. Using a combination metrics (user perception collected by questionnaires, transaction log data, and characteristics of relevant documents), we assessed the effect of goals and structure on search as demonstrated through queries and their use in interactive searching.
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- Wikipedia is becoming ever more popular. Linking between documents is typically provided in similar environments in order to achieve collaborative knowledge sharing. However, this functionality in Wikipedia is not integrated into the document creation process and the quality of automatically generated links has never been quantified. The Link the Wiki (LTW) track at INEX in 2007 aimed at producing a standard procedure, metrics and a discussion forum for the evaluation of link discovery. The tasks offered by the LTW track as well as its evaluation present considerable research challenges. This paper briefly described the LTW task and the procedure of evaluation used at LTW track in 2007. Automated link discovery methods used by participants are outlined. An overview of the evaluation results is concisely presented and further experiments are reported.
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- In this paper, we document our efforts at INEX 2007 where we participated in the Ad Hoc Track, the Link the Wiki Track, and the Interactive Track that continued from INEX 2006. Our main aims at INEX 2007 were the following. For the Ad Hoc Track, we investigated the effectiveness of incorporating link evidence into the model, and of a CAS filtering method exploiting the structural hints in the INEX topics. For the Link the Wiki Track, we investigated the relative effectiveness of link detection based on retrieving similar documents with the Vector Space Model, and then filter with the names of Wikipedia articles to establish a link. For the Interactive Track, we took part in the interactive experiment comparing an element retrieval system with a passage retrieval system. The main results are the following. For the Ad Hoc Track, we see that link priors improve most of our runs for the Relevant in Context and Best in Context Tasks, and that CAS pool filtering is effective for the Relevant in Context and Best in Context Tasks. For the Link the Wiki Track, the results show that detecting links with name matching works relatively well, though links were generally under-generated, which hurt the performance. For the Interactive Track, our test-persons showed a weak preference for the element retrieval system over the passage retrieval system.
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- Ad hoc passage retrieval within the Wikipedia is examined in the context of INEX 2007. An analysis of the INEX 2006 assessments suggests that fixed sized window of about 300 terms is consistently seen and that this might be a good retrieval strategy. In runs submitted to INEX, potentially relevant documents were identified using BM25 (trained on INEX 2006 data). For each potentially relevant document the location of every search term was identified and the center (mean) located. A fixed sized window was then centered on this location. A method of removing outliers was examined in which all terms occurring outside one standard deviation of the center were considered outliers and the center recomputed without them. Both techniques were examined with and without stemming. For Wikipedia linking we identified terms within the document that were over-represented and from the top few generated queries of different lengths. A BM25 ranking search engine was used to identify potentially relevant documents. Links from the source document to the potentially relevant documents (and back) were constructed (at a granularity of whole document). The best performing run used the 4 most over-represented search terms to retrieve 200 documents, and the next 4 to retrieve 50 more.
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- Modern information systems, and web information systems in particular, are faced with frequent database schema changes, which generate the necessity to manage them and preserve the schema, evolution history. In this paper, we describe the Panta Rhei Framework designed to provide powerful tools that: (i) facilitate schema, evolution and guide the Database Administrator in planning and evaluating changes; (ii) support. automatic rewriting of legacy queries against the current schema version, (iii) enable efficient archiving of the histories of data and metadata, and (iv) support complex temporal queries over such histories. We then introduce the Historical Metadata, Manager (HMM), a tool designed to facilitate the process of documenting and querying the schema evolution itself. We use the schema history of the Wikipedia database as a telling example of the many uses and benefits of HMM.
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- The online encyclopedia Wikipedia has become one of the most important online references in the world and has a substantial and growing scientific content. A search of Google with many RNA-related keywords identifies a Wikipedia article as the top hit. We believe that the RNA community has an important and timely opportunity to maximize the content and quality of RNA information in Wikipedia. To this end, we have formed the RNA WikiProject (http://en.wikipedia.org/wiki/Wikipedia: WikiProject_RNA) as part of the larger Molecular and Cellular Biology WikiProject. We have created over 600 new Wikipedia articles describing families of noncoding RNAs based on the Rfam database, and invite the community to update, edit, and correct these articles. The Rfam database now redistributes this Wikipedia content as the primary textual annotation of its RNA families. Users can, therefore, for the first time, directly edit the content of one of the major RNA databases. We believe that this Wikipedia/Rfam link acts as a functioning model for incorporating community annotation into molecular biology databases.
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- With the advent of Web 2.0 technologies, user-edited online resources such as Wikipedia are increasingly tapped for information. However, there is little research on the quality of healthy information found in Wikipedia. OBJECTIVE: To compare the scope, completeness, and accuracy of drug information in Wikipedia with that of a free, online, traditionally edited database (Medscape Drug Reference [MDR]). METHODS: Wikipedia and MDR were assessed on 8 categories of drug information. Questions were constructed and answers were verified with authoritative resources . Wikipedia and MDR were evaluated according to scope (breadth of coverage) and completeness. Accuracy was tracked by factual errors and errors of omission. Descriptive statistics were used to summarize the components. Fisher's exact test was used to compare scope and paired Student's t-test was used to compare current results in Wikipedia with entries 90 days prior to the current access. RESULTS: Wikipedia was able to answer significantly fewer drug information questions (40.0%) compared with MDR (82.5%; P < 0.001). Wikipedia performed poorly regarding information on dosing, with a score of 0% versus the MDR score of 90.0%. Answers found in Wikipedia were 76.0% complete, while MDR provided answers that were 95.5% complete; overall, Wikipedia answers were less complete than those in Medscape (p<0.001). No factual errors were found in Wikipedia, whereas 4 answers in Medscape conflicted with the answer key; errors of omission were higher in Wikipedia (n = 48) than in MDR (n = 14). There was a marked improvement in Wikipedia over time, as current entries were superior to those 90 days prior (p=0.024). CONCLUSIONS: Wikipedia has a more narrow scope, is less complete, and has more errors of omission than the comparator database. Wikipedia may be useful point of engagement for consumers, but is not authoritative and should only be a supplemental source of drug information.
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- One of the major challenges in Peer-to-Peer (P2P) file sharing systems is to support content-based search. Although there have been some proposals to address this challenge, they share the same weakness of using either servers or super-peers to keep global knowledge, which is required to identify importance of terms to avoid popular terms in query processing. As a result, they are not scalable and are prone to the bottleneck problem, which is caused by the high visiting load at the global knowledge maintainers. To that end, in this paper, we propose a novel adaptive indexing approach for content-based search in P2P systems, which can identify importance of terms without keeping global knowledge. Our method is based on an adaptive indexing structure that combines a Chord ring and a balanced tree. The tree is used to aggregate and classify terms adaptively, while the Chord ring is used to index terms of nodes in the tree. Specifically, at each node of the tree, the system classifies terms as either important or unimportant. Important terms, which can distinguish the node from its neighbor nodes, are indexed in the Chord ring. On the other hand, unimportant terms, which are either popular or rare terms, are aggregated to higher level nodes. Such classification enables the system to process queries on the fly without the need for global knowledge. Besides, compared to the methods that index terms separately, term aggregation reduces the indexing cost significantly. Taking advantage of the tree structure, we also develop an efficient search algorithm to tackle the bottleneck problem near the root. Finally, our extensive experiments on both benchmark and Wikipedia datasets validated the effectiveness and efficiency of the proposed method. (c) 2008 Published by Elsevier B.V.
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- Web 2.0 has enabled end users to collaborate through their own developed artifacts, moving on from text (e.g., Wikipedia, Blogs) to images (e.g., Flickr) and movies (e.g., YouTube), changing end-user's role from consumer to producer. But still there is no support for collaboration through interactive end-user developed artifacts, especially for emerging handheld devices, which are the next collaborative platform. Featuring fast always-on networks, Web browsers that are as powerful as their desktop counterparts, and innovative user interfaces, the newest generation of handheld devices can run highly interactive content as Web applications. We have created Ristretto (Mobile), a Web-compliant framework for running end-user developed applications on handheld devices. The Web-based Ristretto Mobile includes compiler and runtime components to turn end-user applications into Web applications that can run on compatible handheld devices, including the Apple iPhone and Nokia N800. Our paper reports on the technological and cognitive challenges in creating interactive content that runs efficiently and is user accessible on handheld devices.
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- This paper deals with collective consumer resistance - that is, organised forms of reaction against the market. In detail, the paper describes BookCrossing (BC) as an alternative mode of book exchange. This is a moderate form of resistance: BC members do not pursue radical, extremist or extreme forms of reaction. Moreover, they act in an alternative way (gift-giving) compared to the traditional market exchange system. They criticise the market system and their activity presents some interesting 'transformative' properties. Not so paradoxically, these forms of moderate resistance in some way support the market in order to meet new needs: solidarity, democracy, consumers' emancipation. Many contemporary communities (e.g. Wayn, Wikipedia, Couchsurfing) seem to possess traits in common with BookCrossing. It is certain that they are not aimed at market subversion: they are aimed at using the market in a different way, in order to compensate for some of its drawbacks. Taken together, these communities collect millions of people around the world, and their role in the emancipation of the consumer society is growing rapidly.
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- The fifth QA campaign at CLEF [1], having its first edition in 2003, offered not only a main task but an Answer Validation Exercise (AVE) [2], which continued last year's pilot, and a new pilot: the Question Answering on Speech Transcripts (QAST) [3, 15]. The main task was characterized by the focus on cross-linguality, while covering as many European languages as possible. As novelty, some QA pairs were grouped in clusters. Every cluster was characterized by a topic (not given to participants). The questions from a cluster possibly contain co-references between one of them and the others. Finally, the need for searching answers in web formats was satisfied by introducing Wikipedia(1) as document corpus. The results and the analyses reported by the participants suggest that the introduction of Wikipedia and the topic related questions led to a drop in systems' performance.
