We propose an approach to index raster images of dictionary pages which in turn would require very little manual effort to enable direct access to the appropriate pages of the dictionary for lookup. Accessibility is further improved by feedback and crowdsourcing that enables highlighting of the specific location on the page where the lookup word is found, annotation, digitization, and fielded searching. This approach is equally applicable on simple scripts as well as complex writing systems. Using our proposed approach, we have built a Web application called "Dictionary Explorer" which supports word indexes in various languages and every language can have multiple dictionaries associated with it. Word lookup gives direct access to appropriate pages of all the dictionaries of that language simultaneously. The application has exploration features like searching, pagination, and navigating the word index through a tree-like interface. The application also supports feedback, annotation, and digitization features. Apart from the scanned images, "Dictionary Explorer" aggregates results from various sources and user contributions in Unicode. We have evaluated the time required for indexing dictionaries of different sizes and complexities in the Urdu language and examined various trade-offs in our implementation. Using our approach, a single person can make a dictionary of 1,000 pages searchable in less than an hour.
HathiTrust @cite_48 is a collaborative repository of digital content from various research institutions and libraries to preserve cultural records and to ensure accessibility and availability of the preserved content in future.
@cite_48
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Academic libraries are increasingly looking to provide services that allow their users to work with digital collections in innovative ways, for example, to analyze large volumes of digitized collections. The HathiTrust Research Center (HTRC) is a large collaborative that provides an innovative research infrastructure for dealing with massive amounts of digital texts. In this poster, we report on the technical progress of the HTRC as well as on the efforts to build a user community around our cyberinfrastructure. In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45 (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28 of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems. This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and CiteSeer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios. Some large scale topical digital libraries, such as CiteSeer, harvest online academic documents by crawling open-access archives, university and author homepages, and authors' self-submissions. While these approaches have so far built reasonable size libraries, they can suffer from having only a portion of the documents from specific publishing venues. We propose to use alternative online resources and techniques that maximally exploit other resources to build the complete document collection of any given publication venue. We investigate the feasibility of using publication metadata to guide the crawler towards authors' homepages to harvest what is missing from a digital library collection. We collect a real-world dataset from two Computer Science publishing venues, involving a total of 593 unique authors over a time frame of 1998 to 2004. We then identify the missing papers that are not indexed by CiteSeer. Using a fully automatic heuristic-based system that has the capability of locating authors' homepages and then using focused crawling to download the desired papers, we demonstrate that it is practical to harvest using a focused crawler academic papers that are missing from our digital library. Our harvester achieves a performance with an average recall level of 0.82 overall and 0.75 for those missing documents. Evaluation of the crawler's performance based on the harvest rate shows definite advantages over other crawling approaches and consistently outperforms a defined baseline crawler on a number of measures In the event of emergencies and disasters, massive amounts of web resources are generated and shared. Due to the rapidly changing nature of those resources, it is important to start archiving them as soon as a disaster occurs. This led us to develop a prototype system for constructing archives with minimum human intervention using the seed URLs extracted from tweet collections. We present the details of our prototype system. We applied it to five tweet collections that had been developed in advance, for evaluation. We also identify five categories of non- relevant files and conclude with a discussion of findings from the evaluation. The CiteSeer x digital library stores and indexes research articles in Computer Science and related fields. Although its main purpose is to make it easier for researchers to search for scientific information, CiteSeer x has been proven as a powerful resource in many data mining, machine learning and information retrieval applications that use rich metadata, e.g., titles, abstracts, authors, venues, references lists, etc. The metadata extraction in CiteSeer x is done using automated techniques. Although fairly accurate, these techniques still result in noisy metadata. Since the performance of models trained on these data highly depends on the quality of the data, we propose an approach to CiteSeer x metadata cleaning that incorporates information from an external data source. The result is a subset of CiteSeer x , which is substantially cleaner than the entire set. Our goal is to make the new dataset available to the research community to facilitate future work in Information Retrieval. LOCKSS (Lots Of Copies Keep Stuff Safe) is a tool designed for libraries to use to ensure their community's continued access to web-published scientific journals. LOCKSS allows libraries to take custody of the material to which they subscribe, in the same way they do for paper, and to preserve it. By preserving it they ensure that, for their community, links and searches continue to resolve to the published material even if it is no longer available from the publisher. Think of it as the digital equivalent of stacks where an authoritative copy of material is always available rather than the digital equivalent of an archive. LOCKSS allows libraries to run web caches for specific journals. These caches collect content as it is published and are never flushed. They cooperate in a peer-to-peer network to detect and repair damaged or missing pages. The caches run on generic PC hardware using open-source software and require almost no skilled administration, making the cost of preserving a journal manageable. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Representing the semantics of unstructured scientific publications will certainly facilitate access and search and hopefully lead to new discoveries. However, current digital libraries are usually limited to classic flat structured metadata even for scientific publications that potentially contain rich semantic metadata. In addition, how to search the scientific literature of linked semantic metadata is an open problem. We have developed a semantic digital library oreChem ChemxSeer that models chemistry papers with semantic metadata. It stores and indexes extracted metadata from a chemistry paper repository Chemx Seer using "compound objects". We use the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) (http: www.openarchives.org ore standard to define a compound object that aggregates metadata fields related to a digital object. Aggregated metadata can be managed and retrieved easily as one unit resulting in improved ease-of-use and has the potential to improve the semantic interpretation of shared data. We show how metadata can be extracted from documents and aggregated using OAI-ORE. ORE objects are created on demand; thus, we are able to search for a set of linked metadata with one query. We were also able to model new types of metadata easily. For example, chemists are especially interested in finding information related to experiments in documents. We show how paragraphs containing experiment information in chemistry papers can be extracted and tagged based on a chemistry ontology with 470 classes, and then represented in ORE along with other document-related metadata. Our algorithm uses a classifier with features that are words that are typically only used to describe experiments, such as "apparatus", "prepare", etc. Using a dataset comprised of documents from the Royal Society of Chemistry digital library, we show that the our proposed methodperforms well in extracting experiment-related paragraphs from chemistry documents. We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. This paper presents a research dataset of historical newspapers comprising over 500 page images, uniquely representative of European cultural heritage from the digitization projects of 12 national and major European libraries, created within the scope of the large-scale digitisation Europeana Newspapers Project (ENP). Every image is accompanied by comprehensive ground truth (Unicode encoded full-text, layout information with precise region outlines, type labels, and reading order) in PAGE format and searchable metadata about document characteristics and artefacts. The first part of the paper describes the nature of the dataset, how it was built, and the challenges encountered. In the second part, a baseline for two state-of-the-art OCR systems (ABBYY FineReader Engine 11 and Tesseract 3.03) is given with regard to both text recognition and segmentation layout analysis performance. Background: Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. Methodology: Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute’s Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. Conclusions: Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data. The World Wide Web is becoming a source of information for researchers, who are more aware of the possibilities for collections of Internet content as resources. Some have begun creating archives of web content for social science and humanities research. However, there is a growing gulf between policies shared between global and national institutions creating web archives and the practices of researchers making use of the archives. Each set of stakeholders finds the others’ web archiving contributions less applicable to their own field. Institutions find the contributions of researchers to be too narrow to meet the needs of the institution’s audience, and researchers find the contributions of institutions to be too broad to meet the needs of their research methods. Resources are extended to advance both institutional and researcher tools, but the gulf between the two is persistent. Institutions generally produce web archives that are broad in scope but with limited access and enrichment tools. The design of common access interfaces, such as the Internet Archive’s Wayback Machine, limit access points to archives to only URL and date. This narrow access limits the ways in which web archives can be valuable for exploring research questions in the humanities and social sciences. Individual scholars, in catering to their own disciplinary and methodological needs, produce web archives that are narrow in scope, and whose access and enrichment tools are personalized to work within the boundaries of the project for which the web archive was built. There is no way to explore a subset of an archive by topic, event, or idea. The current search paradigm in web archiving access tools is built primarily on retrieval, not discovery. We suggest that there is a need for extensible tools to enhance access to and enrichment of web archives to make them more readily reusable and so, more valuable for both institutions and researchers, and that annotation activities can serve as one potential guide for development of such tools to bridge the divide. Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering. While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task. In this paper, we address the problem of designing a scalable, accurate query processor for peer-to-peer filesharing and similar distributed keyword search systems. Using a globally-distributed monitoring infrastructure, we perform an extensive study of the Gnutella filesharing network, characterizing its topology, data and query workloads. We observe that Gnutella's query processing approach performs well for popular content, but quite poorly for rare items with few replicas. We then consider an alternate approach based on Distributed Hash Tables (DHTs). We describe our implementation of PIERSearch, a DHT-based system, and propose a hybrid system where Gnutella is used to locate popular items, and PIERSearch for handling rare items. We develop an analytical model of the two approaches, and use it in concert with our Gnutella traces to study the trade-off between query recall and system overhead of the hybrid system. We evaluate a variety of localized schemes for identifying items that are rare and worth handling via the DHT. Lastly, we show in a live deployment on fifty nodes on two continents that it nicely complements Gnutella in its ability to handle rare items. Recently the Library of Congress began developing a strategy for the preservation of digital content. Efforts have focused on the need to select, harvest, describe, access and preserve Web resources. This poster focuses on the Library's initial investigation and evaluation of Web harvesting software tools. A motion picture film which includes a row of sprocket holes along one marginal edge portion and a longitudinally extending area carrying a photo-sensitive layer for photographing a selected scene, or series of scenes in a succession of stationary frames, the film being characterized in that one of its marginal edge portions carries a latent-image numerical identification of each of the successive frames in binary code form adjacent each said frame. A post-production film-making system is also described with means for transferring the binary numbers from the film to a magnetic medium and means for reading and visually displaying in arabic form the numbers on the film and the magnetic medium. Two kinds of bibliographic tools are used to retrieve scientific publications and make them available online. For one kind, access is free as they store information made publicly available online. For the other kind, access fees are required as they are compiled on information provided by the major publishers of scientific literature. The former can easily be interfered with, but it is generally assumed that the latter guarantee the integrity of the data they sell. Unfortunately, duplicate and fake publications are appearing in scientific conferences and, as a result, in the bibliographic services. We demonstrate a software method of detecting these duplicate and fake publications. Both the free services (such as Google Scholar and DBLP) and the charged-for services (such as IEEE Xplore) accept and index these publications.