Search (86 results, page 2 of 5)

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Abstract

The emerging network environment brings access to an increasing population of heterogeneous repositories. Inevitably, these, have quite diverse metadata vocabularies (categorization codes, classification numbers, index and thesaurus terms). So, necessarily, the number of metadata vocabularies that are accessible but unfamiliar for any individual searcher is increasing steeply. When an unfamiliar metadata vocabulary is encountered, how is a searcher to know which codes or terms will lead to what is wanted? This paper reports work at the University of California, Berkeley, on the design and development of English language indexes to metadata vocabularies. Further details and the current status of the work can be found at the project website http://www.sims.berkeley.edu/research/metadata/

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Abstract

We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.

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Abstract

Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.

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Abstract

The Digital Public Library of America brings together the riches of America's libraries, archives, and museums, and makes them freely available to the world. In order to do this, DPLA has had to build elements of the national digital platform to connect to those institutions and to serve their digitized materials to audiences. In this article, we detail the construction of two critical elements of our work: the decentralized national network of "hubs," which operate in states across the country; and a version of the Hydra repository software that is tailored to the needs of our community. This technology and the organizations that make use of it serve as the foundation of the future of DPLA and other projects that seek to take advantage of the national digital platform.

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Abstract

An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.

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Abstract

Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based on tags, as well as following the user-user connections formed in the social bookmarking community. However, the effectiveness of tag-based search is limited due to the lack of explicitly represented semantics in the tag space. In addition, social connections between users are underused for web content discovery because of the inadequate social functions. In this research, we propose a comprehensive framework to reorganize the flat tag space into a hierarchical faceted model. We also studied the structure and properties of various networks emerging from the tag space for the purpose of more efficient web content discovery. The major research approach used in this research is social network analysis (SNA), together with methodologies employed in design science research. The contribution of our research includes: (i) a faceted model to categorize social bookmarking tags; (ii) a relationship ontology to represent the semantics of relationships between tags; (iii) heuristics to reorganize the flat tag space into a hierarchical faceted model using analysis of tag-tag co-occurrence networks; (iv) an implemented prototype system as proof-of-concept to validate the feasibility of the reorganization approach; (v) a set of evaluations of the social functions of the current networking features of social bookmarking and a series of recommendations as to how to improve the social functions to facilitate web content discovery.

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Abstract

This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.

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Abstract

The speed of scientific communication - the rate of ideas affecting other researchers' ideas - is increasing dramatically. The factor driving this is free, unrestricted access to research papers. Measurements of user activity in mature eprint archives of research papers such as arXiv have shown, for the first time, the degree to which such services support an evolving network of texts commenting on, citing, classifying, abstracting, listing and revising other texts. The Open Citation project has built tools to measure this activity, to build new archives, and has been closely involved with the development of the infrastructure to support open access on which these new services depend. This is the story of the project, intertwined with the concurrent emergence of the Open Archives Initiative (OAI). The paper describes the broad scope of the project's work, showing how it has progressed from early demonstrators of reference linking to produce Citebase, a Web-based citation and impact-ranked search service, and how it has supported the development of the EPrints.org software for building OAI-compliant archives. The work has been underpinned by analysis and experiments on the semantics of documents (digital objects) to determine the features required for formally perfect linking - instantiated as an application programming interface (API) for reference linking - that will enable other applications to build on this work in broader digital library information environments.

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Abstract

We are rapidly constructing an extensive network infrastructure for moving information across national boundaries, but much remains to be done before linguistic barriers can be surmounted as effectively as geographic ones. Users seeking information from a digital library could benefit from the ability to query large collections once using a single language, even when more than one language is present in the collection. If the information they locate is not available in a language that they can read, some form of translation will be needed. At present, multilingual thesauri such as EUROVOC help to address this challenge by facilitating controlled vocabulary search using terms from several languages, and services such as INSPEC produce English abstracts for documents in other languages. On the other hand, support for free text searching across languages is not yet widely deployed, and fully automatic machine translation is presently neither sufficiently fast nor sufficiently accurate to adequately support interactive cross-language information seeking. An active and rapidly growing research community has coalesced around these and other related issues, applying techniques drawn from several fields - notably information retrieval and natural language processing - to provide access to large multilingual collections.

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Abstract

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.

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Date

10.12.2020 9:29:12

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Date

17.12.2013 11:02:22

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Abstract

The Decimal Classification Editorial Policy Committee (EPC) held its Meeting 117 at the Library Dec. 3-5, 2001, with chair Andrea Stamm (Northwestern University) presiding. Through its actions at this meeting, significant progress was made toward publication of DDC unabridged Edition 22 in mid-2003 and Abridged Edition 14 in early 2004. For Edition 22, the committee approved the revisions to two major segments of the classification: Table 2 through 55 Iran (the first half of the geographic area table) and 900 History and geography. EPC approved updates to several parts of the classification it had already considered: 004-006 Data processing, Computer science; 340 Law; 370 Education; 510 Mathematics; 610 Medicine; Table 3 issues concerning treatment of scientific and technical themes, with folklore, arts, and printing ramifications at 398.2 - 398.3, 704.94, and 758; Table 5 and Table 6 Ethnic Groups and Languages (portions concerning American native peoples and languages); and tourism issues at 647.9 and 790. Reports on the results of testing the approved 200 Religion and 305-306 Social groups schedules were received, as was a progress report on revision work for the manual being done by Ross Trotter (British Library, retired). Revisions for Abridged Edition 14 that received committee approval included 010 Bibliography; 070 Journalism; 150 Psychology; 370 Education; 380 Commerce, communications, and transportation; 621 Applied physics; 624 Civil engineering; and 629.8 Automatic control engineering. At the meeting the committee received print versions of _DC&_ numbers 4 and 5. Primarily for the use of Dewey translators, these cumulations list changes, substantive and cosmetic, to DDC Edition 21 and Abridged Edition 13 for the period October 1999 - December 2001. EPC will hold its Meeting 118 at the Library May 15-17, 2002.

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Date

25. 6.2019 11:13:29

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Source

Extensions and corrections to the UDC. 29(2007), S.263-272

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Date

26.12.2011 16:29:21

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Date

26.12.2011 16:29:02

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Date

29. 6.2015 14:51:28

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Date

29. 7.2011 14:44:56

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Date

29. 5.2012 10:17:08