Search (503 results, page 1 of 26)

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Abstract

Two new methods for retrieving mathematical expressions using conventional keyword search and expression images are presented. An expression-level TF-IDF (term frequency-inverse document frequency) approach is used for keyword search, where queries and indexed expressions are represented by keywords taken from LATEX strings. TF-IDF is computed at the level of individual expressions rather than documents to increase the precision of matching. The second retrieval technique is a form of Content-Base Image Retrieval (CBIR). Expressions are segmented into connected components, and then components in the query expression and each expression in the collection are matched using contour and density features, aspect ratios, and relative positions. In an experiment using ten randomly sampled queries from a corpus of over 22,000 expressions, precision-at-k (k= 20) for the keyword-based approach was higher (keyword: µ= 84.0,s= 19.0, image-based:µ= 32.0,s= 30.7), but for a few of the queries better results were obtained using a combination of the two techniques.

Date

22. 2.2017 12:53:49

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Abstract

Relevance feedback was one of the first interactive information retrieval techniques to help systems learn more about users' interests. Relevance feedback has been used in a variety of IR applications including query expansion, term disambiguation, user profiling, filtering and personalization. Initial relevance feedback techniques were explicit, in that they required the user's active participation. Many of today's relevance feedback techniques are implicit and based on users' information seeking behaviors, such as the pages they choose to visit, the frequency with which they visit pages, and the length of time pages are displayed. Although this type of information is available in great abundance, it is difficult to interpret without understanding more about the user's search goals and context. In this talk, I will address the following questions: what techniques are available to help us learn about users' interests and preferences? What types of evidence are available through a user's interactions with the system and with the information provided by the system? What do we need to know to accurately interpret and use this evidence? I will address the first two questions by presenting an overview of relevance feedback research in information retrieval. I will address the third question by presenting results of some of my own research that examined the online information seeking behaviors of users during a 14-week period and the context in which these behaviors took place.

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Abstract

The University of California's California Digital Library (CDL) formed an Ebook Task Force in August 2000 to evaluate academic libraries' experiences with electronic books (e-books), investigate the e-book market, and develop operating guidelines, principles and potential strategies for further exploration of the use of e-books at the University of California (UC). This article, based on the findings and recommendations of the Task Force Report, briefly summarizes task force findings, and outlines issues and recommendations for making e-books viable over the long term in the academic environment, based on the long-term goals of building strong research collections and providing high level services and collections to its users.

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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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Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

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Abstract

In this talk we present a retrieval model based on social ontologies. More specifically, we utilize the Wikipedia category system in order to perform semantic searches. That is, textual input is used to build queries by means of which documents are retrieved which do not necessarily contain any query term but are semantically related to the input text by virtue of their content. We present a desktop which utilizes this search facility in a web-based environment - the so called eHumanities Desktop.

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Abstract

In the proposed article a new, ontology-based approach to information retrieval (IR) is presented. The system is based on a domain knowledge representation schema in form of ontology. New resources registered within the system are linked to concepts from this ontology. In such a way resources may be retrieved based on the associations and not only based on partial or exact term matching as the use of vector model presumes In order to evaluate the quality of this retrieval mechanism, experiments to measure retrieval efficiency have been performed with well-known Cystic Fibrosis collection of medical scientific papers. The ontology-based retrieval mechanism has been compared with traditional full text search based on vector IR model as well as with the Latent Semantic Indexing method.

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Abstract

Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format diffiult. For example, the table header cell "f(Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.

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Abstract

Summaries of the popular engines extrated from the search sites. Summaries are from: AltaVista, Excite, HotBot, InfoSeek, Ultra, Lycos, OpenText Web Index, and Yahoo. Information covered includes Contents, Searching tips, Results, and Update frequency

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Abstract

- Formal characterization given to the thesaurus mapping problem - Interopearbility workflow - - Thesauri SKOS Core transformation - - Thesaurus Mapping algorithms implementation - The "gold standard" data set and the THALEN application - Thesaurus interoperability assessment measures - Experimental results

Date

7.11.2008 10:40:22

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Abstract

A basic distinction in agent-based data-driven Database Semantics (DBS) is between language and nonlanguage cognition. Language cognition transfers content between agents by means of raw data. Nonlanguage cognition maps between content and raw data inside the focus agent. {\it Recognition} applies a concept type to raw data, resulting in a concept token. In language recognition, the focus agent (hearer) takes raw language-data (surfaces) produced by another agent (speaker) as input, while nonlanguage recognition takes raw nonlanguage-data as input. In either case, the output is a content which is stored in the agent's onboard short term memory. {\it Action} adapts a concept type to a purpose, resulting in a token. In language action, the focus agent (speaker) produces language-dependent surfaces for another agent (hearer), while nonlanguage action produces intentions for a nonlanguage purpose. In either case, the output is raw action data. As long as the procedural implementation of place holder values works properly, it is compatible with the DBS requirement of input-output equivalence between the natural prototype and the artificial reconstruction.

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Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

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Abstract

Nur noch Informationsethik, Informationskompetenz und Information Assessment? Doch gerade die Abschottung von anderen Disziplinen verstärkt die Isolation des "kleinen Faches" Informationswissenschaft in der Scientific Community. So bleiben ihr als letzte "eigenständige" Forschungsrandgebiete nur die, die Wolf Rauch als Keynote Speaker bereits in seinem einführenden, historisch-genetischen Vortrag zur Lage der Informationswissenschaft auf der ISI 2021 benannt hat: "Wenn die universitäre Informationswissenschaft (zumindest in Europa) wohl kaum eine Chance hat, im Bereich der Entwicklung von Systemen und Anwendungen wieder an die Spitze der Entwicklung vorzustoßen, bleiben ihr doch Gebiete, in denen ihr Beitrag in der kommenden Entwicklungsphase dringend erforderlich sein wird: Informationsethik, Informationskompetenz, Information Assessment" (Wolf Rauch: Was aus der Informationswissenschaft geworden ist; in: Thomas Schmidt; Christian Wolff (Eds): Information between Data and Knowledge. Schriften zur Informationswissenschaft 74, Regensburg, 2021, Seiten 20-22 - siehe auch die Rezeption des Beitrages von Rauch durch Johannes Elia Panskus, Was aus der Informationswissenschaft geworden ist. Sie ist in der Realität angekommen, in: Open Password, 17. März 2021). Das ist alles? Ernüchternd.

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Abstract

Sponsored by the German Ministry of Education and Research with funding of 800.000 EURO, the German Network of Expertise in long-term storage of digital resources (nestor) began in June 2003 as a cooperative effort of 6 partners representing different players within the field of long-term preservation. The partners include: * The German National Library (Die Deutsche Bibliothek) as the lead institution for the project * The State and University Library of Lower Saxony Göttingen (Staats- und Universitätsbibliothek Göttingen) * The Computer and Media Service and the University Library of Humboldt-University Berlin (Humboldt-Universität zu Berlin) * The Bavarian State Library in Munich (Bayerische Staatsbibliothek) * The Institute for Museum Information in Berlin (Institut für Museumskunde) * General Directorate of the Bavarian State Archives (GDAB) As in other countries, long-term preservation of digital resources has become an important issue in Germany in recent years. Nevertheless, coming to agreement with institutions throughout the country to cooperate on tasks for a long-term preservation effort has taken a great deal of effort. Although there had been considerable attention paid to the preservation of physical media like CD-ROMS, technologies available for the long-term preservation of digital publications like e-books, digital dissertations, websites, etc., are still lacking. Considering the importance of the task within the federal structure of Germany, with the responsibility of each federal state for its science and culture activities, it is obvious that the approach to a successful solution of these issues in Germany must be a cooperative approach. Since 2000, there have been discussions about strategies and techniques for long-term archiving of digital information, particularly within the distributed structure of Germany's library and archival institutions. A key part of all the previous activities was focusing on using existing standards and analyzing the context in which those standards would be applied. One such activity, the Digital Library Forum Planning Project, was done on behalf of the German Ministry of Education and Research in 2002, where the vision of a digital library in 2010 that can meet the changing and increasing needs of users was developed and described in detail, including the infrastructure required and how the digital library would work technically, what it would contain and how it would be organized. The outcome was a strategic plan for certain selected specialist areas, where, amongst other topics, a future call for action for long-term preservation was defined, described and explained against the background of practical experience.
As follow up, in 2002 the nestor long-term archiving working group provided an initial spark towards planning and organising coordinated activities concerning the long-term preservation and long-term availability of digital documents in Germany. This resulted in a workshop, held 29 - 30 October 2002, where major tasks were discussed. Influenced by the demands and progress of the nestor network, the participants reached agreement to start work on application-oriented projects and to address the following topics: * Overlapping problems o Collection and preservation of digital objects (selection criteria, preservation policy) o Definition of criteria for trusted repositories o Creation of models of cooperation, etc. * Digital objects production process o Analysis of potential conflicts between production and long-term preservation o Documentation of existing document models and recommendations for standards models to be used for long-term preservation o Identification systems for digital objects, etc. * Transfer of digital objects o Object data and metadata o Transfer protocols and interoperability o Handling of different document types, e.g. dynamic publications, etc. * Long-term preservation of digital objects o Design and prototype implementation of depot systems for digital objects (OAIS was chosen to be the best functional model.) o Authenticity o Functional requirements on user interfaces of an depot system o Identification systems for digital objects, etc. At the end of the workshop, participants decided to establish a permanent distributed infrastructure for long-term preservation and long-term accessibility of digital resources in Germany comparable, e.g., to the Digital Preservation Coalition in the UK. The initial phase, nestor, is now being set up by the above-mentioned 3-year funding project.

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Abstract

Archival Information Systems (AIS) are becoming increasingly important. For decades, the amount of content created digitally is growing and its complete life cycle nowadays tends to remain digital. A selection of this content is expected to be of value for the future and can thus be considered being part of our cultural heritage. However, digital content poses many challenges for long-term or indefinite preservation, e.g. digital publications become increasingly complex by the embedding of different kinds of multimedia, data in arbitrary formats and software. As soon as these digital publications become obsolete, but are still deemed to be of value in the future, they have to be transferred smoothly into appropriate AIS where they need to be kept accessible even through changing technologies. The successful previous SDA workshop in 2011 showed: Both, the library and the archiving community have made valuable contributions to the management of huge amounts of knowledge and data. However, both are approaching this topic from different views which shall be brought together to cross-fertilize each other. There are promising combinations of pertinence and provenance models since those are traditionally the prevailing knowledge organization principles of the library and archiving community, respectively. Another scientific discipline providing promising technical solutions for knowledge representation and knowledge management is semantic technologies, which is supported by appropriate W3C recommendations and a large user community. At the forefront of making the semantic web a mature and applicable reality is the linked data initiative, which already has started to be adopted by the library community. It can be expected that using semantic (web) technologies in general and linked data in particular can mature the area of digital archiving as well as technologically tighten the natural bond between digital libraries and digital archives. Semantic representations of contextual knowledge about cultural heritage objects will enhance organization and access of data and knowledge. In order to achieve a comprehensive investigation, the information seeking and document triage behaviors of users (an area also classified under the field of Human Computer Interaction) will also be included in the research.
One of the major challenges of digital archiving is how to deal with changing technologies and changing user communities. On the one hand software, hardware and (multimedia) data formats that become obsolete and are not supported anymore still need to be kept accessible. On the other hand changing user communities necessitate technical means to formalize, detect and measure knowledge evolution. Furthermore, digital archival records are usually not deleted from the AIS and therefore, the amount of digitally archived (multimedia) content can be expected to grow rapidly. Therefore, efficient storage management solutions geared to the fact that cultural heritage is not as frequently accessed like up-to-date content residing in a digital library are required. Software and hardware needs to be tightly connected based on sophisticated knowledge representation and management models in order to face that challenge. In line with the above, contributions to the workshop should focus on, but are not limited to:
Semantic search & semantic information retrieval in digital archives and digital libraries Semantic multimedia archives Ontologies & linked data for digital archives and digital libraries Ontologies & linked data for multimedia archives Implementations and evaluations of semantic digital archives Visualization and exploration of digital content User interfaces for semantic digital libraries User interfaces for intelligent multimedia information retrieval User studies focusing on end-user needs and information seeking behavior of end-users Theoretical and practical archiving frameworks using Semantic (Web) technologies Logical theories for digital archives Semantic (Web) services implementing the OAIS standard Semantic or logical provenance models for digital archives or digital libraries Information integration/semantic ingest (e.g. from digital libraries) Trust for ingest and data security/integrity check for long-term storage of archival records Semantic extensions of emulation/virtualization methodologies tailored for digital archives Semantic long-term storage and hardware organization tailored for AIS Migration strategies based on Semantic (Web) technologies Knowledge evolution We expect new insights and results for sustainable technical solutions for digital archiving using knowledge management techniques based on semantic technologies. The workshop emphasizes interdisciplinarity and aims at an audience consisting of scientists and scholars from the digital library, digital archiving, multimedia technology and semantic web community, the information and library sciences, as well as, from the social sciences and (digital) humanities, in particular people working on the mentioned topics. We encourage end-users, practitioners and policy-makers from cultural heritage institutions to participate as well.

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Abstract

With the rapidly increasing pace at which Web content is evolving, particularly social media, preserving the Web and its evolution over time becomes an important challenge. Meaningful analysis of Web content lends itself to an entity-centric view to organise Web resources according to the information objects related to them. Therefore, the crucial challenge is to extract, detect and correlate entities from a vast number of heterogeneous Web resources where the nature and quality of the content may vary heavily. While a wealth of information extraction tools aid this process, we believe that, the consolidation of automatically extracted data has to be treated as an equally important step in order to ensure high quality and non-ambiguity of generated data. In this paper we present an approach which is based on an iterative cycle exploiting Web data for (1) targeted archiving/crawling of Web objects, (2) entity extraction, and detection, and (3) entity correlation. The long-term goal is to preserve Web content over time and allow its navigation and analysis based on well-formed structured RDF data about entities.

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Abstract

Epistemic communities can be understood as networks of knowledge - based experts that hold in common a set of principled and causal beliefs, have shared notions of validity, exchange knowledge, and shape, demarcate, and articulate the identities of present and future knowledge producers. In Knowledge Organization, epistemic communities have been likened to the term "domain" in the domain - analytic paradigm. Acknowledging the important role that ISKO C - US, the International Society for Knowledge Organization: Chapter for Canada and United States, plays in the international production of scientific knowledge, we aim to characterize this epistemic community based on the publications of the five North American Symposium on Knowledge Organization (NASKO) meetings proceedings. The results allow us to conclude that the ISKO C - US community is a productive, dialogical, and a continuously well - developed community with a well - balanced trajectory between an epistemological dimension, in search of its theoretical and methodological bases, and a social dimension, considering different cultural backgrounds. These aspects demarcate and shape the road for future research on knowledge organization.

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Abstract

The Alexandria Digital Library (ADL) was established in the late 1990s as a response to several perceived problems of traditional map libraries, notably access and organization. By 1999 it had evolved into an operational digital library, offering a well-defined set of services to a broad user community, based on an extensive collection of georeferenced information objects. The vision of ADL continues to evolve, as technology makes new services possible, as its users become more sophisticated and demanding, and as the broader field of geographic information science (GIScience) identifies new avenues for research and application.

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Abstract

This paper reports a controlled study with statistical significance tests an five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive Bayes (NB) classifier. We focus an the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten, and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).