-
Toms, E.G.: Task-based information searching and retrieval (2011)
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- Date
- 7. 1.2013 19:29:54
- Source
- Interactive information seeking, behaviour and retrieval. Eds.: Ruthven, I. u. D. Kelly
-
Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015)
0.06
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- Abstract
- We present a short survey of the literature on indexing and retrieval of mathematical knowledge, with pointers to 72 papers and tentative taxonomies of both retrieval problems and recurring techniques.
- Date
- 22. 2.2017 12:51:57
-
Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010)
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- Abstract
- Moderne Verfahren des Information Retrieval verlangen nach aussagekräftigen und detailliert relationierten Dokumentationssprachen. Der selektive Transfer einzelner Modellierungsstrategien aus dem Bereich semantischer Technologien für die Gestaltung und Relationierung bestehender Dokumentationssprachen wird diskutiert. In Form einer Taxonomie wird ein hierarchisch strukturiertes Relationeninventar definiert, welches sowohl hinreichend allgemeine als auch zahlreiche spezifische Relationstypen enthält, die eine detaillierte und damit aussagekräftige Relationierung des Vokabulars ermöglichen. Das bringt einen Zugewinn an Übersichtlichkeit und Funktionalität. Im Gegensatz zu anderen Ansätzen und Überlegungen zur Schaffung von Relationeninventaren entwickelt der vorgestellte Vorschlag das Relationeninventar aus der Begriffsmenge eines bestehenden Gegenstandsbereichs heraus.
- Date
- 2. 3.2013 12:29:05
- Source
- Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
-
Lösse, M.; Svensson, L.: "Classification at a Crossroad" : Internationales UDC-Seminar 2009 in Den Haag, Niederlande (2010)
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- Abstract
- Am 29. und 30. Oktober 2009 fand in der Königlichen Bibliothek in Den Haag das zweite internationale UDC-Seminar zum Thema "Classification at a Crossroad" statt. Organisiert wurde diese Konferenz - wie auch die erste Konferenz dieser Art im Jahr 2007 - vom UDC-Konsortium (UDCC). Im Mittelpunkt der diesjährigen Veranstaltung stand die Erschließung des World Wide Web unter besserer Nutzung von Klassifikationen (im Besonderen natürlich der UDC), einschließlich benutzerfreundlicher Repräsentationen von Informationen und Wissen. Standards, neue Technologien und Dienste, semantische Suche und der multilinguale Zugriff spielten ebenfalls eine Rolle. 135 Teilnehmer aus 35 Ländern waren dazu nach Den Haag gekommen. Das Programm umfasste mit 22 Vorträgen aus 14 verschiedenen Ländern eine breite Palette, wobei Großbritannien mit fünf Beiträgen am stärksten vertreten war. Die Tagesschwerpunkte wurden an beiden Konferenztagen durch die Eröffnungsvorträge gesetzt, die dann in insgesamt sechs thematischen Sitzungen weiter vertieft wurden.
- Date
- 22. 1.2010 15:06:54
- Theme
- Klassifikationssysteme im Online-Retrieval
-
Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014)
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- Content
- Introduction: envisioning semantic information spacesIndexing and knowledge organization -- Semantic technologies for knowledge representation -- Information retrieval and knowledge exploration -- Approaches to handle heterogeneity -- Problems with establishing semantic interoperability -- Formalization in indexing languages -- Typification of semantic relations -- Inferences in retrieval processes -- Semantic interoperability and inferences -- Remaining research questions.
- Date
- 23. 7.2017 13:49:22
- LCSH
- Information retrieval
- RSWK
- Information Retrieval
- Subject
- Information retrieval
Information Retrieval
-
Xiong, C.: Knowledge based text representations for information retrieval (2016)
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- Abstract
- The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
- Content
- Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
-
Raieli, R.: ¬The semantic hole : enthusiasm and caution around multimedia information retrieval (2012)
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- Abstract
- This paper centres on the tools for the management of new digital documents, which are not only textual, but also visual-video, audio or multimedia in the full sense. Among the aims is to demonstrate that operating within the terms of generic Information Retrieval through textual language only is limiting, and it is instead necessary to consider ampler criteria, such as those of MultiMedia Information Retrieval, according to which, every type of digital document can be analyzed and searched by the proper elements of language for its proper nature. MMIR is presented as the organic complex of the systems of Text Retrieval, Visual Retrieval, Video Retrieval, and Audio Retrieval, each of which has an approach to information management that handles the concrete textual, visual, audio, or video content of the documents directly, here defined as content-based. In conclusion, the limits of this content-based objective access to documents is underlined. The discrepancy known as the semantic gap is that which occurs between semantic-interpretive access and content-based access. Finally, the integration of these conceptions is explained, gathering and composing the merits and the advantages of each of the approaches and of the systems to access to information.
- Date
- 22. 1.2012 13:02:10
- Footnote
- Bezugnahme auf: Enser, P.G.B.: Visual image retrieval. In: Annual review of information science and technology. 42(2008), S.3-42.
- Source
- Knowledge organization. 39(2012) no.1, S.13-22
-
Rorissa, A.; Yuan, X.: Visualizing and mapping the intellectual structure of information retrieval (2012)
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- Abstract
- Information retrieval is a long established subfield of library and information science. Since its inception in the early- to mid -1950s, it has grown as a result, in part, of well-regarded retrieval system evaluation exercises/campaigns, the proliferation of Web search engines, and the expansion of digital libraries. Although researchers have examined the intellectual structure and nature of the general field of library and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visualizations of citation behaviors. Citation data for 10 years (2000-2009) were retrieved from the Web of Science and analyzed using existing visualization techniques. Our results address information retrieval's co-authorship network, highly productive authors, highly cited journals and papers, author-assigned keywords, active institutions, and the import of ideas from other disciplines.
- Date
- 29. 1.2016 19:20:01
-
Hauer, M.: Collaborative Catalog Enrichment : Digitalisierung und Information Retrieval (2011)
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- Date
- 29. 5.2012 13:58:08
29. 5.2012 14:10:14
- Source
- ¬Die Kraft der digitalen Unordnung: 32. Arbeits- und Fortbildungstagung der ASpB e. V., Sektion 5 im Deutschen Bibliotheksverband, 22.-25. September 2009 in der Universität Karlsruhe. Hrsg: Jadwiga Warmbrunn u.a
-
Verwer, K.: Freiheit und Verantwortung bei Hans Jonas (2011)
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- Content
- Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.
-
Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014)
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- Abstract
- We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.
- Source
- Semantic Web Evaluation Challenge. SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Eds.: V. Presutti et al
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018)
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- Abstract
- One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text-based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag-of-words approaches, in which the image retrieval problem turns into one of standard text-based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical-image-dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image-relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image-relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state-of-the-art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.
- Date
- 29. 9.2018 11:43:31
-
Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012)
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- Abstract
- The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user's search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.
- Date
- 20. 4.2012 13:19:22
- Source
- Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
-
Womser-Hacker, C.: Evaluierung im Information Retrieval (2013)
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- Date
- 5. 4.2013 13:53:29
-
Crestani, F.; Mizzaro, S.; Scagnetto, I,: Mobile information retrieval (2017)
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- Abstract
- This book offers a helpful starting point in the scattered, rich, and complex body of literature on Mobile Information Retrieval (Mobile IR), reviewing more than 200 papers in nine chapters. Highlighting the most interesting and influential contributions that have appeared in recent years, it particularly focuses on both user interaction and techniques for the perception and use of context, which, taken together, shape much of today's research on Mobile IR. The book starts by addressing the differences between IR and Mobile IR, while also reviewing the foundations of Mobile IR research. It then examines the different kinds of documents, users, and information needs that can be found in Mobile IR, and which set it apart from standard IR. Next, it discusses the two important issues of user interfaces and context-awareness. In closing, it covers issues related to the evaluation of Mobile IR applications. Overall, the book offers a valuable tool, helping new and veteran researchers alike to navigate this exciting and highly dynamic area of research.
- Date
- 29. 9.2018 13:24:44
- LCSH
- Information storage and retrieval
- RSWK
- Information Retrieval
- Subject
- Information Retrieval
Information storage and retrieval
-
Hjoerland, B.: Classical databases and knowledge organisation : a case for Boolean retrieval and human decision-making during search (2014)
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- Abstract
- This paper considers classical bibliographic databases based on the Boolean retrieval model (for example MEDLINE and PsycInfo). This model is challenged by modern search engines and information retrieval (IR) researchers, who often consider Boolean retrieval as a less efficient approach. This speech examines this claim and argues for the continued value of Boolean systems, which implies two further issues: (1) the important role of human expertise in searching (expert searchers and "information literacy") and (2) the role of knowledge organization (KO) in the design and use of classical databases, including controlled vocabularies and human indexing. An underlying issue is the kind of retrieval system for which one should aim. It is suggested that Julian Warner's (2010) differentiation between the computer science traditions, aiming at automatically transforming queries into (ranked) sets of relevant documents, and an older library-orientated tradition aiming at increasing the "selection power" of users seems important. The Boolean retrieval model is important in order to provide users with the power to make informed searches and have full control over what is found and what is not found. These issues may also have important implications for the maintenance of information science and KO as research fields as well as for the information profession as a profession in its own right.
- Source
- Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
-
Bergman, O.; Whittaker, S.; Falk, N.: Shared files : the retrieval perspective (2014)
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- Abstract
- People who are collaborating can share files in two main ways: performing Group Information Management (GIM) using a common repository or performing Personal Information Management (PIM) by distributing files as e-mail attachments and storing them in personal repositories. There is a trend toward using common repositories with many organizations encouraging workers to use GIM to avoid duplication of files and management. So far, PIM and GIM have been studied by different research communities, so their effectiveness for file retrieval has not yet been systematically compared. We compared PIM and GIM in a large-scale elicited personal information retrieval study. We asked 275 users to retrieve 860 of their own shared files, testing the effect of sharing method on success and efficiency of retrieval. Participants preferred PIM over GIM. More important, PIM retrieval was more successful: Participants using GIM failed to find 22% of their files compared with 13% failures using PIM. This may be because active organization aids retrieval: When using personally created folders, the failure percentage was 65% lower than when using default folders (e.g., My Documents), and more than 5 times lower than when using folders created by others for GIM. Theoretical reasons for this are discussed.
-
Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
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- Date
- 1. 2.2016 18:25:22
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Kozikowski, P. et al.: Support of part-whole relations in query answering (2016)
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- Date
- 1. 2.2016 18:25:22
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Pobar, M. et al.: Multimodal image retrieval based on keywords and low-level image features (2016)
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- Date
- 1. 2.2016 18:25:22
