Search (44 results, page 1 of 3)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.04 
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  2. Bettencourt, N.; Silva, N.; Barroso, J.: Semantically enhancing recommender systems (2016) 0.03 
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    Abstract
    As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain's specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource's content, user's preferences, users' social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources' semantic information. The recommendation engine allows the promotion and discovery of unknown-unknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources.
  3. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.03 
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
  4. Kulyukin, V.A.; Settle, A.: Ranked retrieval with semantic networks and vector spaces (2001) 0.02 
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    Abstract
    The equivalence of semantic networks with spreading activation and vector spaces with dot product is investigated under ranked retrieval. Semantic networks are viewed as networks of concepts organized in terms of abstraction and packaging relations. It is shown that the two models can be effectively constructed from each other. A formal method is suggested to analyze the models in terms of their relative performance in the same universe of objects
  5. Walker, S.: Subject access in online catalogues (1991) 0.02 
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    Abstract
    Discusses some of the methods of subject access to on-line catalohues (OPACs) and argues that none are entirley satisfactory. Describes 2 methods for improving subject access: best match searching; and automatic query expansion application and discusses their feasibility. Mentions emerging application standards for information retrieval and concludes that existing standards are incompatible with most methods for improving standards
  6. Gauch, S.; Chong, M.K.: Automatic word similarity detection for TREC 4 query expansion (1996) 0.02 
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    Imprint
    Gaithersburgh, MD : National Institute of Standards and Technology
  7. Gauch, S.; Wang, J.: Corpus analysis for TREC 5 query expansion (1997) 0.02 
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    Imprint
    Gaithersburgh, MD : National Institute of Standards and Technology
  8. Principles of semantic networks : explorations in the representation of knowledge (1991) 0.01 
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  9. Smeaton, A.F.; Kelledy, L.; O'Donnell, R.: TREC-4 experiments at Dublin City University : thresholding posting lists, query expansion with WordNet and POS tagging of Spanish (1996) 0.01 
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    Imprint
    Gaithersburgh, MD : National Institute of Standards and Technology
  10. Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017) 0.01 
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    Abstract
    Nowadays, online data shows an astonishing increase and the issue of semantic indexing remains an open question. Ontologies and knowledge bases have been widely used to optimize performance. However, researchers are placing increased emphasis on internal relations of ontologies but neglect latent semantic relations between ontologies and documents. They generally annotate instances mentioned in documents, which are related to concepts in ontologies. In this paper, we propose an Ontology-based Latent Semantic Indexing approach utilizing Long Short-Term Memory networks (LSTM-OLSI). We utilize an importance-aware topic model to extract document-level semantic features and leverage ontologies to extract word-level contextual features. Then we encode the above two levels of features and match their embedding vectors utilizing LSTM networks. Finally, the experimental results reveal that LSTM-OLSI outperforms existing techniques and demonstrates deep comprehension of instances and articles.
  11. Fowler, R.H.; Wilson, B.A.; Fowler, W.A.L.: Information navigator : an information system using associative networks for display and retrieval (1992) 0.01 
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  12. Lin, J.; DiCuccio, M.; Grigoryan, V.; Wilbur, W.J.: Navigating information spaces : a case study of related article search in PubMed (2008) 0.01 
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    Abstract
    The concept of an "information space" provides a powerful metaphor for guiding the design of interactive retrieval systems. We present a case study of related article search, a browsing tool designed to help users navigate the information space defined by results of the PubMed® search engine. This feature leverages content-similarity links that tie MEDLINE® citations together in a vast document network. We examine the effectiveness of related article search from two perspectives: a topological analysis of networks generated from information needs represented in the TREC 2005 genomics track and a query log analysis of real PubMed users. Together, data suggest that related article search is a useful feature and that browsing related articles has become an integral part of how users interact with PubMed.
  13. Bräscher, M.: Semantic relations in knowledge organization systems (2014) 0.01 
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    Abstract
    Semantic relations in knowledge organization systems (KOS) are discussed as well as the need to analyze and systematize the contributions from different areas of knowledge that are devoted to semantic studies in order to collaborate in the definition of a theoretical framework for the study of types of relations included in KOS. Partial results of a survey reveal that, in general, standards and guidelines for developing thesauri are limited to defining and exemplifying types of relationships without guidance concerning the theoretical underpinning of these definitions. The possibilities of a compositional approach to defining the meaning of syntagmatic relations is discussed. Studies on the theoretical foundations that guide the establishment of semantic relations and approaches to be adopted for the preparation of KOS certainly contribute to consolidating a theoretical framework for the area of knowledge organization.
  14. Roy, R.S.; Agarwal, S.; Ganguly, N.; Choudhury, M.: Syntactic complexity of Web search queries through the lenses of language models, networks and users (2016) 0.01 
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  15. Horch, A.; Kett, H.; Weisbecker, A.: Semantische Suchsysteme für das Internet : Architekturen und Komponenten semantischer Suchmaschinen (2013) 0.01 
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    Abstract
    In der heutigen Zeit nimmt die Flut an Informationen exponentiell zu. In dieser »Informationsexplosion« entsteht täglich eine unüberschaubare Menge an neuen Informationen im Web: Beispielsweise 430 deutschsprachige Artikel bei Wikipedia, 2,4 Mio. Tweets bei Twitter und 12,2 Mio. Kommentare bei Facebook. Während in Deutschland vor einigen Jahren noch Google als nahezu einzige Suchmaschine beim Zugriff auf Informationen im Web genutzt wurde, nehmen heute die u.a. in Social Media veröffentlichten Meinungen und damit die Vorauswahl sowie Bewertung von Informationen einzelner Experten und Meinungsführer an Bedeutung zu. Aber wie können themenspezifische Informationen nun effizient für konkrete Fragestellungen identifiziert und bedarfsgerecht aufbereitet und visualisiert werden? Diese Studie gibt einen Überblick über semantische Standards und Formate, die Prozesse der semantischen Suche, Methoden und Techniken semantischer Suchsysteme, Komponenten zur Entwicklung semantischer Suchmaschinen sowie den Aufbau bestehender Anwendungen. Die Studie erläutert den prinzipiellen Aufbau semantischer Suchsysteme und stellt Methoden der semantischen Suche vor. Zudem werden Softwarewerkzeuge vorgestellt, mithilfe derer einzelne Funktionalitäten von semantischen Suchmaschinen realisiert werden können. Abschließend erfolgt die Betrachtung bestehender semantischer Suchmaschinen zur Veranschaulichung der Unterschiede der Systeme im Aufbau sowie in der Funktionalität.
  16. Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017) 0.01 
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  17. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.01 
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  18. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01 
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    Date
    30. 3.2001 13:32:22
  19. Cai, F.; Rijke, M. de: Learning from homologous queries and semantically related terms for query auto completion (2016) 0.00 
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    Abstract
    Query auto completion (QAC) models recommend possible queries to web search users when they start typing a query prefix. Most of today's QAC models rank candidate queries by popularity (i.e., frequency), and in doing so they tend to follow a strict query matching policy when counting the queries. That is, they ignore the contributions from so-called homologous queries, queries with the same terms but ordered differently or queries that expand the original query. Importantly, homologous queries often express a remarkably similar search intent. Moreover, today's QAC approaches often ignore semantically related terms. We argue that users are prone to combine semantically related terms when generating queries. We propose a learning to rank-based QAC approach, where, for the first time, features derived from homologous queries and semantically related terms are introduced. In particular, we consider: (i) the observed and predicted popularity of homologous queries for a query candidate; and (ii) the semantic relatedness of pairs of terms inside a query and pairs of queries inside a session. We quantify the improvement of the proposed new features using two large-scale real-world query logs and show that the mean reciprocal rank and the success rate can be improved by up to 9% over state-of-the-art QAC models.
  20. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.00 
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    Date
    1. 2.2016 18:25:22

Authors

Years

Languages

  • e 37
  • d 6
  • f 1
  • More… Less…

Types

  • a 38
  • el 5
  • x 2
  • m 1
  • r 1
  • More… Less…

Themes

Subjects

Classifications