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Heuss, T.; Humm, B.; Deuschel, T.; Frohlich, T.; Herth, T.; Mitesser, O.: Semantically guided, situation-aware literature research (2015)
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- Linked data and user interaction: the road ahead. Eds.: Cervone, H.F. u. L.G. Svensson
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- Semantisches Umfeld in Indexierung u. Retrieval
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Gábor, K.; Zargayouna, H.; Tellier, I.; Buscaldi, D.; Charnois, T.: ¬A typology of semantic relations dedicated to scientific literature analysis (2016)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Deuschel, T.; Heuss, T.; Humm, B.: ¬Die Medienplattform : ein System für gerichtete und ungerichtete semantische Suchen (2015)
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- Semantisches Umfeld in Indexierung u. Retrieval
-
Hoppe, T.: Semantische Filterung : ein Werkzeug zur Steigerung der Effizienz im Wissensmanagement (2013)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014)
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- Semantisches Umfeld in Indexierung u. Retrieval
-
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
-
Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016)
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- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016)
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- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Narock, T.; Zhou, L.; Yoon, V.: Semantic similarity of ontology instances using polarity mining (2013)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Gnoli, C.; Pusterla, L.; Bendiscioli, A.; Recinella, C.: Classification for collections mapping and query expansion (2016)
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- Location
- I
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Agarwal, N.K.: Exploring context in information behavior : seeker, situation, surroundings, and shared identities (2018)
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- Abstract
- The field of human information behavior runs the gamut of processes from the realization of a need or gap in understanding, to the search for information from one or more sources to fill that gap, to the use of that information to complete a task at hand or to satisfy a curiosity, as well as other behaviors such as avoiding information or finding information serendipitously. Designers of mechanisms, tools, and computer-based systems to facilitate this seeking and search process often lack a full knowledge of the context surrounding the search. This context may vary depending on the job or role of the person; individual characteristics such as personality, domain knowledge, age, gender, perception of self, etc.; the task at hand; the source and the channel and their degree of accessibility and usability; and the relationship that the seeker shares with the source. Yet researchers have yet to agree on what context really means. While there have been various research studies incorporating context, and biennial conferences on context in information behavior, there lacks a clear definition of what context is, what its boundaries are, and what elements and variables comprise context. In this book, we look at the many definitions of and the theoretical and empirical studies on context, and I attempt to map the conceptual space of context in information behavior. I propose theoretical frameworks to map the boundaries, elements, and variables of context. I then discuss how to incorporate these frameworks and variables in the design of research studies on context. We then arrive at a unified definition of context. This book should provide designers of search systems a better understanding of context as they seek to meet the needs and demands of information seekers. It will be an important resource for researchers in Library and Information Science, especially doctoral students looking for one resource that covers an exhaustive range of the most current literature related to context, the best selection of classics, and a synthesis of these into theoretical frameworks and a unified definition. The book should help to move forward research in the field by clarifying the elements, variables, and views that are pertinent. In particular, the list of elements to be considered, and the variables associated with each element will be extremely useful to researchers wanting to include the influences of context in their studies.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Surfing versus Drilling for knowledge in science : When should you use your computer? When should you use your brain? (2018)
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- Content
- Editorial: Surfing versus Drilling for Knowledge in Science: When should you use your computer? When should you use your brain? Blaise Pascal: Les deux infinis - The two infinities / Philippe Hünenberger and Oliver Renn - "Surfing" vs. "drilling" in the modern scientific world / Antonio Loprieno - Of millimeter paper and machine learning / Philippe Hünenberger - From one to many, from breadth to depth - industrializing research / Janne Soetbeer - "Deep drilling" requires "surfing" / Gerd Folkers and Laura Folkers - Surfing vs. drilling in science: A delicate balance / Alzbeta Kubincová - Digital trends in academia - for the sake of critical thinking or comfort? / Leif-Thore Deck - I diagnose, therefore I am a Doctor? Will drilling computer software replace human doctors in the future? / Yi Zheng - Surfing versus drilling in fundamental research / Wilfred van Gunsteren - Using brain vs. brute force in computational studies of biological systems / Arieh Warshel - Laboratory literature boards in the digital age / Jeffrey Bode - Research strategies in computational chemistry / Sereina Riniker - Surfing on the hype waves or drilling deep for knowledge? A perspective from industry / Nadine Schneider and Nikolaus Stiefl - The use and purpose of articles and scientists / Philip Mark Lund - Can you look at papers like artwork? / Oliver Renn - Dynamite fishing in the data swamp / Frank Perabo 34 Streetlights, augmented intelligence, and information discovery / Jeffrey Saffer and Vicki Burnett - "Yes Dave. Happy to do that for you." Why AI, machine learning, and blockchain will lead to deeper "drilling" / Michiel Kolman and Sjors de Heuvel - Trends in scientific document search ( Stefan Geißler - Power tools for text mining / Jane Reed 42 Publishing and patenting: Navigating the differences to ensure search success / Paul Peters
- Editor
- Hünenberger, P. u. O. Renn
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Cai, F.; Rijke, M. de: Learning from homologous queries and semantically related terms for query auto completion (2016)
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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.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017)
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- Source
- Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Adhikari, A.; Dutta, B.; Dutta, A.; Mondal, D.; Singh, S.: ¬An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology (2018)
0.02
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- Abstract
- Finding similarity between concepts based on semantics has become a new trend in many applications (e.g., biomedical informatics, natural language processing). Measuring the Semantic Similarity (SS) with higher accuracy is a challenging task. In this context, the Information Content (IC)-based SS measure has gained popularity over the others. The notion of IC evolves from the science of information theory. Information theory has very high potential to characterize the semantics of concepts. Designing an IC-based SS framework comprises (i) an IC calculator, and (ii) an SS calculator. In this article, we propose a generic intrinsic IC-based SS calculator. We also introduce here a new structural aspect of an ontology called DCS (Disjoint Common Subsumers) that plays a significant role in deciding the similarity between two concepts. We evaluated our proposed similarity calculator with the existing intrinsic IC-based similarity calculators, as well as corpora-dependent similarity calculators using several benchmark data sets. The experimental results show that the proposed similarity calculator produces a high correlation with human evaluation over the existing state-of-the-art IC-based similarity calculators.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
