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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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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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Deuschel, T.; Heuss, T.; Humm, B.: ¬Die Medienplattform : ein System für gerichtete und ungerichtete semantische Suchen (2015)
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Hoppe, T.: Semantische Filterung : ein Werkzeug zur Steigerung der Effizienz im Wissensmanagement (2013)
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Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011)
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Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014)
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Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
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- 1. 2.2016 18:25:22
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Kozikowski, P. et al.: Support of part-whole relations in query answering (2016)
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Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016)
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Narock, T.; Zhou, L.; Yoon, V.: Semantic similarity of ontology instances using polarity mining (2013)
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Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011)
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Gnoli, C.; Pusterla, L.; Bendiscioli, A.; Recinella, C.: Classification for collections mapping and query expansion (2016)
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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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Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014)
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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)
0.02
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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
-
Salaba, A.; Zeng, M.L.: Extending the "Explore" user task beyond subject authority data into the linked data sphere (2014)
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- 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
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Mlodzka-Stybel, A.: Towards continuous improvement of users' access to a library catalogue (2014)
0.02
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- 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
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
