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Jiang, Y.; Bai, W.; Zhang, X.; Hu, J.: Wikipedia-based information content and semantic similarity computation (2017)
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- Semantisches Umfeld in Indexierung u. Retrieval
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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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- Semantisches Umfeld in Indexierung u. Retrieval
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Pontis, S.; Kefalidou, G.; Blandford, A.; Forth, J.; Makri, S.; Sharples, S.; Wiggins, G.; Woods, M.: Academics' responses to encountered information : context matters (2016)
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- Semantisches Umfeld in Indexierung u. Retrieval
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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)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010)
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Ferreira, R.S.; Graça Pimentel, M. de; Cristo, M.: ¬A wikification prediction model based on the combination of latent, dyadic, and monadic features (2018)
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- Semantisches Umfeld in Indexierung u. Retrieval
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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)
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Qu, R.; Fang, Y.; Bai, W.; Jiang, Y.: Computing semantic similarity based on novel models of semantic representation using Wikipedia (2018)
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- Semantisches Umfeld in Indexierung u. Retrieval
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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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- Semantisches Umfeld in Indexierung u. Retrieval
-
Semantic search over the Web (2012)
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Brambilla, M.; Ceri, S.: Designing exploratory search applications upon Web data sources (2012)
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Hannech, A.: Système de recherche d'information étendue basé sur une projection multi-espaces (2018)
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- Abstract
- Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
However, this assumption does not hold in all cases, the needs of the user evolve over time and can move away from his previous interests stored in his profile. In other cases, the user's profile may be misused to extract or infer new information needs. This problem is much more accentuated with ambiguous queries. When multiple POIs linked to a search query are identified in the user's profile, the system is unable to select the relevant data from that profile to respond to that request. This has a direct impact on the quality of the results provided to this user. In order to overcome some of these limitations, in this research thesis, we have been interested in the development of techniques aimed mainly at improving the relevance of the results of current SRIs and facilitating the exploration of major collections of documents. To do this, we propose a solution based on a new concept and model of indexing and information retrieval called multi-spaces projection. This proposal is based on the exploitation of different categories of semantic and social information that enrich the universe of document representation and search queries in several dimensions of interpretations. The originality of this representation is to be able to distinguish between the different interpretations used for the description and the search for documents. This gives a better visibility on the results returned and helps to provide a greater flexibility of search and exploration, giving the user the ability to navigate one or more views of data that interest him the most. In addition, the proposed multidimensional representation universes for document description and search query interpretation help to improve the relevance of the user's results by providing a diversity of research / exploration that helps meet his diverse needs and those of other different users. This study exploits different aspects that are related to the personalized search and aims to solve the problems caused by the evolution of the information needs of the user. Thus, when the profile of this user is used by our system, a technique is proposed and used to identify the interests most representative of his current needs in his profile. This technique is based on the combination of three influential factors, including the contextual, frequency and temporal factor of the data. The ability of users to interact, exchange ideas and opinions, and form social networks on the Web, has led systems to focus on the types of interactions these users have at the level of interaction between them as well as their social roles in the system. This social information is discussed and integrated into this research work. The impact and how they are integrated into the IR process are studied to improve the relevance of the results.
- 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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- Semantisches Umfeld in Indexierung u. Retrieval
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Hu, K.; Luo, Q.; Qi, K.; Yang, S.; Mao, J.; Fu, X.; Zheng, J.; Wu, H.; Guo, Y.; Zhu, Q.: Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis (2019)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Vo, D.-T.; Bagheri, E.: Feature-enriched matrix factorization for relation extraction (2019)
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- Semantisches Umfeld in Indexierung u. Retrieval
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Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017)
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- Semantisches Umfeld in Indexierung u. Retrieval
