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Статті в журналах з теми "Context of user query (COQ)"
Xu, Zheng, Hai-Yan Chen, and Jie Yu. "Generating Personalized Web Search Using Semantic Context." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/462782.
Повний текст джерелаGajendragadkar, Uma, and Sarang Joshi. "Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 432. http://dx.doi.org/10.11591/ijece.v7i1.pp432-450.
Повний текст джерелаKumar, Sushil, and Naresh Chauhan. "A Context Model For Focused Web Search." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 155–62. http://dx.doi.org/10.24297/ijct.v2i3c.2715.
Повний текст джерелаYao, Jing, Zhicheng Dou, and Ji-Rong Wen. "Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–29. http://dx.doi.org/10.1145/3470564.
Повний текст джерелаYu, Yang Xin. "Personalization Information Retrieval Based on Unigram Language Model." Applied Mechanics and Materials 321-324 (June 2013): 2269–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2269.
Повний текст джерелаChen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (April 2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.
Повний текст джерелаLi, Chenjie, Juseung Lee, Zhengjie Miao, Boris Glavic, and Sudeepa Roy. "CaJaDE." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3594–97. http://dx.doi.org/10.14778/3554821.3554852.
Повний текст джерелаLi, Yi Min. "Querying Deep Web Based on User Query Schema." Applied Mechanics and Materials 220-223 (November 2012): 2916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2916.
Повний текст джерелаXiong, Wei, Michael Recce, and Brook Wu. "Intent-Based User Segmentation with Query Enhancement." International Journal of Information Retrieval Research 3, no. 4 (October 2013): 1–17. http://dx.doi.org/10.4018/ijirr.2013100101.
Повний текст джерелаVoskarides, Nikos. "Supporting search engines with knowledge and context." ACM SIGIR Forum 55, no. 2 (December 2021): 1–2. http://dx.doi.org/10.1145/3527546.3527573.
Повний текст джерелаДисертації з теми "Context of user query (COQ)"
Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.
Повний текст джерелаAsfari, Ounas. "Personalized Access to Contextual Information by using an Assistant for Query Reformulation." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112126.
Повний текст джерелаAccess to relevant information adapted to the needs and the context of the user is areal challenge in Web Search, owing to the increases of heterogeneous resources andthe varied data on the web. There are always certain needs behind the user query,these queries are often ambiguous and shortened, and thus we need to handle thesequeries intelligently to satisfy the user’s needs. For improving user query processing,we present a context-based hybrid method for query expansion that automaticallygenerates new reformulated queries in order to guide the information retrieval systemto provide context-based personalized results depending on the user profile andhis/her context. Here, we consider the user context as the actual state of the task thatthe user is undertaking when the information retrieval process takes place. Thus StateReformulated Queries (SRQ) are generated according to the task states and the userprofile which is constructed by considering related concepts from existing concepts ina domain ontology. Using a task model, we will show that it is possible to determinethe user’s current task automatically. We present an experimental study in order toquantify the improvement provided by our system compared to the direct querying ofa search engine without reformulation, or compared to the personalized reformulationbased on a user profile only. The Preliminary results have proved the relevance of ourapproach in certain contexts
(14030507), Deepani B. Guruge. "Effective document clustering system for search engines." Thesis, 2008. https://figshare.com/articles/thesis/Effective_document_clustering_system_for_search_engines/21433218.
Повний текст джерелаPeople use web search engines to fill a wide variety of navigational, informational and transactional needs. However, current major search engines on the web retrieve a large number of documents of which only a small fraction are relevant to the user query. The user then has to manually search for relevant documents by traversing a topic hierarchy, into which a collection is categorised. As more information becomes available, it becomes a time consuming task to search for required relevant information.
This research develops an effective tool, the web document clustering (WDC) system, to cluster, and then rank, the output data obtained from queries submitted to a search engine, into three pre-defined fuzzy clusters. Namely closely related, related and not related. Documents in closely related and related documents are ranked based on their context.
The WDC output has been compared against document clustering results from the Google, Vivisimo and Dogpile systems as these where considered the best at the fourth Search Engine Awards [24]. Test data was from standard document sets, such as the TREC-8 [118] data files and the Iris database [38], or 3 from test text retrieval tasks, "Latex", "Genetic Algorithms" and "Evolutionary Algorithms". Our proposed system had as good as, or better results, than that obtained by these other systems. We have shown that the proposed system can effectively and efficiently locate closely related, related and not related, documents among the retrieved document set for queries submitted to a search engine.
We developed a methodology to supply the user with a list of keywords filtered from the initial search result set to further refine the search. Again we tested our clustering results against the Google, Vivisimo and Dogpile systems. In all cases we have found that our WDC performs as well as, or better than these systems.
The contributions of this research are:
- A post-retrieval fuzzy document clustering algorithm that groups documents into closely related, related and not related clusters. This algorithm uses modified fuzzy c-means (FCM) algorithm to cluter documents into predefined intelligent fuzzy clusters and this approach has not been used before.
- The fuzzy WDC system satisfies the user's information need as far as possible by allowing the user to reformulate the initial query. The system prepares an initial word list by selecting a few characteristics terms of high frequency from the first twenty documents in the initial search engine output. The user is then able to use these terms to input a secondary query. The WDC system then creates a second word list, or the context of the user query (COQ), from the closely related documents to provide training data to refine the search. Documents containing words with high frequency from the training list, based on a pre-defined threshold value, are then presented to the user to refine the search by reformulating the query. In this way the context of the user query is built, enabling the user to learn from the keyword list. This approach is not available in current search engine technology.
- A number of modifications were made to the FCM algorithm to improve its performance in web document clustering. A factor swkq is introduced into the membership function as a measure of the amount of overlaping between the components of the feature vector and the cluster prototype. As the FCM algorithm is greatly affected by the values used to initialise the components of cluster prototypes a machine learning approach, using an Evolutionary Algorithm, was used to resolve the initialisation problem.
- Experimental results indicate that the WDC system outperformed Google, Dogpile and the Vivisimo search engines. The post-retrieval fuzzy web document clustering algorithm designed in this research improves the precision of web searches and it also contributes to the knowledge of document retrieval using fuzzy logic.
- A relational data model was used to automatically store data output from the search engine off-line. This takes the processing of data of the Internet off-line, saving resources and making better use of the local CPU.
- This algorithm uses Latent Semantic Indexing (LSI) to rank documents in the closely related and related clusters. Using LSI to rank document is wellknown, however, we are the first to apply it in the context of ranking closely related documents by using COQ to form the term x document matrix in LSI, to obtain better ranking results.
- Adjustments based on document size are proposed for dealing with problems associated with varying document size in the retrieved documents and the effect this has on cluster analysis.
Частини книг з теми "Context of user query (COQ)"
Park, Hyun Kyu, In Ho Cho, Sook Young Ji, and Joong Seek Lee. "An Empirical Study on Web Search Behavior through the Investigation of a User-Clustered Query Session." In Modeling and Using Context, 233–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24279-3_25.
Повний текст джерелаMaeda, Haruhisa, Sachio Saiki, and Masahide Nakamura. "User Context Query Service Supporting Home Person-Centered Care for Elderly People." In Intelligent Human Systems Integration, 112–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73888-8_19.
Повний текст джерелаAknouche, Rachid, Ounas Asfari, Fadila Bentayeb, and Omar Boussaid. "Integrating Query Context and User Context in an Information Retrieval Model Based on Expanded Language Modeling." In Lecture Notes in Computer Science, 244–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32498-7_19.
Повний текст джерелаStorey, Veda C., Vijayan Sugumaran, and Andrew Burton-Jones. "The Role of User Profiles in Context-Aware Query Processing for the Semantic Web." In Natural Language Processing and Information Systems, 51–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27779-8_5.
Повний текст джерелаSingh, Jagendra, and Aditi Sharan. "Context Window Based Co-Occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval." In Information Retrieval and Management, 1597–613. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch072.
Повний текст джерелаXiong, Wei, and Y. F. Brook Wu. "User Query Enhancement for Behavioral Targeting." In Ontologies and Big Data Considerations for Effective Intelligence, 413–33. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2058-0.ch009.
Повний текст джерелаKoutrika, Georgia. "Database Query Personalization." In Encyclopedia of Database Technologies and Applications, 147–52. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-560-3.ch025.
Повний текст джерелаHanh, Huu Hoang, Manh Nguyen Tho, and Min Tjoa A. "A Semantic Web-Based Approach for Context-Aware User Query Formulation and Information Retrieval." In Web Engineering Advancements and Trends, 1–23. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-719-5.ch001.
Повний текст джерелаMüller, Henning, and Jayashree Kalpathy-Cramer. "Putting the Content Into Context." In New Technologies for Advancing Healthcare and Clinical Practices, 105–15. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-780-7.ch006.
Повний текст джерелаHurson, A., and Bo Yang. "Multimedia Content Representation Technologies." In Multimedia Technologies, 580–89. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-953-3.ch042.
Повний текст джерелаТези доповідей конференцій з теми "Context of user query (COQ)"
Freitas, Marcelo, Jimmy Silva, Davi Bandeira, Antonio Mendonca, Damires Souza, and Ana Carolina Salgado. "A User Context Management Approach for Query Personalization Settings." In 2012 IEEE Sixth International Conference on Semantic Computing (ICSC). IEEE, 2012. http://dx.doi.org/10.1109/icsc.2012.31.
Повний текст джерела"Log Analysis of Academic Digital Library: User Query Patterns." In iConference 2014 Proceedings: Breaking Down Walls. Culture - Context - Computing. iSchools, 2014. http://dx.doi.org/10.9776/14346.
Повний текст джерелаFeng, Lizhou, Wanli Zuo, and Youwei Wang. "Novel Query Expansion Method based on User Interest Context and Ontology." In 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isrme-15.2015.82.
Повний текст джерелаShibata, Ryoichi, Shoya Matsumori, Yosuke Fukuchi, Tomoyuki Maekawa, Mitsuhiko Kimoto, and Michita Imai. "Utilizing Core-Query for Context-Sensitive Ad Generation Based on Dialogue." In IUI '22: 27th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490099.3511116.
Повний текст джерелаZhang, Binbin, and Rahul Rai. "Materials Follow Form and Function: Probabilistic Factor Graph Approach for Automatic Material Assignments to 3D Objects." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34064.
Повний текст джерелаdos Santos, Veronica, and Sérgio Lifschitz. "A semantic search approach for hyper relational knowledge graphs." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18171.
Повний текст джерелаWu, Sixing, Minghui Wang, Dawei Zhang, Yang Zhou, Ying Li, and Zhonghai Wu. "Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/546.
Повний текст джерелаRangarajan, Arvind, Pradeep Radhakrishnan, Abha Moitra, Andrew Crapo, and Dean Robinson. "Manufacturability Analysis and Design Feedback System Developed Using Semantic Framework." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12028.
Повний текст джерелаAsfoor, Hasan Mahdi, and Dalal Abadi Alharbi. "Unleash the Potential of Upstream Data Using Search, AI and Computer Vision." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211841-ms.
Повний текст джерелаde Resende Neves, Francisa Joana, and Ana Lídia Virtudes. "GIS model for management of expansion areas: the case of Belmonte." In International Conference Virtual City and Territory. Barcelona: Centre de Política de Sòl i Valoracions, 2009. http://dx.doi.org/10.5821/ctv.7536.
Повний текст джерелаЗвіти організацій з теми "Context of user query (COQ)"
Borgwardt, Stefan, and Veronika Thost. Temporal Query Answering in EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.214.
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