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Статті в журналах з теми "Knowledge acquisition bottleneck"
Cullen, J., and A. Bryman. "The Knowledge Acquisition Bottleneck: Time for Reassessment?" Expert Systems 5, no. 3 (August 1988): 216–25. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00065.x.
Повний текст джерелаWagner, Christian. "Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management." Information Resources Management Journal 19, no. 1 (January 2006): 70–83. http://dx.doi.org/10.4018/irmj.2006010104.
Повний текст джерелаAussenac-Gilles, Nathalie, and Fabien Gandon. "From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition." International Journal of Human-Computer Studies 71, no. 2 (February 2013): 157–65. http://dx.doi.org/10.1016/j.ijhcs.2012.10.009.
Повний текст джерелаHoppenbrouwers, Stijn, Bart Schotten, and Peter Lucas. "Towards Games for Knowledge Acquisition and Modeling." International Journal of Gaming and Computer-Mediated Simulations 2, no. 4 (October 2010): 48–66. http://dx.doi.org/10.4018/jgcms.2010100104.
Повний текст джерелаSauer, Christopher, Thilo Breitsprecher, Christof Küstner, Benjamin Schleich, and Sandro Wartzack. "SLASSY—An Assistance System for Performing Design for Manufacturing in Sheet-Bulk Metal Forming: Architecture and Self-Learning Aspects." AI 2, no. 3 (July 8, 2021): 307–29. http://dx.doi.org/10.3390/ai2030019.
Повний текст джерелаLin, Shun-Chieh, Chia-Wen Teng, and Shian-Shyong Tseng. "Capturing Evolutional Knowledge Using Time Interval Tracing." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (April 20, 2007): 373–80. http://dx.doi.org/10.20965/jaciii.2007.p0373.
Повний текст джерелаZhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.
Повний текст джерелаRen, Yong Chang, Tao Xing, and Ping Zhu. "An Attribute Reduction Algorithms of Expert System Knowledge Acquisition." Applied Mechanics and Materials 48-49 (February 2011): 187–91. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.187.
Повний текст джерелаLv, Zhan Min, Wei Yan, Jun Liang He, and Dao Fang Chang. "Knowledge-Based System for Major-Pieces Lifting Project Using MRM." Applied Mechanics and Materials 170-173 (May 2012): 3260–65. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3260.
Повний текст джерелаJensen, Isabel Nadine, Roumyana Slabakova, Marit Westergaard, and Björn Lundquist. "The Bottleneck Hypothesis in L2 acquisition: L1 Norwegian learners’ knowledge of syntax and morphology in L2 English." Second Language Research 36, no. 1 (February 28, 2019): 3–29. http://dx.doi.org/10.1177/0267658318825067.
Повний текст джерелаДисертації з теми "Knowledge acquisition bottleneck"
Suraweera, Pramuditha. "Widening the Knowledge Acquisition Bottleneck for Intelligent Tutoring Systems." Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1150.
Повний текст джерелаBallout, Ali. "Apprentissage actif pour la découverte d'axiomes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4026.
Повний текст джерелаThis thesis addresses the challenge of evaluating candidate logical formulas, with a specific focus on axioms, by synergistically combining machine learning with symbolic reasoning. This innovative approach facilitates the automatic discovery of axioms, primarily in the evaluation phase of generated candidate axioms. The research aims to solve the issue of efficiently and accurately validating these candidates in the broader context of knowledge acquisition on the semantic Web.Recognizing the importance of existing generation heuristics for candidate axioms, this research focuses on advancing the evaluation phase of these candidates. Our approach involves utilizing these heuristic-based candidates and then evaluating their compatibility and consistency with existing knowledge bases. The evaluation process, which is typically computationally intensive, is revolutionized by developing a predictive model that effectively assesses the suitability of these axioms as a surrogate for traditional reasoning. This innovative model significantly reduces computational demands, employing reasoning as an occasional "oracle" to classify complex axioms where necessary.Active learning plays a pivotal role in this framework. It allows the machine learning algorithm to select specific data for learning, thereby improving its efficiency and accuracy with minimal labeled data. The thesis demonstrates this approach in the context of the semantic Web, where the reasoner acts as the "oracle," and the potential new axioms represent unlabeled data.This research contributes significantly to the fields of automated reasoning, natural language processing, and beyond, opening up new possibilities in areas like bioinformatics and automated theorem proving. By effectively marrying machine learning with symbolic reasoning, this work paves the way for more sophisticated and autonomous knowledge discovery processes, heralding a paradigm shift in how we approach and leverage the vast expanse of data on the semantic Web
Suraweera, Pramuditha. "Widening the knowledge acquisition bottleneck for intelligent tutoring systems : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in the University of Canterbury /." 2006. http://library.canterbury.ac.nz/etd/adt-NZCU20070417.162903.
Повний текст джерелаЧастини книг з теми "Knowledge acquisition bottleneck"
Weibel, Robert, Stefan Keller, and Tumasch Reichenbacher. "Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence." In Lecture Notes in Computer Science, 139–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60392-1_10.
Повний текст джерелаMendes, David, Irene Pimenta Rodrigues, and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." In Healthcare Ethics and Training, 625–40. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2237-9.ch028.
Повний текст джерелаMendes, David, Irene Pimenta Rodrigues, and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." In Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions, 349–63. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8828-5.ch016.
Повний текст джерелаAsai, Masataro, Hiroshi Kajino, Alex Fukunaga, and Christian Muise. "Chapter 2. Symbolic Reasoning in Latent Space: Classical Planning as an Example." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210349.
Повний текст джерелаNirenburg, Sergei. "Lexicon Acquisition for NLP: A Consumer Report." In Computational Approaches to the Lexicon, 313–48. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198239796.003.0012.
Повний текст джерелаGarfield, Seth. "Drug Prospects." In Guaraná, 74–99. University of North Carolina PressChapel Hill, NC, 2022. http://dx.doi.org/10.5149/northcarolina/9781469671277.003.0005.
Повний текст джерелаТези доповідей конференцій з теми "Knowledge acquisition bottleneck"
Pasini, Tommaso. "The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/687.
Повний текст джерелаBarba, Edoardo, Luigi Procopio, Niccolò Campolungo, Tommaso Pasini, and Roberto Navigli. "MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/531.
Повний текст джерелаChen, Xiyang, Kewei Zhang, and Yucheng Peng. "Research on Multi Diagnosis Methods for Hydro-Generator Sets." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58163.
Повний текст джерелаStensrud, Rune, Sigmund Valaker, and Olav Rune Nummedal. "Exploring Human autonomy teaming methods in challenging environments: the case of uncrewed system (UxS) solutions – challenges and opportunities (with AI)." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004307.
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