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Готові списки джерел за темами / Knowledge representation

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Добірка наукової літератури з теми "Knowledge representation"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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Статті в журналах з теми "Knowledge representation"

1

SRIHARI, SARGUR N., and ZHIGANG XIANG. "SPATIAL KNOWLEDGE REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (1989): 67–84. http://dx.doi.org/10.1142/s0218001489000073.

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The use of spatial knowledge is necessary in a variety of artificial intelligence and expert systems applications. The need is not only in tasks with spatial goals such as image interpretation and robot motion, but also in tasks not involving spatial goals, e.g. diagnosis and language understanding. The paper discusses methods of representing spatial knowledge, with particular focus on the broad categories known as analogical and propositional representations. The problem of neurological localization is considered in some detail as an example of intelligent problem-solving that requires the us

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2

Wu, Lianlong, Seewon Choi, Daniel Raggi, et al. "Generation of Visual Representations for Multi-Modal Mathematical Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23850–52. http://dx.doi.org/10.1609/aaai.v38i21.30586.

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In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Tree

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3

Chua, Cecil Eng Huang, Veda C. Storey, and Roger H. Chiang. "Knowledge Representation." Journal of Database Management 23, no. 1 (2012): 1–30. http://dx.doi.org/10.4018/jdm.2012010101.

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Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validat

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4

Bottoni, Paolo. "Knowledge Representation." AI Communications 7, no. 3-4 (1994): 234–36. http://dx.doi.org/10.3233/aic-1994-73-409.

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5

Sham, S. H. R. "Knowledge-representation." Engineering Applications of Artificial Intelligence 6, no. 6 (1993): 594–96. http://dx.doi.org/10.1016/0952-1976(93)90058-6.

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6

Inozemtsev, V. A. "Deductive logic in solving computer knowledge representation." Izvestiya MGTU MAMI 8, no. 1-5 (2014): 121–26. http://dx.doi.org/10.17816/2074-0530-67477.

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The article develops the concept of computer representology, which is the philosophical and methodological analysis of deductive models of knowledge representation. These models are one of the varieties of logical models of knowledge representation. These latter knowledge representations together with a logical languages form the important concept of the computer knowledge representation - logical. Under the concepts of computer representation of knowledge are understood aggregates of computer models of representation of domain knowledge of reality, and the corresponding to these models langua

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Xu, Guoyan, Qirui Zhang, Du Yu, Sijun Lu, and Yuwei Lu. "JKRL: Joint Knowledge Representation Learning of Text Description and Knowledge Graph." Symmetry 15, no. 5 (2023): 1056. http://dx.doi.org/10.3390/sym15051056.

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The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However, most methods only consider the triple structure in the knowledge graph and ignore the additional information related to the triple, especially the text description information. In this paper, we propose a knowledge graph representation model with a symmetric architecture called Joint Knowledge Representation Learning of Text Description and Knowled

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Rezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.

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Representation Learning is the core of Machine Learning and Artificial Intelligence as it summarizes input data points into low dimensional vectors. This low dimensional vectors should be accurate portrayals of the input data, thus it is crucial to find the most effective and robust representation possible for given input as the performance of the ML task is dependent on the resulting representations. In this summary, we discuss an approach to augment representation learning which relies on external knowledge. We briefly describe the shortcoming of the existing techniques and describe how an a

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Wang, Shu, Xueying Zhang, Peng Ye, Mi Du, Yanxu Lu, and Haonan Xue. "Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 184. http://dx.doi.org/10.3390/ijgi8040184.

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Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructor

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Stellan, Ohlsson, and Antonija Mitrovic. "Constraint-based knowledge representation for individualized instruction." Computer Science and Information Systems 3, no. 1 (2006): 1–22. http://dx.doi.org/10.2298/csis0601001s.

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Traditional knowledge representations were developed to encode complete explicit and executable programs, a goal that makes them less than ideal for representing the incomplete and partial knowledge of a student. In this paper, we discuss state constraints, a type of knowledge unit originally invented to explain how people can detect and correct their own errors. Constraint-based student modeling has been implemented in several intelligent tutoring systems (ITS) so far, and the empirical data verifies that students learn while interacting with these systems. Furthermore, learning curves are sm

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Дисертації з теми "Knowledge representation"

1

Brockmans, Sara. "Metamodel-based Knowledge Representation." [S.l. : s.n.], 2007. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000007322.

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Schlobach, Klaus Stefan. "Knowledge discovery in hybrid knowledge representation systems." Thesis, King's College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272023.

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au, skhor@iinet net, and Sebastian Wankun Khor. "A Fuzzy Knowledge Map Framework for Knowledge Representation." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070822.32701.

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Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other than

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Khor, Sebastian W. "A fuzzy knowledge map framework for knowledge representation /." Access via Murdoch University Digital Theses Project, 2006. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070822.32701.

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Khor, Sebastian Wankun. "A fuzzy knowledge map framework for knowledge representation." Thesis, Khor, Sebastian Wankun (2007) A fuzzy knowledge map framework for knowledge representation. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/129/.

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Анотація:

Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other th

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Khor, Sebastian Wankun. "A fuzzy knowledge map framework for knowledge representation." Khor, Sebastian Wankun (2007) A fuzzy knowledge map framework for knowledge representation. PhD thesis, Murdoch University, 2007. http://researchrepository.murdoch.edu.au/129/.

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Анотація:

Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other th

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7

DASH, Achyutananda. "KĀRAKA-TEHORY FOR KNOWLEDGE REPRESENTATION." 名古屋大学印度哲学研究室 (Department of Indian Philosophy, University of Nagoya), 1992. http://hdl.handle.net/2237/19175.

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Sukkarieh, Jana Zuheir. "Natural language for knowledge representation." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620452.

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何淑瑩 and Shuk-ying Ho. "Knowledge representation with genetic algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222638.

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Kachintseva, Dina (Dina D. ). "Semantic knowledge representation and analysis." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76983.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 103).<br>Natural language is the means through which humans convey meaning to each other - each word or phrase is a label, or name, for an internal representation of a concept. This internal representation is built up from repeated exposure to particular examples, or instances, of a concept. The way in which we learn that a particular entity in our environment is a "bird" comes from seeing countl

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Книги з теми "Knowledge representation"

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1949-, Brachman Ronald J., Levesque Hector J. 1951-, and Reiter Ray, eds. Knowledge representation. MIT Press, 1992.

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2

1949-, Brachman Ronald J., Levesque Hector J. 1951-, and Reiter Raymond, eds. Knowledge representation. Elsevier, 1991.

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3

Mendes, Emilia. Practitioner's Knowledge Representation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54157-5.

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4

Newen, Albert. Knowledge and representation. CSLI Publ., 2011.

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5

Way, Eileen Cornell. Knowledge Representation and Metaphor. Springer Netherlands, 1991.

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6

Larrazabal, Jesús M., and Luis A. Pérez Miranda, eds. Language, Knowledge, and Representation. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2783-3.

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Way, Eileen Cornell. Knowledge Representation and Metaphor. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7941-4.

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Bergman, Michael K. A Knowledge Representation Practionary. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98092-8.

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1951-, Levesque Hector J., ed. Knowledge representation and reasoning. Morgan Kaufmann, 2003.

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10

1949-, Brachman Ronald J., and Levesque Hector J. 1951-, eds. Readings in knowledge representation. M. Kaufmann Publishers, 1985.

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Частини книг з теми "Knowledge representation"

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Grzymala-Busse, Jerzy W. "Knowledge Representation." In Managing Uncertainty in Expert Systems. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3982-7_2.

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2

Sikos, Leslie F. "Knowledge Representation." In Mastering Structured Data on the Semantic Web. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1049-9_2.

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3

Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. "Knowledge Representation." In Probabilistic Logic Networks. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76872-4_2.

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4

Sagerer, Gerhard, and Heinrich Niemann. "Knowledge Representation." In Semantic Networks for Understanding Scenes. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-1913-7_3.

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Gao, Zhipeng. "Knowledge Representation." In Encyclopedia of Critical Psychology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-5583-7_683.

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Pau, Louis F., and Claudio Gianotti. "Knowledge Representation." In Economic and Financial Knowledge-Based Processing. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-76002-0_5.

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Pirnay-Dummer, Pablo, Dirk Ifenthaler, and Norbert M. Seel. "Knowledge Representation." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_875.

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Shanahan, James G. "Knowledge Representation." In Soft Computing for Knowledge Discovery. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4335-0_2.

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Jakus, Grega, Veljko Milutinović, Sanida Omerović, and Sašo Tomažič. "Knowledge Representation." In Concepts, Ontologies, and Knowledge Representation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7822-5_4.

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Pawlak, Zdzisław. "Knowledge Representation." In Rough Sets. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3534-4_5.

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Тези доповідей конференцій з теми "Knowledge representation"

1

Kommineni, Aditya, Kleanthis Avramidis, Richard Leahy, and Shrikanth Narayanan. "Knowledge-guided EEG Representation Learning." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782310.

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Xie, Ruobing, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. "Image-embodied Knowledge Representation Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/438.

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Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggreg

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Dayal, Surendra, Michael Harmer, Peter Johnson, and David Mead. "Beyond knowledge representation." In the fourth international conference. ACM Press, 1993. http://dx.doi.org/10.1145/158976.158997.

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4

Yan, Rong, Ailing Tang, and Ziyi Zhang. "Increasing Representative Ability for Topic Representation." In The 34th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2022. http://dx.doi.org/10.18293/seke2022-052.

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5

Chambers, Terrence L., and Alan R. Parkinson. "Knowledge Representation and Conversion for Hybrid Expert Systems." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0002.

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Abstract Many different knowledge representations, such as rules and frames, have been proposed for use with engineering expert systems. Every knowledge representation has certain inherent strengths and weaknesses. A knowledge engineer can exploit the advantages, and avoid the pitfalls, of different common knowledge representations if the knowledge can be mapped from one representation to another as needed. This paper derives the mappings between rules, logic diagrams, frames, decision tables and decision trees using the calculus of truth-functional logic. The logical mappings between these re

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Waldon, S., and A. Meystel. "Multiresolutional spatial knowledge representation." In the first international conference. ACM Press, 1988. http://dx.doi.org/10.1145/55674.55732.

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Nakagawa, Elisa Yumi, and José Carlos Maldonado. "Reference architecture knowledge representation." In the 3rd international workshop. ACM Press, 2008. http://dx.doi.org/10.1145/1370062.1370077.

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Lu, Yuxing, Weichen Zhao, Nan Sun, and Jinzhuo Wang. "Enhancing Multimodal Knowledge Graph Representation Learning through Triple Contrastive Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/659.

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Multimodal knowledge graphs incorporate multimodal information rather than pure symbols, which significantly enhance the representation of knowledge graphs and their capacity to understand the world. Despite these advancements, existing multimodal fusion techniques still face significant challenges in representing modalities and fully integrating the diverse attributes of entities, particularly when dealing with more than one modality. To address this issue, this article proposes a Knowledge Graph Multimodal Representation Learning (KG-MRI) method. This method utilizes foundation models to rep

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Wang, Jun, and Qingzhi Meng. "Knowledge Representation for Knowledge-based Generative CAPP." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810663.

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Kusuma, Selvia Ferdiana, Mohammad Farid Naufal, and Rifda Tarimi Octavia. "Knowledge Representation on Pharmacotherapy Using Knowledge Ontology." In 2023 International Electronics Symposium (IES). IEEE, 2023. http://dx.doi.org/10.1109/ies59143.2023.10242595.

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Звіти організацій з теми "Knowledge representation"

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McGregor, Robert, and John Yen. The Knowledge Representation Project. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada211288.

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McCarthy, John, and Carolyn Talcott. Basic Research in Knowledge Representation. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada344511.

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Swartout, Bill, and Yolanda Gil. Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada459767.

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Ossorio, P. G., and L. S. Schneider. Knowledge Representation for C(3)I. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203710.

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Moore, Robert C. Knowledge Representation and Natural-Language Semantics. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada181422.

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Giuse, Dario. KR: An Efficient Knowledge Representation System. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada187705.

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Koller, Daphne. Knowledge Representation for an Uncertain World. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328598.

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Nesbitt, Peter A., Tom Anderson, Jonathan K. Alt, David Ohmen, Kyle Quinnell, and Mario Torres. Knowledge Representation for Decision Making Agents. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada589932.

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Moore, Robert C. Knowledge Representation and Natural-Language Semantics. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162389.

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Mutter, Sharon A., Merryanna L. Swartz, Joseph Psotka, Daria C. Sneed, and Jocelyn O. Turner. Changes in Knowledge Representation with Increasing Expertise. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203716.

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