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Добірка наукової літератури з теми "Knowledge representation"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 9 червня 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 (March 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 use of spatial knowledge. Several solutions for the problem are presented: the first uses an analogical representation only, the second uses a propositional representation and the third uses an integrated representation. Conclusions about the different representations for building intelligent systems are drawn.

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2

Wu, Lianlong, Seewon Choi, Daniel Raggi, Aaron Stockdill, Grecia Garcia Garcia, Fiorenzo Colarusso, Peter C. H. Cheng, and Mateja Jamnik. "Generation of Visual Representations for Multi-Modal Mathematical Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 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 Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

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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 (January 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 validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.

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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 (December 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 (September 10, 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 language means, which are developed by artificial intelligence. These concepts are different ways to solve the problems of computer knowledge representations.

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7

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 (May 10, 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 Knowledge Graph (JKRL), which models the entity description and relationship description of the triple structure for joint representation learning of knowledge and balances the contribution of the triple structure and text description in the process of vector learning. First, we adopt the TransE model to learn the structural vector representations of entities and relations, and then use a CNN model to encode the entity description to obtain the text representation of the entity. To semantically encode the relation descriptions, we designed an Attention-Bi-LSTM text encoder, which introduces an attention mechanism into the Bi-LSTM model to calculate the semantic relevance between each word in the sentence and different relations. In addition, we also introduce position features into word features in order to better encode word order information. Finally, we define a joint evaluation function to learn the joint representation of structural and textual representations. The experiments show that compared with the baseline methods, our model achieves the best performance on both Mean Rank and Hits@10 metrics. The accuracy of the triple classification task on the FB15K dataset reached 93.2%.

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8

Rezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 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 auxiliary knowledge source could result in obtaining improved representations.

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9

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 (April 8, 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 constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.

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10

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 smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the representation. We discuss the differences between constraints and other representational formats, the advantages of constraint-based models and the types of domains in which they are likely to be useful.

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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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2

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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3

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 causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

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4

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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5

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 than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

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6

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 than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

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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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8

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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9

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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10

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 countless examples of different kinds of birds. and combining these experiences to form a menial representation of the concept. Consequently, each individual's understanding of a concept is slightly different, depending on their experiences. A person living in a place where the predominant types of birds are ostriches and emus will have a different representation birds than a person who predominantly sees penguins, even if the two people speak the same language. This thesis presents a semantic knowledge representation that incorporates this fuzziness and context-dependence of concepts. In particular, this thesis provides several algorithms for learning the meaning behind text by using a dataset of experiences to build up an internal representation of the underlying concepts. Furthermore, several methods are proposed for learning new concepts by discovering patterns in the dataset and using them to compile representations for unnamed ideas. Essentially, these methods learn new concepts without knowing the particular label - or word - used to refer to them. Words are not the only way in which experiences can be described - numbers can often communicate a situation more precisely than words. In fact, many qualitative concepts can be characterized using a set of numeric values. For instance, the qualitative concepts of "young" or "strong" can be characterized using a range of ages or strengths that are equally context-specific and fuzzy. A young adult corresponds to a different range of ages from a young child or a young puppy. By examining the sorts of numeric values that are associated with a particular word in a given context, a person can build up an understanding of the concept. This thesis presents algorithms that use a combination of qualitative and numeric data to learn the meanings of concepts. Ultimately, this thesis demonstrates that this combination of qualitative and quantitative data enables more accurate and precise learning of concepts.<br>by Dina Kachintseva.<br>M.Eng.

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Книги з теми "Knowledge representation"

1

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

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2

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

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3

Mendes, Emilia. Practitioner's Knowledge Representation. Berlin, Heidelberg: 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. Stanford, Calif: CSLI Publ., 2011.

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5

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

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6

Larrazabal, Jesús M., and Luis A. Pérez Miranda, eds. Language, Knowledge, and Representation. Dordrecht: 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. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7941-4.

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8

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

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9

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

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10

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

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Частини книг з теми "Knowledge representation"

1

Grzymala-Busse, Jerzy W. "Knowledge Representation." In Managing Uncertainty in Expert Systems, 13–42. Boston, MA: 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, 13–57. Berkeley, CA: 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, 1–17. Boston, MA: 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, 77–166. Boston, MA: 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, 1035–39. New York, NY: 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, 47–122. Berlin, Heidelberg: 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, 1689–92. Boston, MA: 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, 23–34. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4335-0_2.

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9

Jakus, Grega, Veljko Milutinović, Sanida Omerović, and Sašo Tomažič. "Knowledge Representation." In Concepts, Ontologies, and Knowledge Representation, 47–62. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7822-5_4.

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10

Pawlak, Zdzisław. "Knowledge Representation." In Rough Sets, 51–67. Dordrecht: 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), 1–6. IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782310.

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2

Xie, Ruobing, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. "Image-embodied Knowledge Representation Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: 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 aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

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3

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

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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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Анотація:

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 representations are illustrated through a simple example, the limitations of the technique are discussed, and the utility of the technique for the rapid-prototyping and validation of engineering expert systems is introduced.

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

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7

Nakagawa, Elisa Yumi, and José Carlos Maldonado. "Reference architecture knowledge representation." In the 3rd international workshop. New York, New York, USA: 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}. California: 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 represent different modalities and incorporates a triple contrastive learning model and a dual-phase training strategy to effectively fuse the different modalities with knowledge graph embeddings. We conducted comprehensive comparisons with several different knowledge graph embedding methods to validate the effectiveness of our KG-MRI model. Furthermore validation on a real-world Non-Alcohol Fatty Liver Disease (NAFLD) cohort demonstrated that the vector representations learned through our methodology possess enhanced representational capabilities, showing promise for broader applications in complex multimodal environments.

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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"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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

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8

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

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9

Moore, Robert C. Knowledge Representation and Natural-Language Semantics. Fort Belvoir, VA: Defense Technical Information Center, August 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. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada203716.

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