Log in

Relevant bibliographies by topics / Knowledge representation

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Knowledge representation'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Knowledge representation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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%.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Knowledge representation"

1

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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/.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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/.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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

Full text

Abstract:

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 103).
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.
by Dina Kachintseva.
M.Eng.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Knowledge representation"

1

Knowledge representation. Mahwah, NJ: L. Erlbaum, 1999.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Knowledge and representation. Stanford, Calif: CSLI Publications, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Newen, Albert. Knowledge and representation. Stanford, Calif: CSLI Publ., 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Knowledge representation"

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Ali, Amjad, and Mohammad Abid Khan. "Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes." In 2009 International Conference on Emerging Technologies (ICET). IEEE, 2009. http://dx.doi.org/10.1109/icet.2009.5353207.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lei, Yan, Wang Xinying, and Dong Junlei. "A power grid knowledge representation using agent-based knowledge representation in pervasive computing." In 2010 2nd IEEE International Conference on Information Management and Engineering. IEEE, 2010. http://dx.doi.org/10.1109/icime.2010.5477652.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!