Einloggen

Thematische Bibliographien / Knowledge representation

Inhaltsverzeichnis

Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte
Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Knowledge representation“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 6. Juli 2024

Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an

Wählen Sie eine Art der Quelle aus:

Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Knowledge representation" bekannt.

Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.

Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.

Zeitschriftenartikel zum Thema "Knowledge representation"

1

SRIHARI, SARGUR N., und ZHIGANG XIANG. „SPATIAL KNOWLEDGE REPRESENTATION“. International Journal of Pattern Recognition and Artificial Intelligence 03, Nr. 01 (März 1989): 67–84. http://dx.doi.org/10.1142/s0218001489000073.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

2

Wu, Lianlong, Seewon Choi, Daniel Raggi, Aaron Stockdill, Grecia Garcia Garcia, Fiorenzo Colarusso, Peter C. H. Cheng und Mateja Jamnik. „Generation of Visual Representations for Multi-Modal Mathematical Knowledge“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 21 (24.03.2024): 23850–52. http://dx.doi.org/10.1609/aaai.v38i21.30586.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

3

Chua, Cecil Eng Huang, Veda C. Storey und Roger H. Chiang. „Knowledge Representation“. Journal of Database Management 23, Nr. 1 (Januar 2012): 1–30. http://dx.doi.org/10.4018/jdm.2012010101.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

4

Bottoni, Paolo. „Knowledge Representation“. AI Communications 7, Nr. 3-4 (1994): 234–36. http://dx.doi.org/10.3233/aic-1994-73-409.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

5

Sham, S. H. R. „Knowledge-representation“. Engineering Applications of Artificial Intelligence 6, Nr. 6 (Dezember 1993): 594–96. http://dx.doi.org/10.1016/0952-1976(93)90058-6.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

Inozemtsev, V. A. „Deductive logic in solving computer knowledge representation“. Izvestiya MGTU MAMI 8, Nr. 1-5 (10.09.2014): 121–26. http://dx.doi.org/10.17816/2074-0530-67477.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

7

Xu, Guoyan, Qirui Zhang, Du Yu, Sijun Lu und Yuwei Lu. „JKRL: Joint Knowledge Representation Learning of Text Description and Knowledge Graph“. Symmetry 15, Nr. 5 (10.05.2023): 1056. http://dx.doi.org/10.3390/sym15051056.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

8

Rezayi, Saed. „Learning Better Representations Using Auxiliary Knowledge“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 13 (26.06.2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

9

Wang, Shu, Xueying Zhang, Peng Ye, Mi Du, Yanxu Lu und Haonan Xue. „Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation“. ISPRS International Journal of Geo-Information 8, Nr. 4 (08.04.2019): 184. http://dx.doi.org/10.3390/ijgi8040184.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

10

Stellan, Ohlsson, und Antonija Mitrovic. „Constraint-based knowledge representation for individualized instruction“. Computer Science and Information Systems 3, Nr. 1 (2006): 1–22. http://dx.doi.org/10.2298/csis0601001s.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

Mehr Quellen

Dissertationen zum Thema "Knowledge representation"

1

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

au, skhor@iinet net, und 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.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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/.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

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/.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

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

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

Mehr Quellen

Bücher zum Thema "Knowledge representation"

1

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

2

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

5

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

7

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

Den vollen Inhalt der Quelle finden

APA, Harvard, Vancouver, ISO und andere Zitierweisen

8

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Mehr Quellen

Buchteile zum Thema "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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

Sagerer, Gerhard, und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

Pau, Louis F., und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

7

Pirnay-Dummer, Pablo, Dirk Ifenthaler und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

Jakus, Grega, Veljko Milutinović, Sanida Omerović und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Konferenzberichte zum Thema "Knowledge representation"

1

Xie, Ruobing, Zhiyuan Liu, Huanbo Luan und 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.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

2

Dayal, Surendra, Michael Harmer, Peter Johnson und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

Yan, Rong, Ailing Tang und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

Chambers, Terrence L., und 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.

Der volle Inhalt der Quelle

Annotation:

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 und andere Zitierweisen

5

Waldon, S., und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

Nakagawa, Elisa Yumi, und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

7

Wang, Jun, und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

8

Kusuma, Selvia Ferdiana, Mohammad Farid Naufal und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

Ali, Amjad, und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

Lei, Yan, Wang Xinying und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Berichte der Organisationen zum Thema "Knowledge representation"

1

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

2

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

8

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

Mutter, Sharon A., Merryanna L. Swartz, Joseph Psotka, Daria C. Sneed und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Wir bieten Rabatte auf alle Premium-Pläne für Autoren, deren Werke in thematische Literatursammlungen aufgenommen wurden. Kontaktieren Sie uns, um einen einzigartigen Promo-Code zu erhalten!