Thematische Bibliographien / Knowledge representation / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Knowledge representation“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 9. Juni 2025

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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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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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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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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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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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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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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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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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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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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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Sen, T. "Diagrammatic knowledge representation." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 4 (1992): 826–30. http://dx.doi.org/10.1109/21.156595.

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Espinosa, J. Alberto, and Mark A. Clark. "Team Knowledge Representation." Human Factors: The Journal of the Human Factors and Ergonomics Society 56, no. 2 (June 27, 2013): 333–48. http://dx.doi.org/10.1177/0018720813494093.

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Nirenburg, Sergei, and Lori Levin. "Knowledge representation support." Machine Translation 4, no. 1 (March 1989): 25–52. http://dx.doi.org/10.1007/bf00367751.

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Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. "Knowledge-Guided Disentangled Representation Learning for Recommender Systems." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3464304.

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In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

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Gilbert, Stephen B., and Whitman Richards. "The Classification of Representational Forms." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 2244–48. http://dx.doi.org/10.1177/1071181319631530.

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Knowledge access and ease of problem-solving, using technology or not, depends upon our choice of representation. Because of our unique facility with language and pictures, these two descriptions are often used to characterize most representational forms, or their combinations, such as flow charts, tables, trees, graphs, or lists. Such a characterization suggests that language and pictures are the principal underlying cognitive dimensions for representational forms. However, we show that when similarity-based scaling methods (multidimensional scaling, hierarchical clustering, and trajectory mapping) are used to relate user tasks that are supported by different representations, then a new categorization appears, namely, tables, trees, and procedures. This new arrangement of knowledge representations may aid interface designers in choosing an appropriate representation for their users' tasks.

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Thabit, H. Thabit, and A. Jasim Yaser. "A MANUSCRIPT OF KNOWLEDGE REPRESENTATION." International Journal of Human Resource & Industrial Research 4, no. 4 (June 30, 2017): 10–21. https://doi.org/10.5281/zenodo.10701802.

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<strong>Abstract</strong> This paper describes the Knowledge representation and how it is used in artificial intelligence systems such as (Expert Systems, Hybrid intelligence systems, Neural networks, etc..) to construct a robust systems without any bugs and issues, it also depicts the knowledge representation categories (Implicit and Explicit) and types (Rules, Ontology, Frames, Semantic Networks and Logic), who is the person that deals with knowledge to analyze (Knowledge Engineer)?, how to build a knowledge base to use in systems? And what are the issues that will face the engineer? <strong>Keywords</strong>: Artificial intelligence, Knowledge representation , Knowledge base

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Giunchiglia, Fausto, Biswanath Dutta, and and Vincenzo Maltese. "From Knowledge Organization to Knowledge Representation." KNOWLEDGE ORGANIZATION 41, no. 1 (2014): 44–56. http://dx.doi.org/10.5771/0943-7444-2014-1-44.

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Fieschi, M., G. Chatellier, and P. Degoulet. "Decision Support Systems from the Standpoint of Knowledge Representation." Methods of Information in Medicine 34, no. 01/02 (1995): 202–8. http://dx.doi.org/10.1055/s-0038-1634575.

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Abstract:Relationships between decision-support systems and knowledge representation are examined from three different points of view: the characteristics of medical decisions that might influence the selection of appropriate knowledge representations, – the extent to which different knowledge representations can support efficient medical decisions and, – the validation of knowledge hypotheses through the practice of decision support systems. A three-level model of knowledge representation is proposed that includes a contextual, a conceptual and a computational level. Taking into consideration the context that leads to the selection of a given representation raises the issue of multiexpertise and multirepresentation modeling. Implementation of decision support systems as sets of cooperative agents and integration in the health information systems are considered.

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Soni, Rashmi, and Neha Singh. "Knowledge Representation in Artificial Intelligence using Domain Knowledge and Reasoning Mechanism." International Journal of Scientific Engineering and Research 5, no. 3 (March 27, 2017): 17–20. https://doi.org/10.70729/ijser151274.

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Ivanova, Tatyana, and Petya Petkova. "Semantic knowledge models of non-crisp knowledge." International Journal on Information Technologies and Security 17, no. 2 (June 1, 2025): 65–76. https://doi.org/10.59035/ismv6176.

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Many practical applications, such as medical diagnosis, business decision-making, information searching and retrieval, etc., require usage of uncertain or ambiguous knowledge. Classical ontology-based technologies can represent and reasoning only with cri Several fuzzy or probabilistic extensions of classical description logics and languages for semantic knowledge representation have been proposed recently, but high reasoning complexity of its decision procedures make difficult its usage in real applications. It is of great importance to select the knowledge representation technology, ensuring both the needed expressiveness and effective reasoning. In this paper we make short analysis of knowledge representation and reasoning capabilities of description logics and ontology representation languages allowing representation of uncertain knowledge and propose a methodology for selecting the best description logic and ontology representation variant for every practical application.

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Zhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.

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Abstract Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address this problem, we propose a temporal knowledge graph representation learning method d-HyTE that incorporates entity descriptions. We learn structure-based representations of entities and relations and explore a deep convolutional neural network with attention to encode description-based representations of entities. The joint representation of two different representations of an entity is regarded as the final representation. We evaluate this method on link prediction and temporal scope prediction. Experimental results showed that our method d-HyTE outperformed the other baselines on many metrics.

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Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (August 2013): 132–46. http://dx.doi.org/10.1055/s-0038-1638845.

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Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

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Hou, Wenfeng, Qing Liu, and Longbing Cao. "Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge." Applied Sciences 10, no. 14 (July 16, 2020): 4893. http://dx.doi.org/10.3390/app10144893.

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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.

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Kuang, Shi Rong. "Knowledge Representation of Art Patterns Based on the Calculation Mental Image." Advanced Materials Research 989-994 (July 2014): 1493–96. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1493.

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Computational imaginary is a simulation of the human mental image based on the study of cognitive science. Art pattern composition knowledge representation is the basis of the intelligence of computer-aided art pattern design. The paper describes an art pattern composition knowledge representation scheme based on the model of computational imaginary. The scheme includes the deep representation, visual representation and spatial representation, and the operations of these three representations. It further describes the abstract and image information from the perspective of the relation between art pattern layout visual and spatial shape.

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Csaszar, Felipe A., and James Ostler. "A Contingency Theory of Representational Complexity in Organizations." Organization Science 31, no. 5 (September 2020): 1198–219. http://dx.doi.org/10.1287/orsc.2019.1346.

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A long-standing question in the organizations literature is whether firms are better off by using simple or complex representations of their task environment. We address this question by developing a formal model of how firm performance depends on the process by which firms learn and use representations. Building on ideas from cognitive science, our model conceptualizes this process in terms of how firms construct a representation of the environment and then use that representation when making decisions. Our model identifies the optimal level of representational complexity as a function of (a) the environment’s complexity and uncertainty and (b) the firm’s experience and knowledge about the environment’s deep structure. We use this model to delineate the conditions under which firms should use simple versus complex representations; in doing so, we provide a coherent framework that integrates previous conflicting results on which type of representation leaves firms better off. Among other results, we show that the optimal representational complexity generally depends more on the firm’s knowledge about the environment than it does on the environment’s actual complexity. We also show that the relative advantage of heuristics vis-à-vis more complex representations critically depends on an unstated assumption of “informedness”: that managers can know what are the most relevant variables to pay attention to. We show that when this assumption does not hold, complex representations are usually better than simpler ones.

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Chambers, T. L., and A. R. Parkinson. "Knowledge Representation and Conversion for Hybrid Expert Systems." Journal of Mechanical Design 120, no. 3 (September 1, 1998): 468–74. http://dx.doi.org/10.1115/1.2829175.

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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, decision tables and decision trees using the calculus of truth-functional logic. The mappings for frames have also been derived by Chambers and Parkinson (1995). 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. The technique is then applied to three engineering applications, showing great improvements in the resulting knowledge base.

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Sunik, Boris. "Knowledge representation with T." Artificial Intelligence Research 7, no. 2 (December 5, 2018): 55. http://dx.doi.org/10.5430/air.v7n2p55.

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The universal representation language T proposed in the article is the set of linguistic items employed in the manner of a natural language with the purpose of information exchange between various communicators. The language is not confined to any particular representation domain, implementation, communicator or discourse type. Assuming there is sufficient vocabulary, each text composed in any of the human languages can be adequately translated to T in the same way as it can be translated to another human language. The semantics transmitted by T code consist of conventional knowledge regarding objects, actions, properties, states and so on.T allows the explicit expression of kinds of information traditionally considered as inexpressible, like tacit knowledge or even non-human knowledge.

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Subrahmanyam, M. V. V. S., Burugupalli Sharmila, Kolupuri Devi Charani, and Yerra Sri Naga Mahesh. "KNOWLEDGE REPRESENTATION AND REASONING." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 23, 2021): 1152–57. http://dx.doi.org/10.51201/jusst/21/07206.

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This paper provides a professional approach to construct a Knowledge Representation and Reasoning module. The development of AGI agents requires architecture modeled after human cognition and this also provides a framework for agents that have interaction with the actual world and to constitute and use it for making decisions that capture and permit implementation of a behavior.

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Levesque, H. J. "Knowledge Representation and Reasoning." Annual Review of Computer Science 1, no. 1 (June 1986): 255–87. http://dx.doi.org/10.1146/annurev.cs.01.060186.001351.

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Rothwell, D. J. "SNOMED-Based Knowledge Representation." Methods of Information in Medicine 34, no. 01/02 (1995): 209–13. http://dx.doi.org/10.1055/s-0038-1634589.

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Abstract:A standardized vocabulary and a standardized representation for this vocabulary are necessary prerequisites for the development of a computer-based patient record. A standard conceptual scheme or data structure for this vocabulary must be in place to define clinical events and to share data. SNOMED International is a detailed, fine grained, semantically typed and comprehensive computer processable vocabulary encompassing both human and veterinary medicine. Each term is placed in a standardized data structure that shows the term relationship within its own and other related taxonomic hierarchies. SNOMED International is a standardized vocabulary and data structure suitable for use in the computer-based patient record.

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AIELLO, LUIGIA CARLUCCI, and DANIELE NARDI. "PERSPECTIVES IN KNOWLEDGE REPRESENTATION." Applied Artificial Intelligence 5, no. 1 (January 1991): 29–44. http://dx.doi.org/10.1080/08839519108927916.

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Shoham, Yoav. "Why knowledge representation matters." Communications of the ACM 59, no. 1 (December 21, 2015): 47–49. http://dx.doi.org/10.1145/2803170.

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Giuse, Dario. "Efficient knowledge representation systems." Knowledge Engineering Review 5, no. 1 (March 1990): 35–50. http://dx.doi.org/10.1017/s0269888900005221.

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AbstractFrame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge representation including user-defined inheritance and relations, object-oriented programming, and constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.

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Rassinoux, A. M. "Knowledge Representation and Management." Yearbook of Medical Informatics 19, no. 01 (August 2010): 64–67. http://dx.doi.org/10.1055/s-0038-1638691.

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Summary Objectives: To summarize current outstanding research in the field of knowledge representation and management. Method: Synopsis of the articles selected for the IMIA Yearbook 2010. Results: Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to. extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring informa- tion within clinical terminologies in order to render such information easily available and shareable among the health informatics com- munity. Conclusions: Delivering common powerful tools able to automati- cally extract meaningful information from the huge amount of elec- tronically unstructured free texts is an essential step towards promot- ing sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.

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Portmann, Edy, Patrick Kaltenrieder, and Witold Pedrycz. "Knowledge Representation through Graphs." Procedia Computer Science 62 (2015): 245–48. http://dx.doi.org/10.1016/j.procs.2015.08.446.

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Kur'erov, Yu I. "Logical knowledge-representation formalisms." Cybernetics and Systems Analysis 28, no. 2 (1992): 211–18. http://dx.doi.org/10.1007/bf01126207.

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Muravitsky, Alexei Yu. "Knowledge representation as domain." Journal of Applied Non-Classical Logics 7, no. 3 (January 1997): 343–64. http://dx.doi.org/10.1080/11663081.1997.10510919.

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Stanojević, Mladen, and Sanja Vraneš. "Knowledge representation with SOUL." Expert Systems with Applications 33, no. 1 (July 2007): 122–34. http://dx.doi.org/10.1016/j.eswa.2006.04.009.

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Grimm, Lisa R. "Psychology of knowledge representation." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 3 (February 22, 2014): 261–70. http://dx.doi.org/10.1002/wcs.1284.

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Lu, Cai, Xuyang Zou, and Jingjing Zong. "Signal Separation Based on Knowledge Representation." Applied Sciences 15, no. 6 (March 18, 2025): 3319. https://doi.org/10.3390/app15063319.

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The separation of mixed signals typically requires appropriate prior assumptions, while traditional signal separation methods struggle to describe the differences in separation targets with significant features. This paper proposes a signal separation framework based on knowledge representation, where separation targets are represented with knowledge, guiding the branches of autoencoders for signal separation. Firstly, under the proposed knowledge representation framework, corresponding knowledge representations are obtained based on observed mixed signals. Secondly, the number of branches of the autoencoder is determined based on the number of separation target signals. Then, utilizing the results of knowledge representation, a branch autoencoder network is constructed, with branches guided by knowledge to achieve the separation of target sub-signals. Finally, a self-encoding network architecture is constructed with a combination of observation signal reconstruction error and knowledge-guided error constraints. Through numerical simulations on a layered velocity model, the Marmousi-II geological model, and the MNIST dataset, the proposed method is validated by comparing the numerical energy differences between predictions and ground truths, demonstrating its effectiveness under both limited and ample data conditions.

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Lestari, Nurcholif Diah Sri, Wasilatul Murtafiah, Marheny Lukitasari, Suwarno Suwarno, and Inge Wiliandani Setya Putri. "IDENTIFIKASI RAGAM DAN LEVEL KEMAMPUAN REPRESENTASI PADA DESAIN MASALAH LITERASI MATEMATIS DARI MAHASISWA CALON GURU." KadikmA 13, no. 1 (April 30, 2022): 11. http://dx.doi.org/10.19184/kdma.v13i1.31538.

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Representation is one of the fundamental abilities of mathematics reflected by students understanding of mathematics concepts, principles, or procedures, so it becomes crucial for teachers to develop students' mathematical representation skills. This research was time to describe the representation used in the problem and the level of mathematical representation ability needed to solve mathematical literacy problems. The data was collected through the assignment to design mathematical literacy problems between 3-10 pieces and interview as triangulation on 35 prospective elementary school teacher students. The data are grouped based on various representations and analyzed quantitatively and descriptively. Then one problem is chosen randomly for each type of representation to describe the level of representation ability needed to solve the problem qualitatively. The results show that the mathematical representations used in designed mathematical literacy problems are pictorial-verbal, pictorial-symbolic, verbal-symbolic, pictorial, verbal, symbolic, and pictorial-verbal-symbolic representations. The level of representational ability that tends to be needed to solve problems is levels 0 and 1. This study suggests that prospective teacher students should develop mathematical representation knowledge to improve the quality of their learning in the future

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Raden, Megan J., and Andrew F. Jarosz. "Knowledge Representations: Individual Differences in Novel Problem Solving." Journal of Intelligence 11, no. 4 (April 21, 2023): 77. http://dx.doi.org/10.3390/jintelligence11040077.

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The present study investigates how the quality of knowledge representations contributes to rule transfer in a problem-solving context and how working memory capacity (WMC) might contribute to the subsequent failure or success in transferring the relevant information. Participants were trained on individual figural analogy rules and then asked to rate the subjective similarity of the rules to determine how abstract their rule representations were. This rule representation score, along with other measures (WMC and fluid intelligence measures), was used to predict accuracy on a set of novel figural analogy test items, of which half included only the trained rules, and half were comprised of entirely new rules. The results indicated that the training improved performance on the test items and that WMC largely explained the ability to transfer rules. Although the rule representation scores did not predict accuracy on the trained items, rule representation scores did uniquely explain performance on the figural analogies task, even after accounting for WMC and fluid intelligence. These results indicate that WMC plays a large role in knowledge transfer, even when transferring to a more complex problem-solving context, and that rule representations may be important for novel problem solving.

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Ataeva, O. M., V. A. Serebryakov, and N. P. Tuchkova. "Ontological Approach: Knowledge Representation and Knowledge Extraction." Lobachevskii Journal of Mathematics 41, no. 10 (October 2020): 1938–48. http://dx.doi.org/10.1134/s1995080220100030.

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Zenkert, Johannes, André Klahold, and Madjid Fathi. "Knowledge discovery in multidimensional knowledge representation framework." Iran Journal of Computer Science 1, no. 4 (April 4, 2018): 199–216. http://dx.doi.org/10.1007/s42044-018-0019-0.

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Pramling, Niklas. "External representation and the architecture of music: Children inventing and speaking about notations." British Journal of Music Education 26, no. 3 (October 2, 2009): 273–91. http://dx.doi.org/10.1017/s0265051709990106.

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This study concerns children's representational knowledge, more specifically, their ‘invented notations’ of music. A small-scale empirical study of four 5-year-old children and their teachers working on the representation of music is reported. The challenges posed by the teachers and how the children respond to these challenges are analysed. The teachers challenge the children to explain their understanding and use contrast to direct children's attention towards distinctions and important terms in the domain of music. The children use coloured geometrical shapes on paper and a sequence of building blocks to represent music. By means of these visuospatial representations, sounding and conversing about them, the children are able to communicate their understanding of the relationship between representation (sign) and sound. The role of external representations in the development of children's musical knowledge is discussed.

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Faber, Pamela. "The dynamics of specialized knowledge representation." Terminology 17, no. 1 (June 20, 2011): 9–29. http://dx.doi.org/10.1075/term.17.1.02fab.

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Dynamicity is the condition of being in motion, and thus, is characterized by continuous change, activity, or progress. Not surprisingly, dynamicity is generally acknowledged to be an important part of any kind of knowledge representation system or knowledge acquisition scenario. This means that it might be a good idea to reconsider concept representations in Terminology, and modify them so that they better reflect the nature of conceptualization in the mind and brain. In this sense, recent theories of cognition have emphasized that situated or grounded experiences are activated in cognitive processing (Louwerse and Jeuniaux 2010; Barsalou 1999; Zwaan 2003). According to these theories, meaning construction heavily relies on perceptually simulating the information that is presented to the comprehender. Specialized knowledge representation that facilitates knowledge acquisition could thus be conceived as a situation model or event that enables comprehenders to use communicated information to better interact with the world

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Mao, Yanying, and Honghui Chen. "Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types." Mathematics 9, no. 16 (August 18, 2021): 1978. http://dx.doi.org/10.3390/math9161978.

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The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

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Cliff, Dave, and Noble Jason. "Knowledge-based vision and simple visual machines." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1358 (August 29, 1997): 1165–75. http://dx.doi.org/10.1098/rstb.1997.0100.

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The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the ‘knowledge’ in knowledge–based vision or form the ‘modelsrsquo; in model–based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place–holders for yet–to–be–identified causal mechanistic interactions. That is, applying the knowledge–based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong.

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Veselý, A. "On the representation of expert procedural knowledge ." Agricultural Economics (Zemědělská ekonomika) 52, No. 11 (February 17, 2012): 516–21. http://dx.doi.org/10.17221/5059-agricecon.

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Procedural knowledge is used by experts for complex system control. In this article, the notion of a complex system is taken in a broad sense. It might be a patient cured by a physician specialist, a biotechnological device, a department of some business enterprise etc. The GLIF model was designed in collaboration of American universities for the formalization of medical guidelines, but it can be used for formal representation of any procedural knowledge. The main objective of the GLIF model was to enable computer processing and comparing of medical guidelines. In this, article also a more sophisticated use of procedural knowledge representation by the means of the GLIF model is proposed. The provided data about expert actions are stored into the database, the formalized knowledge represented by the GLIF model can be used for building up sophisticated reminder systems that warn the user if he decides to make an impropriate action. Different kinds of warnings in the reminder system are proposed and their properties are discussed. At the end, also the possibility of using the GLIF model for decision support is discussed.&nbsp;

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Coiera, Enrico. "The qualitative representation of physical systems." Knowledge Engineering Review 7, no. 1 (March 1992): 55–77. http://dx.doi.org/10.1017/s0269888900006159.

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AbstractThe representation of physical systems using qualitative formalisms is examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but has now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties, has shifted as the field has developed. The evolution of causal and functional representations is thus examined. Finally, a growing body of work that allows reasoning systems to utilize multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are examined, including mode of behaviour, granularity, ontology, and representational depth.

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