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Artículos de revistas sobre el tema "Knowledge representation"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

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

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

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2

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

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

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3

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

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

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4

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

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5

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

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6

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

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

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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 (2023): 1056. http://dx.doi.org/10.3390/sym15051056.

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

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8

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

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

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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 (2019): 184. http://dx.doi.org/10.3390/ijgi8040184.

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

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10

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

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

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11

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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12

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

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13

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

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14

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 (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

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15

Gilbert, Stephen B., and Whitman Richards. "The Classification of Representational Forms." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (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 ma

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16

Thabit, H. Thabit, and A. Jasim Yaser. "A MANUSCRIPT OF KNOWLEDGE REPRESENTATION." International Journal of Human Resource & Industrial Research 4, no. 4 (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

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17

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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18

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

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19

Ivanova, Tatyana, and Petya Petkova. "Semantic knowledge models of non-crisp knowledge." International Journal on Information Technologies and Security 17, no. 2 (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

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20

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 (2017): 17–20. https://doi.org/10.70729/ijser151274.

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21

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 (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 th

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22

Hou, Wenfeng, Qing Liu, and Longbing Cao. "Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge." Applied Sciences 10, no. 14 (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 mo

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23

Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (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 misinterpre

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24

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

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25

Csaszar, Felipe A., and James Ostler. "A Contingency Theory of Representational Complexity in Organizations." Organization Science 31, no. 5 (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)

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26

Chambers, T. L., and A. R. Parkinson. "Knowledge Representation and Conversion for Hybrid Expert Systems." Journal of Mechanical Design 120, no. 3 (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 Cham

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27

Sunik, Boris. "Knowledge representation with T." Artificial Intelligence Research 7, no. 2 (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

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28

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 (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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29

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

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30

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 h

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31

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

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32

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

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33

Giuse, Dario. "Efficient knowledge representation systems." Knowledge Engineering Review 5, no. 1 (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 b

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34

Rassinoux, A. M. "Knowledge Representation and Management." Yearbook of Medical Informatics 19, no. 01 (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 meaningfu

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35

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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36

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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37

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

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38

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

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39

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

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40

Lu, Cai, Xuyang Zou, and Jingjing Zong. "Signal Separation Based on Knowledge Representation." Applied Sciences 15, no. 6 (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

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41

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 (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 teach

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42

Raden, Megan J., and Andrew F. Jarosz. "Knowledge Representations: Individual Differences in Novel Problem Solving." Journal of Intelligence 11, no. 4 (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 figur

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43

Ataeva, O. M., V. A. Serebryakov, and N. P. Tuchkova. "Ontological Approach: Knowledge Representation and Knowledge Extraction." Lobachevskii Journal of Mathematics 41, no. 10 (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 (2018): 199–216. http://dx.doi.org/10.1007/s42044-018-0019-0.

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45

Pramling, Niklas. "External representation and the architecture of music: Children inventing and speaking about notations." British Journal of Music Education 26, no. 3 (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 buil

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46

Mao, Yanying, and Honghui Chen. "Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types." Mathematics 9, no. 16 (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

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47

Faber, Pamela. "The dynamics of specialized knowledge representation." Terminology 17, no. 1 (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 p

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48

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 (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. Consequent

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49

Veselý, A. "On the representation of expert procedural knowledge ." Agricultural Economics (Zemědělská ekonomika) 52, No. 11 (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 sop

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Coiera, Enrico. "The qualitative representation of physical systems." Knowledge Engineering Review 7, no. 1 (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

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