Artykuły w czasopismach na temat „Knowledge representation (Information theory)”
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Haddawy, Peter, i Larry Rendell. "Planning and decision theory". Knowledge Engineering Review 5, nr 1 (marzec 1990): 15–33. http://dx.doi.org/10.1017/s026988890000521x.
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Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.
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Popping, Roel, i Inge Strijker. "Representation and integration of sociological knowledge using knowledge graphs". Social Science Information 36, nr 4 (grudzień 1997): 731–47. http://dx.doi.org/10.1177/053901897036004006.
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The representation and integration of sociological knowledge using knowledge graphs, a specific kind of semantic network, is discussed. Knowledge is systematically searched; this reveals inconsistencies, reducing superfluous research and knowledge, and showing gaps in a theory. This representation is conceivable under certain conditions, which are discussed. A graph for sociological theories about labour markets is presented.
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Joubert, M., M. Fieschi i F. Volot. "Review of Biomedical Knowledge and Data Representation with Conceptual Graphs". Methods of Information in Medicine 37, nr 01 (1998): 86–96. http://dx.doi.org/10.1055/s-0038-1634504.
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Abstract:The basis of conceptual graphs theory is an ontology of types of concepts. Concepts issued from the ontology are interlinked by semantic relationships and constitute canonical conceptual graphs. Canonical graphs may be combined to derive new conceptual graphs by means of formation rules. This formalism allows to separate knowledge representation into a conceptual level and a domain-dependent level, and enables to share and reuse a representation. This paper presents conceptual graph applications to biomedical data and concept representation, classification systems, information retrieval, and natural language understanding and processing. A discussion on the unifying role conceptual graphs theory plays in the implementation of knowledge-based systems is also presented.
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Zhitomirsky-Geffet, Maayan. "Towards a diversified knowledge organization system". Journal of Documentation 75, nr 5 (9.09.2019): 1124–38. http://dx.doi.org/10.1108/jd-10-2018-0163.
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Purpose The need for inclusive and logically consistent representation of diverse and even confronting viewpoints on the domain knowledge has been widely discussed in the literature in the past decade. The purpose of this paper is to propose a generic model for building an open coherent diversified knowledge organization system (KOS). Design/methodology/approach The proposed model incorporates a generic epistemological component, the validity scope type, assigned to each statement in the constructed KOS. Statements are clustered by their association with various validity scope types into internally coherent subsystems. These subsystems form a knowledge organization network connected through the universal (consensual) subsystems with more than one validity scope type. The model extends the Galili’s Cultural Content Representation paradigm, which divides the knowledge content of a scientific theory into two confronting parts: body and periphery. Findings The knowledge organization network model makes it possible to comparatively examine similarities and differences among various viewpoints and theories on the domain knowledge. The presented approach conforms with the principle of Open Knowledge Network initiative for creation of open accessible knowledge. Practical implications The proposed model can be used for ontological reasoning by a variety of information services, such as ontology-based decision-support and learning systems, diversified search and customer management applications. Social implications The model enables explicit representation of social and cultural minority voices and historical knowledge in the KOS. Originality/value The main contribution of the proposed model is that it generalizes and enhances various previously proposed representations of epistemological aspects of KOS and allows for multiple inter-linked subsystems to coherently co-exist as part of the extensible network.
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Rousi, Antti Mikael, Reijo Savolainen i Pertti Vakkari. "A typology of music information for studies on information seeking". Journal of Documentation 72, nr 2 (14.03.2016): 265–76. http://dx.doi.org/10.1108/jd-01-2015-0018.
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Purpose – A need to renew music-related information notions arises from both information-seeking models and literature of musical semiotics. The purpose of this paper is to create a music information typology, which aims at facilitating the examination of music information types at varying levels of abstraction in the context of information seeking. Design/methodology/approach – Literature of musical semiotics and information seeking are juxtaposed to develop a novel approach to music-related information. The grounding concepts are Bruner’s enactive, iconic and symbolic modes of representation. The modes of representation offer a universal scheme of knowledge that is applied to the domain of music by defining their content through Tarasti’s Theory of Musical Semiotics. Findings – This conceptual paper results in a music information typology ranging from the enactive music information representations to the abstract ones as follows. Music making as the first mode of enactive representations; music listening as the second mode of enactive representations; iconic representations of music; technological models of music as the first mode of symbolic representations; and ideological models of music as the second mode of symbolic representations. Originality/value – The present paper develops a music information typology that encompasses broadly different music information facets by categorizing music information sources according to their level of abstraction. When applied into empirical research, the typology opens a new window into the perceived roles of music information types in the context of information seeking.
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de Mello, Flávio Luis, i Roberto Lins de Carvalho. "Knowledge Geometry". Journal of Information & Knowledge Management 14, nr 04 (grudzień 2015): 1550028. http://dx.doi.org/10.1142/s0219649215500288.
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This paper aims to present what we call knowledge geometry, an alternative theory for spatial representation of features related to information processing, information management, and knowledge management. It is a unique geometric approach for representing intuition, reification, interpretation, and deduction processes, as well as their relations. We employ the concept of cultural filter and use what we call real, conceptual, and symbolic planes in order to support transformations which occur along the perception of a phenomenon. After that, we discuss the use of evaluation systems to judge concepts and also the use of semantic systems as a communication language. Finally, a framework of the knowledge acquisition process in the field of the proposed theory is offered, proving the feasibility of its automation.
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Radhika, B. "Ground-breaking Theory of Knowledge Representation Practices for Information Sharing in IT Organization". September 2022 4, nr 3 (29.07.2022): 125–43. http://dx.doi.org/10.36548/jitdw.2022.3.001.
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Sharing information has become very important in the proper use of information assets, and the reason for this is that sharing information can be considered the most important part of an organization because information from organizations must be transferred and participated in order to be known and understood, wherein a clear and unambiguous information is considered a key criterion. To stimulate creativity, information sharing or integration is used to bring disparate pieces of knowledge together. Many current information sharing practices, such as training and development programs, IT systems, reports, official documents, and hard-working groups, are examples of integrating information. By integrating information everywhere to improve the quality of products and services, increases responsiveness to customer needs, develop new capabilities, and improve every aspect of the environment. This study reviews the ground-breaking theory behind information sharing in an organization. From the author’s perspective, this is the first study which gives a complete overview about knowledge representation.
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Astaneh, Mahboobeh Farashbashi. "Representationalism in Knowledge Organization and Information Retrieval: Cheer or Criticism?" KNOWLEDGE ORGANIZATION 50, nr 7 (2023): 475–88. http://dx.doi.org/10.5771/0943-7444-2023-7-475.
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The purpose of this paper is to critically analyze the representational approach to language using the concept of ‘Representationalism’ in philosophy and a new framework referred to in this paper as Information Retrieval Action (IRA). IRA is a theoretical construct based on Information Retrieval (IR) theory. Therefore, the methodology employed in this study is philosophical and speculative. It examines and discusses representationalism in both the mental (cognitive) and linguistic dimensions, incorporating concepts from the philosophy of mind and the philosophy of language as methodological approaches. ‘Concept’ and ‘Meaning’ emerge as two critical semantic elements when considering language as a semiotic phenomenon. Representationalism in IRA is rooted in logical positivism, which serves as the foundation for interpreting meaning and concept based on mental representation. In response, the paper proposes an alternative non-representational approach to the semantic elements of language (meaning and concept) based on the philosophy of pragmatism within the IRA framework. While the emergence of new technologies like ontologies implicitly criticizes representationalism in IRA, previous research has not explicitly addressed the theoretical and philosophical criticism of representationalism within the IRA framework.
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IVÁNEK, JIŘÍ. "REPRESENTATION OF EXPERT KNOWLEDGE AS A FUZZY AXIOMATIC THEORY". International Journal of General Systems 20, nr 1 (grudzień 1991): 55–58. http://dx.doi.org/10.1080/03081079108945013.
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Chen, Yen-Liang, i Fang-Chi Chi. "Summarization of information systems based on rough set theory". Journal of Intelligent & Fuzzy Systems 40, nr 1 (4.01.2021): 1001–15. http://dx.doi.org/10.3233/jifs-201160.
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In the rough set theory proposed by Pawlak, the concept of reduct is very important. The reduct is the minimum attribute set that preserves the partition of the universe. A great deal of research in the past has attempted to reduce the representation of the original table. The advantage of using a reduced representation table is that it can summarize the original table so that it retains the original knowledge without distortion. However, using reduct to summarize tables may encounter the problem of the table still being too large, so users will be overwhelmed by too much information. To solve this problem, this article considers how to further reduce the size of the table without causing too much distortion to the original knowledge. Therefore, we set an upper limit for information distortion, which represents the maximum degree of information distortion we allow. Under this upper limit of distortion, we seek to find the summary table with the highest compression. This paper proposes two algorithms. The first is to find all summary tables that satisfy the maximum distortion constraint, while the second is to further select the summary table with the greatest degree of compression from these tables.
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Lehmann, Constance M., i Cynthia D. Heagy. "An Introduction to the Clinical Case Representation Paradigm for Information Systems Research". Journal of Information Systems 20, nr 1 (1.03.2006): 87–101. http://dx.doi.org/10.2308/jis.2006.20.1.87.
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The purpose of this paper is to explain how the use of the Clinical Case Representation Paradigm (CCRP) in accounting information systems (IS) research has the potential to enrich our knowledge of expertise development in IS. The CCRP has been tested extensively in the medical literature, improving the previous novice-expert research by introducing a stage theory of expertise development, which analyzes novice, intermediate, and expert-level judgment and decision making. We propose the use of this paradigm in IS research and provide suggestions for its application using data collected from IS professionals and AIS students. Our results are similar to those found in other CCRP studies in that the more experienced professionals had more concise problem representations, included more inferences (i.e., indicating encapsulated knowledge), and matched the canonical representation and solution more closely. Interestingly, evidence of solution quality was found at the intermediate level. We indicate the limitations of our application of the CCRP and make suggestions for further research projects.
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Duboid, Didier, i Henri Prade. "Non-standard theories of uncertainty in knowledge representation and reasoning". Knowledge Engineering Review 9, nr 4 (grudzień 1994): 399–416. http://dx.doi.org/10.1017/s0269888900007128.
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AbstractThis paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enables classical logic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.
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Burgin, Mark, i Rao Mikkilineni. "From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines". Big Data and Cognitive Computing 5, nr 1 (10.03.2021): 13. http://dx.doi.org/10.3390/bdcc5010013.
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Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate the means of working with knowledge representations (as opposed to data), because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema, which can belong to one of three classes: operational, descriptive, and representation schemas. The goal of this paper is the development of theoretical and practical tools for processing operational schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machines as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic machine is a technical system capable of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.
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Macedo, L. S. Ascensão de, Carlos Guardado da Silva i Maria Cristina Vieira de Freitas. "Information Representation in Displaced Archives: A Meta-Synthesis". KNOWLEDGE ORGANIZATION 49, nr 5 (2022): 329–51. http://dx.doi.org/10.5771/0943-7444-2022-5-329.
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This paper aims to perform a qualitative synthesis of literature concerning the representation of information in displaced archives. Methodologically, this communication is configured in a metasynthesis oriented to theory building, constituting a non-reactive, documentary-based and exploratory type of study, focused on articles and books chapters published in English between 1954 and 2019. The collection of texts is supported by the SPICE strategy, applied to the search in databases (WoS and EBSCO). We adopted content analysis according to the assumptions of Charmaz and Finfgeld-Connett. Of the 443 records, 155 texts that responded to the research purposes were included. Three themes emerged from the content analysis around the aforementioned theme with a view to theory building: “anarchivism as (non-) representation”, “archive of the archive”, and “archival canon”. Finally, displaced archives constitute an emerging theme in several domains, so it is important to explore the complex nature of this phenomenon from the point of view of representation and knowledge organization.
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Crespo, Jonathan, Jose Carlos Castillo, Oscar Martinez Mozos i Ramon Barber. "Semantic Information for Robot Navigation: A Survey". Applied Sciences 10, nr 2 (9.01.2020): 497. http://dx.doi.org/10.3390/app10020497.
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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.
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Hong-Sen Yan. "A new complicated-knowledge representation approach based on knowledge meshes". IEEE Transactions on Knowledge and Data Engineering 18, nr 1 (styczeń 2006): 47–62. http://dx.doi.org/10.1109/tkde.2006.2.
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John, Robert. "Type 2 Fuzzy Sets: An Appraisal of Theory and Applications". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, nr 06 (grudzień 1998): 563–76. http://dx.doi.org/10.1142/s0218488598000434.
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This paper provides a guide and tutorial to type 2 fuzzy sets. Type 2 fuzzy sets allow for linguistic grades of membership thus assisting in knowledge representation. They also offer improvement on inferencing with type 1 sets. The various approaches to knowledge representation and inferencing are discussed, with worked examples, and some of the applications of type 2 sets are reported.
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Kwaśnik, Barbara H. "Changing Perspectives on Classification as a Knowledge-Representation Process". KNOWLEDGE ORGANIZATION 46, nr 8 (2019): 656–67. http://dx.doi.org/10.5771/0943-7444-2019-8-656.
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No matter how immutable a classification may seem, it is, after all, an artifact of the human imagination and functions in a particular place and time. The author describes her personal inquiry into classification as a knowledge-representation process. She traces her changing perspectives on how classifications should be viewed and evaluated by posing the following questions: 1) How does the classification process enable or constrain knowing about something or discovering something we did not already know?; 2) In what ways might we develop classifications that enhance our ability to discover meaningful information in the information stores that form a part of our scholarly as well as our everyday lives?; and 3) How might classifications mask or distort knowledge, and how might they serve to disenfranchise people and ideas? These questions are considered through a discussion of classification structures, personal classification, the link of classification to theory, everyday working classifications, translation of classifications, cognitive aspects, browsing, genres, warrant, and the difficulties of navigating complex ontological commitments. The through thread is the importance of context, because classifications can only be seen with respect to the human endeavors that generate them.
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Costantini, Stefania, i Andrea Formisano. "Adding Metalogic Features to Knowledge Representation Languages*". Fundamenta Informaticae 181, nr 1 (30.06.2021): 71–98. http://dx.doi.org/10.3233/fi-2021-2051.
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In this paper we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. The proposed approach is based on concepts of introspection and reflection previously introduced and discussed by various authors in relevant literature. This allows a knowledge engineer to specify enhanced reasoning engines by defining properties and meta-properties of relations as expressible for instance in OWL. We employ meta-level axiom schemata based upon a naming (reification) device. We propose general principles for extending the semantics of “host” formalisms accordingly. Consequently, suitable pre-defined libraries of properties can be made available, while user-defined new schemata are also allowed. We make the specific cases of Answer Set Programming (ASP) and Datalog±, where such features may be part of software engineering toolkits for these programming paradigms. On the one hand, concerning ASP, we extend the programming principles and practice to accommodate the proposed methodology, so as to perform meta-reasoning within the plain ASP semantics. The computational complexity of the resulting framework does not change. On the other hand, we show how metalogic features can significantly enrich Datalog± with minor changes to its operational semantics (provided in terms of “chase”) and, also in this case, no additional complexity burden.
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Mrózek, Adam, i Leszek Płonka. "Knowledge Representation in Fuzzy and Rough Controllers". Fundamenta Informaticae 30, nr 3,4 (1997): 299–311. http://dx.doi.org/10.3233/fi-1997-303406.
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Ai, Qingyao. "Neural generative models and representation learning for information retrieval". ACM SIGIR Forum 53, nr 2 (grudzień 2019): 97. http://dx.doi.org/10.1145/3458553.3458565.
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Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis : We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice : Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework : We further generalize our proposed neural generative framework for complicated heterogeneous information retrieval scenarios that concern text, images, knowledge entities, and their relationships. Empirical results show that the proposed neural generative framework can effectively learn information representations and construct retrieval models that outperform the state-of-the-art systems in a variety of IR tasks.
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Marchiori, Patricia Zeni, Andre Luiz Appel, Eduardo Michellotti Bettoni, Denise Fukumi Tsunoda i Frank Coelho de Alcântara. "Elements of social representation theory incollaborative tagging systems". Transinformação 26, nr 1 (kwiecień 2014): 27–37. http://dx.doi.org/10.1590/s0103-37862014000100004.
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This article discusses the information representation process based on the Moscovici's Social Representation Theory and domain analysis in Information Science. The aim was to identify mechanisms and constituent dimensions of social representation in collaborative tagging systems/social bookmarking systems. Scientific knowledge was defined as the object/phenomenon of representation in these systems; and the tag as the shareable structure of meaning that connects participants and resources. The empirical research involved descriptive statistical techniques applied to a corpora of tags available in CiteULike, which is a social tagging system developed for the academic community. The data analysis, performed in a sample of groups derived from the dataset, showed that the users' reuse of their own tags resembles the anchorage mechanism. The reuse of tags by other participants - in the same group - reveals some evidence of the objectification mechanism. Some speculation arose about the cognitive effort made by the individual, under group influence, with regard to the tagging activity, user's choice of resources, and sharing styles. Further studies on social bookmarking systems depend both on a "gain scale" of users and items tagged, requiring techniques and procedures redesigned by Information Science, Statistics, Network Analysis, Linguistics/Sociolinguistics and Social Psychology.
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Sull, W., i R. L. Kashyap. "A self-organizing knowledge representation scheme for extensible heterogeneous information environment". IEEE Transactions on Knowledge and Data Engineering 4, nr 2 (kwiecień 1992): 185–91. http://dx.doi.org/10.1109/69.134257.
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Hemmecke, Jeannette, i Christian Star. "Informing Work Interaction Design by 3rd Generation Activity Theory". Interaction Design and Architecture(s), nr 37 (10.06.2018): 100–129. http://dx.doi.org/10.55612/s-5002-037-005.
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In this paper, we reflect on stakeholder interaction design supported by task behavior specifications and stakeholder benefits when framing design with (i) eliciting task knowledge and understanding work activities and activity systems, (ii) means of interactional representation for design, and (iii) capturing the dynamics of activity systems. Thereby, complimentary inputs to theory development and work interaction design techniques become evident. In particular, eliciting implicit knowledge on human task accomplishment and work processes helps understanding and representing activities as design-relevant behavior entities. Eliciting implicit knowledge influences the representation of work knowledge and the subsequent design process of socio-technical systems. We elaborate on some methodological interventions for creating stakeholder task behavior models, including patterns of information exchange for collaborative task accomplishment.
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Jiang-Liang Hou i A. W. J. Tsai. "Knowledge Reuse Enhancement with Motional Visual Representation". IEEE Transactions on Knowledge and Data Engineering 20, nr 10 (październik 2008): 1424–39. http://dx.doi.org/10.1109/tkde.2008.75.
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Fensel, D., J. Angele i R. Studer. "The knowledge acquisition and representation language, KARL". IEEE Transactions on Knowledge and Data Engineering 10, nr 4 (1998): 527–50. http://dx.doi.org/10.1109/69.706055.
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Lipnitsky, S. F. "Model of knowledge representation in the system of information support for decision making". Informatics 19, nr 3 (28.06.2022): 40–49. http://dx.doi.org/10.37661/1816-0301-2022-19-3-40-49.
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Objectives. The problem of mathematical modeling of the knowledge in the information support system for decision-making is being solved. The approach is based on the formalization of semantic relations in the language. The formalization of such relations provides an adequate representation of knowledge and serves as a theoretical basis for the algorithmization of user interaction with the system.When solving the problem of knowledge modeling, three main goals are pursued: the construction of a mathematical model of semantic relations, the study of the properties of marginal syntagmas and the construction of algorithms for the formation of a dictionary of syntagmatic structures.Methods. Methods of set theory, graph theory and mathematical linguistics are used.Results. A mathematical model of knowledge representation in the information decision support system has been developed. The concepts of semantic relations in the language are formalized and the properties of marginal syntagmas are investigated. The proven properties of such structures provide the algorithmization of information processes in the system under consideration.Conclusion. As an implementation of the model proposed in the article, algorithms for creating dictionaries of syntagmas and syntagmatic structures have been developed. When forming a set of syntagmas, the assertions about proved in the article marginal syntagmas are used. A set of syntagmatic structures is built taking into account their typology. Syntagmatic structures in the dictionary acquire the status of communicative fragment, i.e. stable phrases to synthesize the information system language.
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Halkias, Georgios. "Mental representation of brands: a schema-based approach to consumers’ organization of market knowledge". Journal of Product & Brand Management 24, nr 5 (17.08.2015): 438–48. http://dx.doi.org/10.1108/jpbm-02-2015-0818.
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Purpose – This paper aims to review the notion of schemata in consumer behavior, placing particular emphasis on the conceptualization of brand knowledge, and illustrate how schema theory may act as a unifying conceptual framework to study what consumers know about products and brands. Extant research on how consumers conceptualize brands lacks a single, coherent theoretical framework. The literature is fragmented into different approaches that may prevent comparisons across studies and make it difficult to draw conclusive results. Design/methodology/approach – The paper discusses the central tenets of schema theory and then presents the structure of schematic knowledge and the main typology of consumer schemata. It focuses on the brand schema, delineating its internal properties and drawing analogies with other approaches used to describe consumers’ mental representation of brands. Findings – Schema theory can provide a comprehensive framework to analyze how consumers perceive brand information. A cognitive schema specifies the parameters of knowledge content, discriminates between different types of information and indicates how various pieces of information relate to one another. Importantly, the internal structure of schemata remains stable across conceptual domains, allowing to investigate brand-specific knowledge in different contexts and in conjunction with superordinate and subordinate knowledge structures. Originality/value – This is the first systematic review of the notion of schemata in consumer behavior. It thoroughly describes how schema theory from psychology has been applied in marketing research to describe the organization of market knowledge and illustrates how it may function as an analytical tool.
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Sekiwu, Denis, i Nina Olivia Rugambwa. "Documenting Student Representation of Indigenous HIV/AIDS Information and Integration Into the School Curriculum". International Journal of Curriculum Development and Learning Measurement 2, nr 1 (styczeń 2021): 11–28. http://dx.doi.org/10.4018/ijcdlm.2021010102.
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Often times, contemporary health and epidemiological practices ignore indigenous information on HIV prevention. Colonial hegemony tends to replicate indigenous knowledge bases as primordial, superstitious, and lacking vivid scientific explanation to qualify the test for medical diagnostic study. Using an information science viewpoint and an anti-colonial discursive theory, this paper challenges the skewed discernment that it is only Western knowledge production that is considered legitimate knowledge. The authors argue that indigenous HIV/AIDS information exists and can be integrated into the curriculum to complement Western knowledge paradigms on adolescent HIV prevention.
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Roland-Lévy, Christine, Ruxanda Kmiec i Jérémy Lemoine. "How is the economic crisis socially assessed?" Social Science Information 55, nr 2 (8.02.2016): 235–54. http://dx.doi.org/10.1177/0539018416629228.
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Based on the Social Representation Theory, the purpose of this article is to explore how lay-people consider both the economic crisis and risk, and to link these social representations to behavior. The article offers an original approach with the articulation of two studies about the social construction of risk and crises. It also contributes to the development of research methods for studying the connections between representations and practical implications. Based on this, the impact of the social representation of the crisis on the perceived ability to act is approached. The first study focuses on free-association tasks, with two distinct target terms: ‘risk’ and ‘crisis’. The structural approach, with a prototypical analysis, allowed the identification of two different representations: (1) for risk, ‘danger’ is the central element; (2) for crisis, ‘economy’ and ‘money’ constitute the main components of the representation. The second study investigates the links between the two previously detected structures and their relations with the perceived ability to act in a financial crisis context. Some aspects of social knowledge were found to have an impact on perceived ability to act.
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Zucker, Jean-Daniel. "A grounded theory of abstraction in artificial intelligence". Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, nr 1435 (29.07.2003): 1293–309. http://dx.doi.org/10.1098/rstb.2003.1308.
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In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.
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Ikeda, Naruhiro, i Masafumi Hagiwara. "A novel knowledge representation (area representation) and its implementation by neural network". Systems and Computers in Japan 30, nr 13 (30.11.1999): 34–42. http://dx.doi.org/10.1002/(sici)1520-684x(19991130)30:13<34::aid-scj4>3.0.co;2-x.
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Zanin, Massimiliano. "Using complex networks for refining survival prognosis in prostate cancer patient". F1000Research 5 (16.11.2016): 2675. http://dx.doi.org/10.12688/f1000research.8282.1.
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Complex network theory has been used, during the last decade, to understand the structures behind complex biological problems, yielding new knowledge in a large number of situations. Nevertheless, such knowledge has remained mostly qualitative. In this contribution, I show how information extracted from a network representation can be used in a quantitative way, to improve the score of a classification task. As a test bed, I consider a dataset corresponding to patients suffering from prostate cancer, and the task of successfully prognosing their survival. When information from a complex network representation is added on top of a simple classification model, the error is reduced from 27.9% to 23.8%. This confirms that network theory can be used to synthesize information that may not readily be accessible by standard data mining algorithms.
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Adlaj, Semjon F., i Sergey N. Pozdniakov. "Digital Representations of Mathematical Objects in the Context of Various Forms of Representation of Mathematical Knowledge". Computer tools in education, nr 1 (30.03.2020): 58–86. http://dx.doi.org/10.32603/10.32603/2071-2340-2020-58-86.
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This article is devoted to a comparative analysis of the results of the ReMath project (Representing Mathematics with digital media), devoted to the study of digital representations of mathematical concepts. The theoretical provisions and conclusions of this project will be analyzed based on the theory of the information environment [1], developed with the participation of one of the authors of this article. The analysis performed in this work partially coincides with the conclusions of the ReMath project, but uses a different research basis, based mainly on the work of Russian scientists. It is of interest to analyze the work of the ReMath project from the conceptual positions set forth in this monograph and to establish links between concepts and differences in understanding the impact of computer tools (artifacts) on the process of teaching mathematics. At the same time, the authors dispute the interpretation of some issues in Vygotsky’s works by foreign researchers and give their views on the types and functions of digital artifacts in teaching mathematics.
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Adlaj, Semjon F., i Sergey N. Pozdnyakov. "Digital Representations of Mathematical Objects in the Context of Various Forms of Representation of Mathematical Knowledge". Computer tools in education, nr 1 (28.03.2020): 58–86. http://dx.doi.org/10.32603/2071-2340-2020-1-58-86.
Pełny tekst źródłaStreszczenie:
This article is devoted to a comparative analysis of the results of the ReMath project (Representing Mathematics with digital media), devoted to the study of digital representations of mathematical concepts. The theoretical provisions and conclusions of this project will be analyzed based on the theory of the information environment [1], developed with the participation of one of the authors of this article. The analysis performed in this work partially coincides with the conclusions of the ReMath project, but uses a different research basis, based mainly on the work of Russian scientists. It is of interest to analyze the work of the ReMath project from the conceptual positions set forth in this monograph and to establish links between concepts and differences in understanding the impact of computer tools (artifacts) on the process of teaching mathematics. At the same time, the authors dispute the interpretation of some issues in Vygotsky’s works by foreign researchers and give their views on the types and functions of digital artifacts in teaching mathematics.
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Adlaj, Semjon F., i Sergey N. Pozdniakov. "Digital Representations of Mathematical Objects in the Context of Various Forms of Representation of Mathematical Knowledge". Computer tools in education, nr 1 (30.03.2020): 58–86. http://dx.doi.org/10.32603/2071-2340-2020-58-86.
Pełny tekst źródłaStreszczenie:
This article is devoted to a comparative analysis of the results of the ReMath project (Representing Mathematics with digital media), devoted to the study of digital representations of mathematical concepts. The theoretical provisions and conclusions of this project will be analyzed based on the theory of the information environment [1], developed with the participation of one of the authors of this article. The analysis performed in this work partially coincides with the conclusions of the ReMath project, but uses a different research basis, based mainly on the work of Russian scientists. It is of interest to analyze the work of the ReMath project from the conceptual positions set forth in this monograph and to establish links between concepts and differences in understanding the impact of computer tools (artifacts) on the process of teaching mathematics. At the same time, the authors dispute the interpretation of some issues in Vygotsky’s works by foreign researchers and give their views on the types and functions of digital artifacts in teaching mathematics.
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Liu, Hu-Chen, Xue Luan, ZhiWu Li i Jianing Wu. "Linguistic Petri Nets Based on Cloud Model Theory for Knowledge Representation and Reasoning". IEEE Transactions on Knowledge and Data Engineering 30, nr 4 (1.04.2018): 717–28. http://dx.doi.org/10.1109/tkde.2017.2778256.
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Laaziz, Safia, Younes Zeboudj, Salem Benferhat i Faiza Haned Khellaf. "A Weighted-Logic Representation of C-Revising Ordinal Conditional Functions". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, nr 04 (sierpień 2020): 569–89. http://dx.doi.org/10.1142/s0218488520500245.
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The problem of belief change is considered as a major issue in managing the dynamics of an information system. It consists in modifying an uncertainty distribution, representing agents’ beliefs, in the light of a new information. In this paper, we focus on the so-called multiple iterated belief revision or C-revision, proposed for conditioning or revising uncertain distributions under uncertain inputs. Uncertainty distributions are represented in terms of ordinal conditional functions. We will use prioritized or weighted knowledge bases as a compact representation of uncertainty distributions. The input information leading to a revision of an uncertainty distribution is also represented by a set of consistent weighted formulas. This paper shows that C-revision, defined at a semantic level using ordinal conditional functions, has a very natural representation using weighted knowledge bases. We propose simple syntactic methods for revising weighted knowledge bases, that are semantically meaningful in the frameworks of possibility theory and ordinal conditional functions. In particular, we show that the space complexity of the proposed syntactic C-revision is linear with respect to the size of initial weighted knowledge bases.
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Malloy, Tyler, Yinuo Du, Fei Fang i Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model". Proceedings of the AAAI Symposium Series 2, nr 1 (22.01.2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.
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Instance-Based Learning Theory (IBLT) suggests that humans learn to engage in dynamic decision making tasks through the accumulation of experiences, represented by the decision task features, the actions performed, and the utility of decision outcomes. This theory has been applied to the design of Instance-Based Learning (IBL) models of human behavior in a variety of contexts. One key feature of all IBL model applications is the method of accumulating instance-based memory and performing recognition-based retrieval. In simple tasks with few features, this knowledge representation and retrieval could hypothetically be done using all relevant information. However, these methods do not scale well to complex tasks when exhaustive enumeration of features is unfeasible. This requires cognitive modelers to design task-specific representations of state features, as well as similarity metrics, which can be time consuming and fail to generalize to related tasks. To address this issue, we leverage recent advancements in Artificial Neural Networks, specifically generative models (GMs), to learn representations of complex dynamic decision making tasks without relying on domain knowledge. We evaluate a range of GMs in their usefulness in forming representations that can be used by IBL models to predict human behavior in a complex decision making task. This work connects generative and cognitive models by using GMs to form representations and determine similarity.
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Wei, Xing. "Using Rough Set to Construct the Enterprise Information Management System". Advanced Engineering Forum 6-7 (wrzesień 2012): 641–46. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.641.
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Rough set theory as a new hotspot in the field of artificial intelligence, it can effectively deal with incomplete and uncertain knowledge representation and reasoning. Rough set theory is built on the basis of the classification mechanism, it will be classified understand in a particular space on the equivalence relation, equivalence relations constitute the division of space. The paper puts forward using rough set to construct the enterprise information management system. The experiment shows the CPU Time in the attribute numbers, indicating that Jelonek is superior to rough set in building enterprise information management system.
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Kukhar, M. "APPLICATION OF THE THEORY OF THE SETS TO REPRESENT THE LINGUISTIC OBJECTS IN ONTOLOGY". Municipal economy of cities 3, nr 156 (1.07.2020): 94–98. http://dx.doi.org/10.33042/2522-1809-2020-3-156-94-98.
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World experience shows that intelligent information systems for decision support are an integral part of modern society functioning. The basis of many tasks that lies in the development of decision support systems is the presentation of knowledge of a particular subject field. Each decision-support system, depending on the application field, has its own characteristics that characterize the main objectives of this subject area, among which, for example, the presentation of knowledge of multi-level administration systems for decision support purposes. In modern conditions, mathematical modeling is the most effective for the formal representation of knowledge in decision support systems which can be used to represent declarative knowledge of land relations. Therefore, urgent scientific practical problem is relevant now that lies in the representing contradictory knowledge in multilevel administration systems. The scientific and practical task envisages the transformation of declarative knowledge of the subject field in the form of mathematical and informational models using elements of set theory. The task envisages the transformation of declarative knowledge in the form of mathematical models with using set theory. The research used methods: analysis, set theory, mathematical modeling, corpus linguistics, ontological engineering. The results of the study are important for organizing activities in a variety of industries using a large body of documents and laws. The purpose of this work is to develop models of knowledge representation on the use of set theory in ontology. The object of research is the process of decision support in multilevel systems. Subject of research is the mathematical models of knowledge representation in multilevel administration systems using set theory. Keywords: formalization, theory, predicate logic, corpus linguistics, ontology, set theory, model.
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Whynot, Jane, Catherine Mavriplis, Annemieke Farenhorst, Eve Langelier, Tamara Franz-Odendaal i Lesley Shannon. "Knitting Theory in STEM Performance Stories: Experiences in Developing a Performance Framework". Canadian Journal of Program Evaluation 33, nr 3 (styczeń 2019): 354–74. http://dx.doi.org/10.3138/cjpe.53011.
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Abstract: Gender equality has made its way to the forefront of discussions across various sectors in the Canadian context. Yet the intentional inclusion of gender and other intersectional identity dimensions is just beginning to permeate the realities of performance measurement and evaluation practitioners, particularly those using program theory. There is a vast body of knowledge regarding the measurement of women’s empowerment, gradually declining availability of resources targeting the inclusion of gender in theory, and even less guidance on integrating gender in theory in the context of gendered programming. Similarly, coordinated efforts from multiple sectors have resulted in an abundance of theory regarding girls and women’s representation, recruitment, retention, and promotion within STEM (Science, Technology, Engineering, and Math) but less guidance on the measurement and evaluation in these areas. This article shares recent efforts to bridge the divide using theory knitting to develop a performance measurement framework addressing the decreasing representation of girls and women across the STEM “leaky pipeline” using the COM-B theory of change model.
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Comer, Stephen D. "An Algebraic Approach to the Approximation of Information". Fundamenta Informaticae 14, nr 4 (1.04.1991): 492–502. http://dx.doi.org/10.3233/fi-1991-14406.
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This paper is based on the notion of an information system <U,Ω,V,f> in the sense of Pawlak. Every set of knowledge P ⊆ Ω determines a closure operator on U. The class of Boolean algebras with added operations determined by all sets of know ledge are axiomatixed. As a consequence of the representation theorem information systems can be constructed that have a prescribed lattice of functional dependencies.
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Richter, Michael M. "Some Recent Developments in the Representation and Processing of Knowledge". Fundamenta Informaticae 18, nr 2-4 (1.04.1993): 233–48. http://dx.doi.org/10.3233/fi-1993-182-411.
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Bessy, Christian. "Distributed Cognition and the Theory of the Firm : Enriching Nelson and Winter’s Evolutionary Analysis of Organisational Learning and Memory". Économie appliquée 56, nr 4 (2003): 65–90. http://dx.doi.org/10.3406/ecoap.2003.3498.
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The aim of this paper is to re-examine the analysis of organisational learning and memory advanced by the evolutionary analysis of the firm associated with Nelson and Winter (1982). Rather than focus on the debate about codification of knowledge, we propose to pay more attention to the way knowledge is distributed, not only among individuals, but also between individuals and their socio-material environment. We present a distributed cognition approach that offers an understanding of learning as a permanent reorganisation process of representational media that are inside as well as outside the individuals involved. We show how this perspective is different from Simon’s conception of cognition considered as information processing. Moreover, this approach renews the tacit knowledge tradition by examining more precisely the links between perception, memory and representation. It also opens new fields of empirical observations.
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Dotsenko, Serhiy, Brezhniev Eugene, Dmytro Nor, Lyubov Klymenko i Alina Hnatchuk. "Logical-semantic models and methods of knowledge representation: cases for energy management systems and SMR digital infrastructures". Radioelectronic and Computer Systems 2024, nr 2 (23.04.2024): 213–29. http://dx.doi.org/10.32620/reks.2024.2.17.
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The subject of this article is the development of information technologies at the end of the 20th and the beginning of the 21st century for the Fourth Industrial Revolution in the form of Industry 4.0, i.e., Internet of Things (IoT) technologies. Successes in the implementation of this technology have led to the development of new applications in the energy sector, such as energy management systems, small modular reactor (SMR) management systems, and alternative power supply systems based on renewable energy sources. The digital infrastructure of these management systems is characterized by a high "density of knowledge", which requires clarification of fundamental concepts in information theory, namely the content of the concepts "data", "information", "knowledge" and "meaning of knowledge". Special attention was given to defining the role of knowledge. The aim of this study is to further develop methods and models of semiotic theory by determining the role and place of logical as well as eight- and four-factor logical-semantic models of knowledge bases in semiotic space. The tasks: comparing existing knowledge representation methods and models. Research results: We found that the logical models used in the development of knowledge bases are based on the principles of artificial intelligence theory, which relies on sign system hypotheses and formal logic theory. The main drawback is the complexity of practical implementation in the form of expert systems. For logical-semantic models in the form of eight-vector graphic models, it was found that there is currently no theoretical justification for defining the vectors that form the coordinate axes, making these models unique to specific subject areas. It was determined that the advantage of using these methods is that an expert can independently form such a knowledge model. For logical-semantic models in the form of four-factor graphic models, there is a theoretical justification for defining the factors of the model that form the coordinate axes, making these models universal for specific subject areas. It was established that the advantage of these models is that they can be developed by experts without the involvement of a knowledge engineer. Therefore, it is proposed to use four-factor logical-semantic knowledge representation models for further application. It is also proposed to split the element "logical-semantic models" into two elements in the semiotic spatial model in vector K8 "Knowledge Representation Models", namely: "logical-semantic eight-vector models" and "logical-semantic four-factor models". Additionally, it is proposed to add the element "post-Cartesian representation of meta-knowledge" to the element "geometric" in vector K5 "Ideal Models". Conclusions: The theoretical basis for developing eight-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products on elements of the corresponding inter-coordinate matrices. The theoretical basis for the methodology of developing four-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products for elements of the corresponding inter-coordinate matrices, as well as for diametrically opposite pairs of factors in the form of dialectical unity of the concepts "general" and "particular." The application of logical-semantic knowledge representation models for alternative energy-source management systems will ensure increased energy efficiency. Other cases related to the development of databases for SMR digital infrastructure are discussed.
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Lee, Daniel T. "An Overview of Intelligent Decision Systems". Journal of Information Technology 4, nr 3 (wrzesień 1989): 123–35. http://dx.doi.org/10.1177/026839628900400302.
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System integration has been the goal for both academicians and practitioners for more than two decades. Tremendous efforts have been made toward the achievement of a unifying theory or at least a framework in system integration. Unfortunately, the goal has not been achieved because of the dynamic nature of the subject. The purpose of this paper is to conduct a systematic analysis of the related subjects in system integration and to develop a framework which can be used to guide researchers and practitioners in system integration development. Traditional data modelling techniques and knowledge representation methodologies will be reviewed first. Major emphasis will be placed on object-oriented system integration and hybrid knowledge representation methods for developing an integrated intelligent decision system for next generation decision making.
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Otero, R. P., R. Marín, J. Mira i A. Barreiro. "A Modular Knowledge Base for the Follow-Up of Clinical Protocols". Methods of Information in Medicine 32, nr 05 (1993): 373–81. http://dx.doi.org/10.1055/s-0038-1634947.
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AbstractFrom the knowledge engineering point of view, the observation of patients subjected to clinical protocols of therapy constitutes a domain characterized by the existence of strongly structured knowledge. We have approached the problem from the perspective of a homogeneous and modular knowledge representation theory, based on the concept of Generalized Magnitude. This concept arises from identifying and collecting all possible facts of a domain established a priori, and being inspired by the concept of physical magnitudes. The Generalized Magnitudes scheme includes temporal extensions necessary to solve a medical problem for which exists a therapy and a follow-up plan with temporal specifications, and also facilitates the creation of advisory expert systems.
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Pang, Jinzhong, Xiaoyan Zhang i Weihua Xu. "Attribute Reduction in Intuitionistic Fuzzy Concept Lattices". Abstract and Applied Analysis 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/271398.
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As an effective tool for knowledge discovery, concept lattice has been successfully applied to various fields. And one of the key problems of knowledge discovery is attribute reduction. In order to understand the problems better, the attribute reduction is necessary to perfect the theory as well as expand application of concept lattice. This paper introduces the intuitionistic fuzzy theory into the concept lattice theory and proposes a kind of intuitionistic fuzzy concept lattice. Then, an approach to attribute reduction based on the discernibility matrix is proposed and investigated, which makes the discovery of implicit knowledge easier and the representation simpler in data; furthermore, the theory of concept lattice is perfected. The theory of intuitionistic fuzzy concept lattice is useful and meaningful in view of the complexity and fuzziness of information in real world, and the potential value of dealing with information is expected in the future.
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SUN, XINGMING, HUOWANG CHEN, LIHUA YANG i Y. Y. TANG. "MATHEMATICAL REPRESENTATION OF A CHINESE CHARACTER AND ITS APPLICATIONS". International Journal of Pattern Recognition and Artificial Intelligence 16, nr 06 (wrzesień 2002): 735–47. http://dx.doi.org/10.1142/s0218001402001939.
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In this paper, a novel method to express Chinese characters mathematically is presented based on the knowledge of the structure of Chinese characters. Each Chinese character can be denoted by a mathematical expression in which the operands are components of Chinese characters and the operators are the location relations between the components. Five hundred five components are selected and 6 operators are defined to express all the Chinese characters successfully. These mathematical expressions of Chinese characters are simple, natural, and can be operated like the common mathematical expression of numbers. It makes Chinese information processing much simpler than before. This theory has been applied successfully in fonts automation, Chinese information transmission among different platforms and different operating systems on Internet, and knowledge discovery of the structure of Chinese characters. It can also be applied extensively to many areas such as typesetting, advertising, packing design, virtual library, network transmission, pattern recognition and Chinese mobile communication.