Results for 'Knowledge Representation'

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  1.  51
    Sophisticated Knowledge Representation and Reasoning Requires Philosophy.Selmer Bringsjord, Micah Clark & Joshua Taylor - forthcoming - In Ruth Hagengruber (ed.), Philosophy's Relevance in Information Science.
    Knowledge Representation and Reasoning (KR&R) is based on the idea that propositional content can be rigorously represented in formal languages long the province of logic, in such a way that these representations can be productively reasoned over by humans and machines; and that this reasoning can be used to produce knowledge-based systems (KBSs). As such, KR&R is a discipline conventionally regarded to range across parts of artificial intelligence (AI), computer science, and especially logic. This standard view of (...)
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  2.  1
    Logical Knowledge Representation of Regulatory Relations in Biomedical Pathways.Sine Zambach & Jens Ulrik Hansen - 2010 - In S. Khuri, L. Lhotská & N. Pisanti (eds.), Information Technology in Bio- and Medical Informatics, ITBAM 2010. ITBAM 2010. Lecture Notes in Computer Science, vol 6266. Berlin, Germany: Springer.
    Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller sections of a pathway. As an alternative, in this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a (...)
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  3.  45
    Knowledge Representation and Classical Logic.Vladimir Lifschitz, L. Morgenstern & D. Plaisted - manuscript
    in Handbook of Knowledge representation, Elsevier, 2008.
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  4.  23
    Design Knowledge Representation: An Ontological Perspective.Emilio M. Sanfilippo, Claudio Masolo & Daniele Porello - 2015 - In Proceedings of the 1st Workshop on Artificial Intelligence and Design, {A} workshop of the {XIV} International Conference of the Italian Association for Artificial Intelligence (AI*IA 2015), Ferrara, Italy, September 22, 2015. pp. 41-54.
    We present a preliminary high-level formal theory, grounded on knowledge representation techniques and foundational ontologies, for the uniform and integrated representation of the different kinds of (quali- tative and quantitative) knowledge involved in the designing process. We discuss the conceptual nature of engineering design by individuating and analyzing the involved notions. These notions are then formally charac- terized by extending the DOLCE foundational ontology. Our ultimate purpose is twofold: (i) to contribute to foundational issues of design; (...)
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  5.  47
    Knowledge Representation by Schemata in Financial Expert Systems.P. Lévine & J. -Ch Pomerol - 1989 - Theory and Decision 27 (1-2):147-161.
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  6.  2
    Knowledge Representations Derived From Semantic Fluency Data.Jeffrey C. Zemla - 2022 - Frontiers in Psychology 13.
    The semantic fluency task is commonly used as a measure of one’s ability to retrieve semantic concepts. While performance is typically scored by counting the total number of responses, the ordering of responses can be used to estimate how individuals or groups organize semantic concepts within a category. I provide an overview of this methodology, using Alzheimer’s disease as a case study for how the approach can help advance theoretical questions about the nature of semantic representation. However, many open (...)
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  7. Logic in Knowledge Representation and Reasoning: Central Topics Via Readings.Luis M. Augusto - manuscript
    Logic has been a—disputed—ingredient in the emergence and development of the now very large field known as knowledge representation and reasoning. In this book (in progress), I select some central topics in this highly fruitful, albeit controversial, association (e.g., non-monotonic reasoning, implicit belief, logical omniscience, closed world assumption), identifying their sources and analyzing/explaining their elaboration in highly influential published work.
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  8.  21
    On Knowledge Representation in Belief Networks.Bruce Abramson - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 86--96.
  9. Perceptual Knowledge, Representation and Imagination.Alan Thomas - manuscript
    The focus of this paper will be on the problem of perceptual presence and on a solution to this problem pioneered by Kant [1781; 1783] and refined by Sellars [Sellars, 1978] and Strawson [Strawson, 1971]. The problem of perceptual presence is that of explaining how our perceptual experience of the world gives us a robust sense of the presence of objects in perception over and above those sensory aspects of the object given in perception. Objects possess other properties which are, (...)
     
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  10.  38
    Knowledge Representation: An AI Perspective.Xabier Arrazola - 1993 - Theoria 8 (1):187-188.
  11.  1
    Structuralist Knowledge Representation: Paradigmatic Examples.Wolfgang Balzer, Joseph D. Sneed & Carles Ulises Moulines (eds.) - 2000 - Brill | Rodopi.
    Contents: Foreword. Wolfgang BALZER and C. ULISES MOULINES: Introduction. José A. DÍEZ CALZADA: Structuralist Analysis of Theories of Fundamental Measurement. Adolfo GARCÍA DE LA SIENRA and Pedro REYES: The Theory of Finite Games in Extensive Form. Hans Joachim BURSCHEID und Horst STRUVE: The Theory of Stochastic Fairness - its Historical Development, Formulation and Justification. Wolfgang BALZER and Richard MATTESSICH: Formalizing the Basis of Accounting. Werner DIEDERICH: A Reconstruction of Marxian Economics. Bert HAMMINGA and Wolfgang BALZER: The Basic Structure of Neoclassical (...)
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  12. Managing Knowledge Representation in Design.Jennifer Whyte, Boris Ewenstein, Mike Hales, Joe Tidd & David Gann - 2008 - In Harry Scarbrough (ed.), The Evolution of Business Knowledge. Oxford University Press.
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  13.  1
    Structuralist Knowledge Representation: Paradigmatic Examples.Joseph D. Sneed, Wolfgang Balzer & C.-U. Moulines (eds.) - 2000 - Rodopi.
  14.  1
    Knowledge Representation, Reflexive Reasoning and Discourse Processing.Jesus Ezquerro & Mauricio Iza - 1996 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 11 (2):125-145.
    Classical approaches such as frames, scripts,... have been unable to deal with the kind of inferences necessary in natural language processing situations such as text comprehension. Shastri & Ajjanagadde, proposed a local connectionist model for the sort of reasoning requiring such a fast inference. The problem with this system is that it controls only the adequacy of argument- fillers, leaving untouched the activation control issue, namely, why we perform certain inferences, and not others, in a given situation. The aim of (...)
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  15. Knowledge Representation, Reflexive Reasoning and Discourse Processing.Jesús Ezquerro & Mauricio Iza - 1996 - Theoria 11 (26):125-145.
  16. Knowledge Representation, Reflexive Reasoning and DIscourse Processing.Mauricio Iza & Jesús Ezquerro Martínez - 1996 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 11 (2):125-145.
     
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  17. Knowledge Representation.Arthur B. Markman - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
  18. Knowledge Representation and the Interrogative Model of Inquiry.Jaakko Hintikka - 1989 - In Marjorie Clay & Keith Lehrer (eds.), Knowledge and Skepticism. Westview Press.
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  19. Knowledge Representation: An AI Perspective.Han Reichgelt - 1991
     
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  20. Mathematical Knowledge Representation and Reasoning Based on Strong Relevant Logic.Jingde Cheng - 2002 - In Robert Trappl (ed.), Cybernetics and Systems. Austrian Society for Cybernetics Studies. pp. 789--794.
     
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  21.  7
    Knowledge Representation Systems for Groups of Agents.Cecylia M. Rauszer - 1994 - In Jan Wolenski (ed.), Philosophical Logic in Poland. Kluwer Academic Publishers. pp. 217--238.
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  22.  3
    Structuralist Knowledge Representation: Paradigmatic Examples.Rainer Reisenzein (ed.) - 2000 - Atlanta: Rodopi.
  23. Knowledge Representation for Natural Language Processing.Stuart C. Shapiro & Bell Hall - 1993 - Minds and Machines 3 (4):377-380.
  24. Knowledge Representation.Stuart C. Shapiro - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
     
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  25.  21
    Structuralist Knowledge Representation: Paradigmatic Examples.P. Lorenzano, W. Balzer, C. U. Moulines & J. Sneed - 2000 - In Joseph D. Sneed, Wolfgang Balzer & C.-U. Moulines (eds.), Structuralist Knowledge Representation: Paradigmatic Examples. Rodopi.
    Contents: Foreword. Wolfgang BALZER and C. ULISES MOULINES: Introduction. José A. DÍEZ CALZADA: Structuralist Analysis of Theories of Fundamental Measurement. Adolfo GARCÍA DE LA SIENRA and Pedro REYES: The Theory of Finite Games in Extensive Form. Hans Joachim BURSCHEID und Horst STRUVE: The Theory of Stochastic Fairness - its Historical Development, Formulation and Justification. Wolfgang BALZER and Richard MATTESSICH: Formalizing the Basis of Accounting. Werner DIEDERICH: A Reconstruction of Marxian Economics. Bert HAMMINGA and Wolfgang BALZER: The Basic Structure of Neoclassical (...)
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  26. Knowledge Representation, the World Wide Web, and the Evolution of Logic.Christopher Menzel - 2011 - Synthese 182 (2):269-295.
    It is almost universally acknowledged that first-order logic (FOL), with its clean, well-understood syntax and semantics, allows for the clear expression of philosophical arguments and ideas. Indeed, an argument or philosophical theory rendered in FOL is perhaps the cleanest example there is of “representing philosophy”. A number of prominent syntactic and semantic properties of FOL reflect metaphysical presuppositions that stem from its Fregean origins, particularly the idea of an inviolable divide between concept and object. These presuppositions, taken at face value, (...)
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  27.  70
    Paraconsistent Logics for Knowledge Representation and Reasoning: Advances and Perspectives.Walter A. Carnielli & Rafael Testa - 2020 - 18th International Workshop on Nonmonotonic Reasoning.
    This paper briefly outlines some advancements in paraconsistent logics for modelling knowledge representation and reasoning. Emphasis is given on the so-called Logics of Formal Inconsistency (LFIs), a class of paraconsistent logics that formally internalize the very concept(s) of consistency and inconsistency. A couple of specialized systems based on the LFIs will be reviewed, including belief revision and probabilistic reasoning. Potential applications of those systems in the AI area of KRR are tackled by illustrating some examples that emphasizes the (...)
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  28.  11
    Formal Ontologies in Biomedical Knowledge Representation.S. Schulz & L. Jansen - 2013 - In M.-C. Jaulent, C. U. Lehmann & B. Séroussi (eds.), Yearbook of Medical Informatics 8. pp. 132-146.
    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 (...)
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  29.  59
    Knowledge Representation: Two Kinds Of Emergence.Veikko Rantala - 2001 - Synthese 129 (2):195-209.
    Two different but closely related issues in current cognitive science will be considered in this essay. One is the controversial and extensively discussed question of how connectionist and symbolic representations of knowledge are related to each other. The other concerns the notion of connectionist learning and its relevance for the understanding of the distinction between propositional and nonpropositional knowledge. More specifically, I shall give an overview of a result in Rantala and Vadén establishing a limiting case correspondence between (...)
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  30.  2
    Handbook of Knowledge Representation.Frank Van Harmelen, Vladimir Lifschitz & Bruce Porter - 2008 - Elsevier.
    Knowledge representation, which lies at the core of artificial intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The aims are to help readers make their computer smarter, handle qualitative and uncertain information, and improve computational tractability.
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  31. Conceptual Structures: Knowledge Representations as Interlingua: 4th International Conference on Conceptual Structures, Iccs'96, Sydney, Australia, August 19 - 22, 1996, Proceedings. [REVIEW]Peter W. Eklund, Gerard Ellis & Graham Mann - 1996 - Springer Verlag.
    This volume constitutes the refereed proceedings of the Fourth International Conference on Conceptual Structures, ICCS '96, held in Sydney, Australia, in August 1996. The book presents five full papers by the invited speakers together with 15 revised full papers selected for presentation at the conference from a respectable number of submissions. The issues addressed are natural language processing, information retrieval, graph operations, conceptual graph and Peirce theory, knowledge acquisition, theorem proving and CG programming, and order-based organisation and encoding.
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  32.  38
    A Knowledge Representation Based on the Belnap's Four-Valued Logic.Yuri Kaluzhny & Alexei Yu Muravitsky - 1993 - Journal of Applied Non-Classical Logics 3 (2):189-203.
  33.  9
    Specialized Knowledge Representation and the Parameterization of Context.Pamela Faber & Pilar León-Araúz - 2016 - Frontiers in Psychology 7.
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  34.  2
    Knowledge, Representation and the Dynamics of Computation.Jiří Wiedermann & Jan van Leeuwen - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation of Reality: Humans, Other Living Organism and Intelligent Machines. Springer.
    Cognitive processes are often modelled in computational terms. Can this still be done if only minimal assumptions are made about any sort of representation of reality? Is there a purely knowledge-based theory of computation that explains the key phenomena which are deemed to be computational in both living and artificial systems as understood today? We argue that this can be done by means of techniques inspired by the modelling of dynamical systems. In this setting, computations are defined as (...)
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  35.  3
    Truth and Modality for Knowledge Representation.Raymond Turner - 1990 - MIT Press.
    Turner introduces various logics of truth and modality as part of a foundation for the construction of theories of knowledge representation.
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  36. Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets.Dan Geiger & David Heckerman - 1996 - Artificial Intelligence 82 (1-2):45-74.
  37. Knowledge Representation and Logic Programming.Michael Gelfond & Nicola Leone - 2002 - Artificial Intelligence 138 (1-2):1.
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  38.  4
    Visual Knowledge Representation of Moving Scenes.A. Chella, Μ Frixione & S. Gaglio - 2000 - Journal of Intelligent Systems 10 (4):377-404.
  39. Principles of Knowledge Representation.Gerhard Brewka - 1996 - Center for the Study of Language and Inf.
    The book contains a collection of eight survey papers written by some of the best researchers in foundations of knowledge representation and reasoning. It covers topics like theories of uncertainty, nonmonotonic and causal reasoning, logic programming, abduction, inductive logic programming, description logics, complexity in Artificial Intelligence, and model-based diagnosis. It thus provides an up-to-date coverage of recent approaches to some of the most challenging problems underlying knowledge representation and Artificial Intelligence in general.
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  40.  4
    A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data.Dong Zhong, Yi-An Zhu, Lanqing Wang, Junhua Duan & Jiaxuan He - 2020 - Complexity 2020:1-17.
    The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet (...)
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  41. Logic Programming and Knowledge Representation: Third International Workshop, LPKR '97, Port Jefferson, New York, USA, October 1997, Selected Papers'. [REVIEW]Jürgen Dix, Luís Moniz Pereira & Teodor C. Przymusinski - 2001 - Studia Logica 68 (2):299-300.
  42.  12
    A Description Logic Based Knowledge Representation Model for Concept Understanding.Farshad Badie - 2017 - In Jasper van den Herik, A. Rocha & J. Filipe (eds.), Agents and Artificial Intelligence. Cham, Switzerland: Springer.
    This research employs Description Logics in order to focus on logical description and analysis of the phenomenon of ‘concept understanding’. The article will deal with a formal-semantic model for figuring out the underlying logical assumptions of ‘concept understanding’ in knowledge representation systems. In other words, it attempts to describe a theoretical model for concept understanding and to reflect the phenomenon of ‘concept understanding’ in terminological knowledge representation systems. Finally, it will design an ontology that schemes the (...)
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  43.  65
    Knowledge Representation as Domain.Alexei Yu Muravitsky - 1997 - Journal of Applied Non-Classical Logics 7 (3):343-364.
    ABSTRACT This is a continuing attempt in a series of papers [KM 93, Mur 93, Mur 96] to show how computer-represented knowledge can be arranged as elements of an effectively represented semantic domain in the sense of [GS 90]. We present a direct deductive description of the domain, which was defined semantically in [KM 93], via the Scott's notion of information system. Also, the internal structure of the continuous ampliative operations coordinated with the domain's effective basis is established. Though (...)
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  44.  1
    Logic-Based Knowledge Representation.Peter Jackson, Han Reichgelt & Frank Van Harmelen - 1989 - Mit Press.
    This book explores the building of expert systems using logic for knowledge representation and meta-level inference for control. It presents research done by members of the expert systems group of the Department of Artificial Intelligence in Edinburgh, often in collaboration with others, based on two hypotheses: that logic is a suitable knowledge representation language, and that an explicit representation of the control regime of the theorem prover has many advantages. The editors introduce these hypotheses and (...)
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  45.  2
    Principles of Knowledge Representation.Gerhard Brewka - 1996 - Center for the Study of Language and Inf.
    The book contains a collection of eight survey papers written by some of the most excellent researchers in foundations of knowledge representation and reasoning. It covers topics like theories of uncertainty, nonmonotonic and causal reasoning, logic programming, abduction, inductive logic programming, description logics, complexity in Artificial Intelligence, and model based diagnosis. It thus provides an up-to-date coverage of recent approaches to some of the most challenging problems underlying knowledge representation and Artificial Intelligence in general.
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  46.  1
    Knowledge Representation and Commonsense Reasoning: Reviews of Four Books.Leora Morgenstern - 2006 - Artificial Intelligence 170 (18):1239-1250.
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  47. Ontology-Based Knowledge Representation of Experiment Metadata in Biological Data Mining.Scheuermann Richard, Kong Megan, Dahlke Carl, Cai Jennifer, Lee Jamie, Qian Yu, Squires Burke, Dunn Patrick, Wiser Jeff, Hagler Herb, Herb Hagler, Barry Smith & David Karp - 2009 - In Jake Chen & Stefano Lonardi (eds.), Biological Data Mining. Boca Raton: Chapman Hall / Taylor and Francis. pp. 529-559.
    According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...)
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  48.  6
    Relations in Knowledge Representation: An Interdisciplinary Study in Nyāya, Mīmāṁsā, Vyākaraṇa, Tantra, Modern Linguistics, and Artificial Intelligence in Computer Application.Keśavacandra Dāśa - 1991 - Sri Satguru Publications.
  49.  2
    Lexical Knowledge Representation and Natural Language Processing.James Pustejovsky & Branimir Boguraev - 1993 - Artificial Intelligence 63 (1-2):193-223.
  50.  28
    Biologically Plausible, Human‐Scale Knowledge Representation.Eric Crawford, Matthew Gingerich & Chris Eliasmith - 2016 - Cognitive Science 40 (4):782-821.
    Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony, “mesh” binding, and conjunctive binding. Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture approach (...)
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