Semantic Networks In Artificial Intelligence

Author : John F. Sowa
Publisher : Morgan Kaufmann
Page : 594 pages
File Size : 43,8 Mb
Release : 2014-07-10
Category : Computers
ISBN : 9781483221144

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Principles of Semantic Networks by John F. Sowa Pdf

Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.

Author : Fritz W. Lehmann,Ervin Y. Rodin
Publisher : Pergamon
Page : 776 pages
File Size : 44,9 Mb
Release : 1992
Category : Artificial intelligence
ISBN : UOM:39015028445552

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Semantic Networks in Artificial Intelligence by Fritz W. Lehmann,Ervin Y. Rodin Pdf

Hardbound. Semantic Networks are graphic structures used to represent concepts and knowledge in computers. Key uses include natural language understanding, information retrieval, machine vision, object-oriented analysis and dynamic control of combat aircraft. This major collection addresses every level of reader interested in the field of knowledge representation. Easy to read surveys of the main research families, most written by the founders, are followed by 25 widely varied articles on semantic networks and the conceptual structure of the world. Some extend ideas of philosopher Charles S Peirce 100 years ahead of his time. Others show connections to databases, lattice theory, semiotics, real-world ontology, graph-grammers, lexicography, relational algebras, property inheritance and semantic primitives. Hundreds of pictures show semantic networks as a visual language of thought.

Author : John Sowa
Publisher : Unknown
Page : 0 pages
File Size : 40,6 Mb
Release : 2014
Category : Electronic
ISBN : OCLC:1371785292

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Principles of Semantic Networks by John Sowa Pdf

Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.

Author : Lokendra Shastri
Publisher : Pitman Publishing
Page : 240 pages
File Size : 51,9 Mb
Release : 1988
Category : Computers
ISBN : UOM:39015013829497

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Semantic Networks by Lokendra Shastri Pdf

Shastri’s book describes how a high-level specification of hierarchically structured knowledge about concepts and their properties may be encoded as a massively parallel network of a simple processing elements. The evidential formalization of semantic networks leads to a principled treatment of exceptions, multiple inheritance and conflicting information during inheritance, and the best match or partial match computation during recognition. This formalization offers semantically justifiable solutions to a larger class of problems than existing formulations (e.g. default logic). The network operates without the intervention of a central controller or interpreter. The knowledge as well as mechanisms for drawing limited inferences on it are encoded within the network. It uses controlled spreading activation to solve inheritance and recognition problems in time proportional to the depth of the conceptual hierarchy independent of the total number of concepts in the conceptual structure. The number of nodes in the connectionist network is at most quadratic in the number of concepts. The book has six chapters and one appendix. After the introduction in chapter 1 semantic networks their properties and formalizations are discussed in chapter 2. Especially the significance of inheritance and recognition and the evidential approach to it is pointed out here. Chapter 3 specifies a knowledge representation language. The problems of inheritance and recognition are reformulated in this language. In chapter 4 the evidential formalization and its application to inheritance and recognition are demonstrated. Section 4.1 derives an evidence combination rule. In the following two sections this rule is compared to the DEMPSTER-SHAFER evidence combination rule (section 4.2) and to the BAYES’ rule for computing conditional probabilities. The next two sections develop solutions to evidential inheritance (section 4.4) and evidential recognition (section 4.5) together with constraints for a conceptual structure. The connectionist realization of the memory network is developed in chapter 5. First the need for parallelism is discussed (section 5.1), then the connectionist model (section 5.2) and other massively parallel models of semantic memory (section 5.3) are reviewed. The connectionist encoding of the high-level specification is described in section 5.4 together with the connectivity and computational characteristics of node types. This is followed by examples of network encoding (section 5.5) and the elaboration of some implementation details (section 5.6). In section 5.7 and appendix A there is a proof that the proposed network solves the inheritance and recognition problem in accordance with the evidential formulation and in time proportional to the depth of the conceptual hierarchy. Section 5.8 describes the simulation of the proposed system on a conventional computer together with simulation runs of test examples often cited as being problematic. The book ends with a general discussion (chapter 6).

Author : Nicholas V. Findler
Publisher : Academic Press
Page : 481 pages
File Size : 52,9 Mb
Release : 2014-05-10
Category : Reference
ISBN : 9781483263014

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Associative Networks by Nicholas V. Findler Pdf

Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.

Author : Dash, Sujata,Subudhi, Bidyadhar
Publisher : IGI Global
Page : 514 pages
File Size : 51,8 Mb
Release : 2016-06-20
Category : Computers
ISBN : 9781522504283

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Handbook of Research on Computational Intelligence Applications in Bioinformatics by Dash, Sujata,Subudhi, Bidyadhar Pdf

Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

Author : Gerhard Sagerer,Heinrich Niemann
Publisher : Advances in Computer Vision and Machine Intelligence
Page : 520 pages
File Size : 54,6 Mb
Release : 1997-09-30
Category : Computers
ISBN : UOM:39015040543889

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Semantic Networks for Understanding Scenes by Gerhard Sagerer,Heinrich Niemann Pdf

The explosion in the use of digital imaging in recent years has made it necessary to develop computer languages that can efficiently translate photographic images into their digital analogues. This state-of-the-science guide presents the technical problems that need to be overcome in the development of this technology. The text proceeds from a review of the standard models and system architectures in use today to new systems under investigation. Chapters cover: segmentation knowledge representation languages criteria for judgment search and control algorithms explanation in a semantic network applications in medical and industrial contexts, as well as those involved in speech understanding. £/LIST£

Author : Gordon A. Ringland,David A. Duce
Publisher : John Wiley & Sons
Page : 286 pages
File Size : 45,8 Mb
Release : 1988
Category : Computers
ISBN : UOM:39015013840700

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Approaches to Knowledge Representation by Gordon A. Ringland,David A. Duce Pdf

Author : Justin E. Lane
Publisher : Bloomsbury Publishing
Page : 237 pages
File Size : 50,9 Mb
Release : 2021-05-06
Category : Religion
ISBN : 9781350103573

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Understanding Religion Through Artificial Intelligence by Justin E. Lane Pdf

In Understanding Religion through Artificial Intelligence, Justin E. Lane looks at the reasons why humans feel they are part of a religious group, despite often being removed from other group members by vast distances or multiple generations. To achieve this, Lane offers a new perspective that integrates religious studies with psychology, anthropology, and data science, as well as with research at the forefront of Artificial Intelligence (AI). After providing a critical analysis of approaches to religion and social cohesion, Lane proposes a new model for religious studies, which he calls the “Information Identity System.” This model focuses on the idea of conceptual ties: links between an individual's self-concept and the ancient beliefs of their religious group. Lane explores this idea through real-world examples, ranging from the rise in global Pentecostalism, to religious extremism and self-radicalization, to the effect of 9/11 on sermons. Lane uses this lens to show how we can understand religion and culture today, and how we can better contextualize the changes we see in the social world around us.

Author : Vladimir Golenkov,Victor Krasnoproshin,Vladimir Golovko,Elias Azarov
Publisher : Springer Nature
Page : 271 pages
File Size : 44,9 Mb
Release : 2020-10-24
Category : Computers
ISBN : 9783030604479

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Open Semantic Technologies for Intelligent System by Vladimir Golenkov,Victor Krasnoproshin,Vladimir Golovko,Elias Azarov Pdf

This book constitutes the refereed proceedings of the 10th International Conference on Open Semantic Technologies for Intelligent System, OSTIS 2020, held in Minsk, Belarus, in February 2020. The 14 revised full papers and 2 short papers were carefully reviewed and selected from 62 submissions. The papers mainly focus on standardization of intelligent systems and cover wide research fields including knowledge representation and reasoning, semantic networks, natural language processing, temporal reasoning, probabilistic reasoning, multi-agent systems, intelligent agents.

Author : Norman Foo
Publisher : Springer
Page : 518 pages
File Size : 55,9 Mb
Release : 2007-12-07
Category : Computers
ISBN : 9783540466956

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Advanced Topics in Artificial Intelligence by Norman Foo Pdf

The 12th Australian Joint Conference on Artificial Intelligence (AI'QQ) held in Sydney, Australia, 6-10 December 1999, is the latest in a series of annual re gional meetings at which advances in artificial intelligence are reported. This series now attracts many international papers, and indeed the constitution of the program committee reflects this geographical diversity. Besides the usual tutorials and workshops, this year the conference included a companion sympo sium at which papers on industrial appUcations were presented. The symposium papers have been published in a separate volume edited by Eric Tsui. Ar99 is organized by the University of New South Wales, and sponsored by the Aus tralian Computer Society, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Computer Sciences Corporation, the KRRU group at Griffith University, the Australian Artificial Intelligence Institute, and Neuron- Works Ltd. Ar99 received over 120 conference paper submissions, of which about o- third were from outside Australia. Prom these, 39 were accepted for regular presentation, and a further 15 for poster display. These proceedings contain the full regular papers and extended summaries of the poster papers. All papers were refereed, mostly by two or three reviewers selected by members of the program committee, and a list of these reviewers appears later. The technical program comprised two days of workshops and tutorials, fol lowed by three days of conference and symposium plenary and paper sessions.

Author : Stefano Spaccapietra,Lois Delcambre
Publisher : Springer
Page : 163 pages
File Size : 53,7 Mb
Release : 2009-11-16
Category : Computers
ISBN : 9783642105623

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Journal on Data Semantics XIV by Stefano Spaccapietra,Lois Delcambre Pdf

The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence. Volume XIV results from a rigorous selection among 21 full papers received in response to a call for contributions issued in September 2008.

Author : K.R. Chowdhary
Publisher : Springer Nature
Page : 730 pages
File Size : 45,8 Mb
Release : 2020-04-04
Category : Computers
ISBN : 9788132239727

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Fundamentals of Artificial Intelligence by K.R. Chowdhary Pdf

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.

Author : Thiago Christiano Silva,Liang Zhao
Publisher : Springer
Page : 331 pages
File Size : 47,5 Mb
Release : 2016-01-28
Category : Computers
ISBN : 9783319172903

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Machine Learning in Complex Networks by Thiago Christiano Silva,Liang Zhao Pdf

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Author : Richard Golden
Publisher : CRC Press
Page : 525 pages
File Size : 43,5 Mb
Release : 2020-06-24
Category : Computers
ISBN : 9781351051491

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Statistical Machine Learning by Richard Golden Pdf

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.