scholarly journals Knowledge Representation and Reasoning — A History of DARPA Leadership

AI Magazine ◽  
2020 ◽  
Vol 41 (2) ◽  
pp. 9-21
Author(s):  
Richard Fikes ◽  
Tom Garvey
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Knowledge Representation And Reasoning ◽  
Research Projects ◽  
Artificial Intelligence Research ◽  
Fundamental Goal ◽  
Computational Support ◽  
Intelligent Behavior ◽  
History Of ◽  
The Impact

A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from the Defense Advanced Research Projects Agency and other agencies. This article highlights several decades of advances in knowledge representation and reasoning methods, paying particular attention to research on planning and on the impact of the Defense Advanced Research Projects Agency’s support.

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and thus Semantic Interoperability, based in HL7 v2.xml Messaging

2013 ◽  
pp. 205-214 ◽  
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Ontology Mapping ◽  
Knowledge Representation And Reasoning ◽  
Healthcare Applications ◽  
Current Trends ◽  
Web Tools ◽  
Ontology Population ◽  
Tools And Techniques

The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease the path to the fundamental concept of Shared Semantics. In this chapter, the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. The authors present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi-automated ontology population, and the current trends about knowledge representation and reasoning that concur to the proposed achievement.

scholarly journals A Brief Overview of Artificial Intelligence in South Africa

AI Magazine ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 99-103 ◽  
Author(s):  
Alexander Ferrein ◽  
Thomas Meyer
Keyword(s):  
Artificial Intelligence ◽  
South Africa ◽  
Knowledge Representation ◽  
Computational Intelligence ◽  
Mobile Robotics ◽  
Current Level ◽  
Knowledge Representation And Reasoning ◽  
Human Language ◽  
Language Technologies

One of the consequences of the growth in AI research in South Africa in recent years is the establishment of a number of research hubs involved in AI activities ranging from mobile robotics and computational intelligence, to knowledge representation and reasoning, and human language technologies. In this survey we take the reader through a quick tour of the research being conducted at these hubs, and touch on an initiative to maintain and extend the current level of interest in AI research in the country.

Artificial Intelligence-Based Knowledge Representation and Reasoning

2021 ◽  
pp. 134-149
Author(s):  
Dimpal Tomar ◽  
Pradeep Tomar
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Knowledge Representation ◽  
Knowledge Bases ◽  
Knowledge Representation And Reasoning ◽  
Artificial Intelligent ◽  
Logical Interpretation ◽  
Quality Of Higher Education ◽  
The Right

The quality of higher education can be enhanced only by upgrading the content and skills towards knowledge. Hence, knowledge representation and reasoning play a chief role to represent the facts, beliefs, and information, and inferring the logical interpretation of represented knowledge stored in the knowledge bases. This chapter provide a broad overview of knowledge, representation, and reasoning along with the related art of study in the field of higher education. Various artificial intelligent-based knowledge representation and reasoning techniques and schemes are provided for better representation of facts, beliefs, and information. Various reasoning types are discussed in order to infer the right meaning of the knowledge followed by various issues of knowledge representation and reasoning. .

An Overview of the KL-ONE Knowledge Representation System**The history of the ideas in KL-ONE is too complex to recount here, and the number of contributors to this whole area of work makes it inevitable that we will forget to mention many who deserve to be acknowledged. At the very least, we would like to extend our thanks to these people, who have been intimately involved with KL-ONE, KL-TWO, and Krypton: Danny Bobrow, Rusty Bobrow, Phil Cohen, Richard Fikes, Mike Freeman, Jeff Gibbons, Victoria Gilbert, Brad Goodman, Norton Greenfeld, Austin Henderson, David Israel, Henry Leitner, Hector Levesque, Bob Lingard, Tom Lipkis, Bill Mark, Peter Patel-Schneider, Candy Sidner, Mark Stefik, Marc Vilain, David Wilczynski, Bill Woods, Martin Yonke, and Frank Zdybel.This research was supported in part by the Defense Advanced Research Projects Agency under Contract N00014-77-C-0378. Views and conclusions contained in this paper are the authors' and should not be interpreted as representing the official opinion or policy of DARPA, the U.S. Government, or any person or agency connected with them.Correspondence and requests for reprints should be sent to Ronald J. Brachman, AT&T, Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ 07974.

1989 ◽  
pp. 207-230 ◽  
Author(s):  
RONALD J. BRACHMAN ◽  
JAMES G. SCHMOLZE
Keyword(s):  
Knowledge Representation ◽  
Research Projects ◽  
Representation System ◽  
History Of ◽  
Knowledge Representation System ◽  
The U.S ◽  
Advanced Research Projects Agency

scholarly journals From Musical Grammars to Music Cognition in the 1980s and 1990s: Highlights of the History of Computer-Assisted Music Analysis

2007 ◽  
Vol 43 (2) ◽  
pp. 371-396
Author(s):  
Nico Schüler
Keyword(s):  
Artificial Intelligence ◽  
Music Cognition ◽  
Music Analysis ◽  
Human Cognition ◽  
Computer Assisted ◽  
Artificial Intelligence Research ◽  
New Methods ◽  
Technical Developments ◽  
History Of ◽  
Analytical Approaches

While approaches that had already established historical precedents – computer-assisted analytical approaches drawing on statistics and information theory – developed further, many research projects conducted during the 1980s aimed at the development of new methods of computer-assisted music analysis. Some projects discovered new possibilities related to using computers to simulate human cognition and perception, drawing on cognitive musicology and Artificial Intelligence, areas that were themselves spurred on by new technical developments and by developments in computer program design. The 1990s ushered in revolutionary methods of music analysis, especially those drawing on Artificial Intelligence research. Some of these approaches started to focus on musical sound, rather than scores. They allowed music analysis to focus on how music is actually perceived. In some approaches, the analysis of music and of music cognition merged. This article provides an overview of computer-assisted music analysis of the 1980s and 1990s, as it relates to music cognition. Selected approaches are being discussed.

scholarly journals The Discerning Ear: Cardiac Auscultation in the Era of Artificial Intelligence and Telemedicine

2021 ◽  
Author(s):  
Vivek Jani ◽  
David A Danford ◽  
W Reid Thompson ◽  
Andreas Schuster ◽  
Cedric Manlhiot ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Assisted ◽  
Cardiac Auscultation ◽  
Cardiac Murmur ◽  
New Era ◽  
History Of ◽  
The Impact ◽  
Paradigm Shifting ◽  
Insight Into

Abstract Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of pediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, artificial intelligence assisted auscultation, and the implications of these technologies on telemedicine in pediatric cardiology. It is our hope that this article provides an updated perspective on the impact of artificial intelligence on cardiac auscultation for the modern pediatric cardiologist.

scholarly journals Model-theoretic Characterizations of Existential Rule Languages

2020 ◽  
Author(s):  
Heng Zhang ◽  
Yan Zhang ◽  
Guifei Jiang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Computer Science ◽  
Model Theory ◽  
Deep Understanding ◽  
Knowledge Representation And Reasoning ◽  
Complexity Bounds ◽  
Rule Languages ◽  
Finite Structures

Existential rules, a.k.a. dependencies in databases, and Datalog+/- in knowledge representation and reasoning recently, are a family of important logical languages widely used in computer science and artificial intelligence. Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for the class of arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these results, complexity bounds for the rewritability of above languages are also identified.

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