Session details: Artificial intelligence and agents: KRR - knowledge representation and reasoning track

2018 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Knowledge Representation And Reasoning

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. .

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.

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 Introduction to the TPLP Special Issue on User-oriented Logic Programming and Reasoning Paradigms

2019 ◽  
Vol 19 (2) ◽  
pp. 109-113
Author(s):  
STEFAN ELLMAUTHALER ◽  
CLAUDIA SCHULZ
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Unstructured Data ◽  
Knowledge Representation And Reasoning ◽  
Special Issue

With the rise of machine learning, and more recently the overwhelming interest in deep learning, knowledge representation and reasoning (KRR) approaches struggle to maintain their position within the wider Artificial Intelligence (AI) community. Often considered as part of thegood old-fashioned AI(Haugeland 1985) – like a memory of glorious old days that have come to an end – many consider KRR as no longer applicable (on its own) to the problems faced by AI today (Blackwell 2015; Garneloet al.2016). What they see are logical languages with symbols incomprehensible by most, inference mechanisms that even experts have difficulties tracing and debugging, and the incapability to process unstructured data like text.

scholarly journals Towards Consumer-Empowering Artificial Intelligence

2018 ◽  
Author(s):  
Giuseppe Contissa ◽  
Francesca Lagioia ◽  
Marco Lippi ◽  
Hans-Wolfgang Micklitz ◽  
Przemyslaw Palka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Civil Society ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Sudden Change ◽  
Balance Of Power ◽  
Knowledge Representation And Reasoning ◽  
Potential Threat ◽  
Artificial Intelligence And Law ◽  
Legal Domain

Artificial Intelligence and Law is undergoing a critical transformation. Traditionally focused on the development of expert systems and on a scholarly effort to develop theories and methods for knowledge representation and reasoning in the legal domain, this discipline is now adapting to a sudden change of scenery. No longer confined to the walls of academia, it has welcomed new actors, such as businesses and companies, who are willing to play a major role and seize new opportunities offered by the same transformational impact that recent AI breakthroughs are having on many other areas. As it happens, commercial interests create new opportunities but they also represent a potential threat to consumers, as the balance of power seems increasingly determined by the availability of data. We believe that while this transformation is still in progress, time is ripe for the next frontier of this field of study, where a new shift of balance may be enabled by tools and services that can be of service not only to businesses but also to consumers and, more generally, the civil society. We call that frontier consumer-empowering AI.

scholarly journals Knowledge representation for explainable artificial intelligence

2022 ◽  
Author(s):  
Joaquín Borrego-Díaz ◽  
Juan Galán Páez
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Philosophy Of Science ◽  
Black Box ◽  
Point Of View ◽  
Knowledge Representation And Reasoning ◽  
Computational Logic ◽  
Advantages And Disadvantages ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai

AbstractAlongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.

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