We Need Algorithms That Can Make Explicit What Is Implicit

2017 ◽  
Author(s):  
Bernardo Cuenca Grau ◽  
Adolfo Plasencia
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Computer Science ◽  
Health Systems ◽  
Large Scale ◽  
Practical Applications ◽  
University Of Oxford ◽  
Comprehensive Health ◽  
Structured Information

In this dialogue, Bernardo Cuenca Grau, a computer scientist at the Department of Computer Science, University of Oxford, begins by explaining his research in technology based on ontologies and knowledge representation, somewhere between mathematics, philosophy, and computer science. He goes on to argue why we need to represent knowledge in a way that it can be processed by a computer and therefore enable automated reasoning of this knowledge using artificial intelligence. Later he explains how his investigation probes the limits of mathematics to find the most appropriate languages for developing practical applications. For example, the large-scale processing of structured information linked to comprehensive health systems. Bernardo is supportive of collective tools such as Wikipedia. He also discusses why in his opinion the success of a scientific or technological idea depends very much on luck, and why the semantic web has not been defined. Furthermore, he argues why bureaucracy confuses process with progress.

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 Efficient Results in Semantic Interoperability for Health Care

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  
Keyword(s):  
Health Care ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Large Scale ◽  
Recommendation System ◽  
Clinical Care ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
The Third ◽  
Research Activities

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

scholarly journals In Memoriam: RAYMOND REITER June 12, 1939 – September 16, 2002

2002 ◽  
Vol 3 (1) ◽  
pp. i-ix
Author(s):  
Jack Minker
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Computer Science ◽  
Theorem Proving ◽  
Royal Society ◽  
Research Scientist ◽  
University Of Toronto ◽  
The World ◽  
In Memoriam ◽  
The University

Raymond Reiter, Professor of computer science at the University of Toronto, a Fellow of the Royal Society of Canada, and winner of the 1993 – IJCAI Outstanding Research Scientist Award, died September 16, 2002, after a year-long struggle with cancer. Reiter, known throughout the world as “Ray,” made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving.

scholarly journals Art, Creativity, and the Potential of Artificial Intelligence

Arts ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 26 ◽  
Author(s):  
Marian Mazzone ◽  
Ahmed Elgammal
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Contemporary Art ◽  
21St Century ◽  
Art History ◽  
Large Scale ◽  
Style Analysis ◽  
Art Making ◽  
Making Art

Our essay discusses an AI process developed for making art (AICAN), and the issues AI creativity raises for understanding art and artists in the 21st century. Backed by our training in computer science (Elgammal) and art history (Mazzone), we argue for the consideration of AICAN’s works as art, relate AICAN works to the contemporary art context, and urge a reconsideration of how we might define human and machine creativity. Our work in developing AI processes for art making, style analysis, and detecting large-scale style patterns in art history has led us to carefully consider the history and dynamics of human art-making and to examine how those patterns can be modeled and taught to the machine. We advocate for a connection between machine creativity and art broadly defined as parallel to but not in conflict with human artists and their emotional and social intentions of art making. Rather, we urge a partnership between human and machine creativity when called for, seeing in this collaboration a means to maximize both partners’ creative strengths.

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 Secured authentication using CAPTCHA and visualized passcode

2018 ◽  
Vol 7 (1.8) ◽  
pp. 72
Author(s):  
C Narasimha ◽  
M Sreedevi
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Internet Technology ◽  
Related Services ◽  
Practical Applications ◽  
Privacy Requirements ◽  
Privacy Model ◽  
Privacy Issues ◽  
Dictionary Attacks

Numerical harms initiate many privacy characteristics like cryptography. But Artificial intelligence is the best aid for the current privacy requirements, still not properly applied for privacy issues. Now we introduce a new privacy model of privacy that uses Captcha model, in our privacy model we use both the Captcha and a visualized pass code. This model tolerates from most of the privacy attacks like dictionary attacks, keyboard logging attacks, forwarding methods, search set methods etc., This model is well suitable for either a small or large scale applications, the primary intention is improving privacy in internet technology and related services. In this methodology solving a Captcha is a challenge in every login. Finally to improve privacy for practical applications this technique is efficient.

Research on Automatic Construction of Financial Ontology Using Chinese Encyclopedia Resource

2013 ◽  
Vol 765-767 ◽  
pp. 1240-1244
Author(s):  
Qian Mo ◽  
Shu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Semantic Web ◽  
Large Scale ◽  
Semantic Information ◽  
Dominant Role ◽  
Experimental Results ◽  
Automatic Construction ◽  
Scale Creation

Ontology plays a dominant role in a growing number of different fields, such as information retrieval, artificial intelligence, semantic Web and knowledge management, etc. However, manual construction of large ontology is not feasible. This article discusses how to create Financial Ontology automatically from a resource of Chinese Encyclopedia. Financial Ontology includes Is-A relationship, Class-Instance relationship, Attribute-of relationship and Synonym relationship. Experimental Results show us that the constructed Financial Ontology has great advantages in the large scale, creation cost and the richness of semantic information.

scholarly journals Building Resilient Health Systems: Patient Safety during COVID-19 and Lessons for the Future

2021 ◽  
Vol 23 (1) ◽  
pp. 166-181
Author(s):  
Sharda Narwal ◽  
Susmit Jain
Keyword(s):  
Patient Safety ◽  
Health Systems ◽  
Large Scale ◽  
Health Sector ◽  
Control Measures ◽  
Infection Prevention And Control ◽  
Safety Issues ◽  
Comprehensive Health ◽  
Media Reports ◽  
Prevention And Control Measures

Background: The COVID-19 pandemic has profoundly impacted the country’s health systems and diminished its capability to provide safe and effective healthcare. This article attempts to review patient safety issues during COVID-19 pandemic in India, and derive lessons from national and international experiences to inform policy actions for building a ‘resilient health system’. Methods: Systematic review of existing published articles, government and media reports was undertaken. Online databases were searched using key terms related to patient safety during COVID-19 and health systems resilience. Seventy-three papers were included dependent on their relevance to research objectives. Findings: Patient safety was impacted during COVID-19, owing to sub-optimal infection prevention and control measures coupled with reduced access to essential health services. This was largely due to inadequate infrastructure, human and material resources resulting from chronic underinvestment in public health systems, paucity of reliable data for evidence-based actions and limited leadership and regulatory capacity. Conclusions: India’s health systems were found ill prepared to tackle large-scale pandemic, which has major implications for patient safety. The shortcomings observed in the COVID-19 response must be rectified and comprehensive health sector reforms should be initiated for building agile and resilient health systems that can withstand future pandemics.

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