Addressing the ‘Tower of Babel’ of pesticide regulations: an ontology for supporting pest-control decisions

2019 ◽  
Vol 157 (6) ◽  
pp. 493-503
Author(s):  
A. Goldstein ◽  
L. Fink ◽  
O. Raphaeli ◽  
A. Hetzroni ◽  
G. Ravid
Keyword(s):  
Pest Control ◽  
Web Application ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Core Component ◽  
Tower Of Babel ◽  
Comprehensive Method ◽  
Ontology Language ◽  
Efficiency And Effectiveness ◽  
Country Specific

AbstractFarmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.

scholarly journals A Framework for Evaluating Agricultural Ontologies

2021 ◽  
Vol 13 (11) ◽  
pp. 6387
Author(s):  
Anat Goldstein ◽  
Lior Fink ◽  
Gilad Ravid
Keyword(s):  
Semantic Web ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Development Process ◽  
Evaluation Methods ◽  
Evaluation Procedure ◽  
Ontology Development ◽  
Formal Representation ◽  
Core Component ◽  
Selection Of

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.

Hybrid Computational Intelligence and the Basic Concepts and Recent Advances

2018 ◽  
pp. 180-190
Author(s):  
Georgios Dounias
Keyword(s):  
Computational Intelligence ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Hybrid Intelligent Systems ◽  
Complex Problems ◽  
Recent Advances ◽  
New Findings ◽  
Wide Range ◽  
Computational Intelligent ◽  
Computational Technology

In this paper computational intelligence and its major methodologies are introduced in the first place, and then hybrid intelligent systems are defined and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade, is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The paper includes recent advances and new findings in the area of hybrid computational intelligence.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

scholarly journals COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cristian Tebé ◽  
Joan Valls ◽  
Pau Satorra ◽  
Aurelio Tobías
Keyword(s):  
Data Visualization ◽  
Web Application ◽  
Case Fatality ◽  
Epidemiological Surveillance ◽  
Fatality Rate ◽  
Specific Data ◽  
User Friendly ◽  
And Control ◽  
Shiny Application ◽  
Country Specific

Abstract Background Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. Results Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. Conclusions The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

The Applications of Description Logics in Natural Language Processing

2011 ◽  
Vol 181-182 ◽  
pp. 236-241
Author(s):  
Xian Yi Cheng ◽  
Chen Cheng ◽  
Qian Zhu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Description Logic ◽  
Description Logics ◽  
Semantic Interpretation ◽  
Natural Language Semantic ◽  
Ontology Language ◽  
Basic Ideas

As a sort of formalizing tool of knowledge representation, Description Logics have been successfully applied in Information System, Software Engineering and Natural Language processing and so on. Description Logics also play a key role in text representation, Natural Language semantic interpretation and language ontology description. Description Logics have been logical basis of OWL which is an ontology language that is recommended by W3C. This paper discusses the description logic basic ideas under vocabulary semantic, context meaning, domain knowledge and background knowledge.

Research of Maize Field Knowledge Representation Building Based on Ontology

2012 ◽  
Vol 546-547 ◽  
pp. 441-445
Author(s):  
Ying Zhang ◽  
Gui Fen Chen
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Semantic Level ◽  
Maize Field ◽  
Complex Knowledge ◽  
The Relationship ◽  
Knowledge Ontology ◽  
Knowledge Hierarchy

The knowledge representation of the traditional artificial intelligence used different modeling methods and the different development tools, it led to the lack of interoperability between all kinds of knowledge, ontology solved the problem. Ontology, which is a model in semantic and knowledge hierarchy describing the concept and the relationship between the concepts, has been the focus of the field of artificial intelligence since it was proposed. This paper explored the knowledge representation based on ontology in the field of artificial intelligence, built the maize domain knowledge ontology, the result shows: ontology can effectively solve the heterogeneous problem of expression of complex knowledge, makes the computer to understand information for the semantic level, and benefit to develop the intelligent systems of maize.

Semantic System for Attacks and Intrusions Detection

2015 ◽  
Vol 7 (4) ◽  
pp. 19-32
Author(s):  
Abdeslam El Azzouzi ◽  
Kamal Eddine El Kadiri
Keyword(s):  
Web Application ◽  
Description Logics ◽  
Attack Detection ◽  
Web Ontology Language ◽  
Detection Systems ◽  
Web Attacks ◽  
Ontology Language ◽  
Essential Properties ◽  
Prevention Methods ◽  
The Web

The increasing development of information systems complicate task of protecting against threats. They have become vulnerable to malicious attacks that may affect the essential properties such as confidentiality, integrity and availability. Then the security becomes an overriding concern. Securing a system begins with prevention methods that are insufficient to reduce the danger of attacks, that must be accomplished by intrusion and attack detection systems. In this paper, a method for detecting web application attacks is proposed. Unlike methods based on signatures, the proposed solution is a technique based on ontology. It describes the Web attacks, the HTTP request, and the application using semantic rules. The system is able to detect effectively the sophisticated attacks by analysing user requests. The semantic rules allow inference about the ontologies models to detect complex variations of web attacks. The ontologies models was developed using description logics which was based Web Ontology Language (OWL). The proposed system is able to be installed on an HTTP server.

scholarly journals A semantic-based decision support platform to assist products’ eco-labeling process

2017 ◽  
Vol 117 (7) ◽  
pp. 1340-1361 ◽  
Author(s):  
Da Xu ◽  
Mohamed Hedi Karray ◽  
Bernard Archimède
Keyword(s):  
Decision Support ◽  
Domain Knowledge ◽  
Service Providers ◽  
Laundry Detergent ◽  
Content Type ◽  
Ontology Language ◽  
Complex Process ◽  
Comprehensive Knowledge ◽  
Description Framework

Purpose With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels. Design/methodology/approach This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation. Findings Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper. Originality/value The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

scholarly journals An Interpretable Deep Learning Model for Automatic Sound Classification

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 850
Author(s):  
Pablo Zinemanas ◽  
Martín Rocamora ◽  
Marius Miron ◽  
Frederic Font ◽  
Xavier Serra
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Domain Knowledge ◽  
Learning Model ◽  
Unintended Effects ◽  
Learning Models ◽  
Time Frequency ◽  
Sound Classification ◽  
Proposed Model ◽  
Deep Learning Model

Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach.

scholarly journals Using reverse engineering to construct the platform independent model of a web application for student information systems

2013 ◽  
Vol 10 (4) ◽  
pp. 1557-1583 ◽  
Author(s):  
Igor Rozanc ◽  
Bostjan Slivnik
Keyword(s):  
Reverse Engineering ◽  
Web Application ◽  
Domain Knowledge ◽  
New Technology ◽  
Student Information Systems ◽  
Business Functions ◽  
Student Information ◽  
The One ◽  
Independent Model

A methodology for extracting the domain knowledge from an existing three-tier web application and subsequent formulation of the platform independent model (PIM) is described. As it was devised during a reverse engineering process of an existing web application which needed to be reimplemented on a new platform using new technology, it focuses on the domain knowledge and business functions. It produces the business model and the hypertext model leaving the presentation model aside. The methodology is semi-automated - the generation of the activity diagrams and parts of the hypertext model must be in part performed by an analyst, preferably the one with some domain knowledge. As the paper is primarily aimed at practitioners, a case study illustrating the application of the presented method is included.

Sign in / Sign up

Export Citation Format

Share Document