A KNOWLEDGE-BASED APPROACH TO COOPERATIVE RELATIONAL DATABASE QUERYING

2000 ◽  
Vol 14 (01) ◽  
pp. 73-90 ◽  
Author(s):  
JOSÉ LUÍS BRAGA ◽  
ALBERTO H. F. LAENDER ◽  
CLAUDINEY VANDER RAMOS
Keyword(s):  
Artificial Intelligence ◽  
Relational Database ◽  
Knowledge Bases ◽  
Application Domain ◽  
Query Formulation ◽  
Database Querying ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based

We present in this paper an approach to providing cooperativeness in database querying using artificial intelligence techniques. The main focus is a cooperative interface that assists nonexperienced and casual users in extracting useful answers from a relational database. Our approach relies on an architecture that comprises two knowledge bases which store rules that describe the application domain and guide the process of query formulation and answering. A subset of SQL is used for expressing queries, and the cooperative interface relieves the user from knowing its full syntax and the structure of the database.

Applying Artificial Intelligence to Financial Investing

2018 ◽  
pp. 1-14
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

scholarly journals Knowledge-Based Systems Manufacturing With Viability Ensuring

2021 ◽  
Author(s):  
Valeriya V. Gribova ◽  
Elena A. Shalfeeva
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Product Family ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Manufacturing Environment ◽  
Development Environment ◽  
Systematic Analysis ◽  
Knowledge Based ◽  
Medical Software

Abstract With highly increased competition, intelligent product manufacturing based on interpretable knowledge bases has been recognized as an effective method for building applications of explainable Artificial Intelligence that is the hottest topic in the field of Artificial Intelligence. The success of product family directly depends on how effective the viability mechanisms are laid down in its design. In this paper, a systematic cloud-based set of tool family is proposed to develop viable knowledge-based systems. For productive participation of domain and cognitive specialists in manufacturing, the knowledge base should be declarative, testable and integratable with other architectural components. Mechanisms to ensure KBS viability are provided in an ontology-oriented development environment, where each component is formed in terms of domain ontology by using the adaptable instrumental support. Due to the explicit separation of ontology from knowledge, it became possible to divide competencies between specialists creating an ontology and specialists creating a knowledge base. We rely on the fact that the activity of creating an ontology is significantly different from the activity of creating a knowledge base. Creating an ontology is a creative process that requires a systematic analysis of the domain area in order to identify common patterns among its knowledge.The characteristic properties of knowledge-based systems related to viability are described. It is explained, how these properties are provided in development environments implemented on cloud platform. The concept of a specialized manufacturing environment for knowledge-based system is introduced. The necessary set of tools for such ontology-oriented environment construction is determined. The example of tools for creating specialized manufacturing environments is the instruments implemented on the «IACPaaS» platform. The IACPaaS is already used for collective development of thematic cloud knowledge portals with viable knowledge-based systems. This specialized manufacturing environment has enabled the creation of multi-purpose medical software services to support specialist solutions based on knowledge being remotely improved by experts.

scholarly journals Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

2021 ◽  
Vol 7 ◽  
pp. e488
Author(s):  
Amir Masoud Rahmani ◽  
Elham Azhir ◽  
Saqib Ali ◽  
Mokhtar Mohammadi ◽  
Omed Hassan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimization Theory ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Complete Survey ◽  
The Internet Of Things

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

Applying Artificial Intelligence to Financial Investing

2019 ◽  
pp. 1-16
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence, this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this chapter summarizes key contributions of applying AI to financial investing as appears in the academic literature.

Research on Ontology Knowledge Based System of Meteorological and Hydrological Support

2013 ◽  
Vol 373-375 ◽  
pp. 1027-1030
Author(s):  
Wan Li ◽  
Bi Hua Zhou ◽  
Qi Zhang ◽  
Ya Peng Fu ◽  
Tao Wang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Sharing ◽  
Knowledge Base ◽  
Prototype System ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Class Testing ◽  
Ontology Language ◽  
Testing Algorithm ◽  
Positioning Algorithm

According to knowledge sharing&reusing problem and system extensibility&portability problem in meteorological and hydrological support, the knowledge concepts, attributes, instances, hierarchy and relationships were clarified; the ontological knowledge base was established by OWL ontology language and SWRL rule language. With the help of Racer, Protégé, Jess, and class positioning algorithm, class testing algorithm, rule testing algorithm, the meteorological and hydrological support prototype system was implemented by Eclipse, ProtegeInEclipse plug-in and Jena. The system includes the server, the client and the processing controller. It provides artificial intelligence techniques means for knowledge maintenance and usage in meteorological and hydrological field.

scholarly journals Evaluation of artificial intelligence techniques used in the diagnosis of failures in power plants

Respuestas ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 177-189
Author(s):  
Jesús Filander-Caratar ◽  
Andrés Mauricio-Valencia ◽  
Gladys Caicedo-Delgado ◽  
Cristian Chamorro
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Nuclear Power ◽  
Power Plants ◽  
Artificial Intelligence Techniques ◽  
Hybrid Techniques ◽  
Knowledge Based ◽  
Generation Plant ◽  
Power Generation Plant ◽  
Detection And Diagnosis

This article presents an evaluation about the research related to the development of computational tools based on artificial intelligence techniques, which focus on the detection and diagnosis of faults in the different processes associated with a power generation plant such as: hydroelectric, thermoelectric and nuclear power plants. Initially, the main techniques of artificial intelligence that allow the construction of intelligent systems in the area of fault diagnosis is described in a general way, techniques such as: fuzzy logic, neural networks, knowledge-based systems and hybrid techniques Subsequently A summary of the research based on each of these techniques is presented. Subsequently, the different articles found for each of the techniques are presented in tables, illustrating the year of publication and the description of the research carried out. The result of this work is the comparison and evaluation of each technique focused on the diagnosis of failures in power plants. The novelty of this work is that it presents an extensive bibliography of the applications of the different intelligent techniques in solving the problem of detection and diagnosis of failure in power plants

scholarly journals Applications of Ontologies in Knowledge Management Systems

2014 ◽  
Vol 65 (1) ◽  
pp. 78-80
Author(s):  
Zobia Rehman ◽  
Claudiu V. Kifor
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
21St Century ◽  
Organizational Knowledge ◽  
Knowledge Bases ◽  
Management Systems ◽  
Knowledge Management Systems ◽  
Artificial Intelligence Techniques ◽  
Poor Management

Abstract Enterprises are realizing that their core asset in 21st century is knowledge. In an organization knowledge resides in databases, knowledge bases, filing cabinets and peoples' head. Organizational knowledge is distributed in nature and its poor management causes repetition of activities across the enterprise. To get true benefits from this asset, it is important for an organization to “know what they know”. That’s why many organizations are investing a lot in managing their knowledge. Artificial intelligence techniques have a huge contribution in organizational knowledge management. In this article we are reviewing the applications of ontologies in knowledge management realm

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

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