scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

scholarly journals Knowledge machines

2018 ◽  
Vol 33 ◽  
Author(s):  
Paul Smart
Keyword(s):  
Intelligent Systems ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Theory And Practice ◽  
Philosophical Study ◽  
Engineering Systems ◽  
Web Based ◽  
Knowledge Based ◽  
Data Formats

AbstractThe World Wide Web has had a notable impact on a variety of epistemically relevant activities, many of which lie at the heart of the discipline of knowledge engineering. Systems like Wikipedia, for example, have altered our views regarding the acquisition of knowledge, while citizen science systems such as Galaxy Zoo have arguably transformed our approach to knowledge discovery. Other Web-based systems have highlighted the ways in which the human social environment can be used to support the development of intelligent systems, either by contributing to the provision of epistemic resources or by helping to shape the profile of machine learning. In the present paper, such systems are referred to asknowledge machines. In addition to providing an overview of the knowledge machine concept, the present paper reviews a number of issues that are associated with the scientific and philosophical study of knowledge machines. These include the potential impact of knowledge machines for the theory and practice of knowledge engineering, the role of social participation in the realization of knowledge-based processes, and the role of standardized, semantically enriched data formats in supporting thead hocassembly of special-purpose knowledge systems and knowledge processing pipelines.

scholarly journals Modeling Frameworks for Knowledge Engineering Approaches

2022 ◽  
pp. 15-20
Author(s):  
Daniel Ashlock
Keyword(s):  
Knowledge Base ◽  
Transfer Process ◽  
Knowledge Engineering ◽  
Knowledge Based Systems ◽  
Human Knowledge ◽  
Production Rules ◽  
Knowledge Based ◽  
New Ideas ◽  
The Creation ◽  
Applied Knowledge

Human knowledge was regarded as a transfer process into an applied knowledge base in the early 1980s as the creation of a Knowledge-Based Systems (KBS). The premise behind this transfer was that the KBS-required information already existed and only needed to be gathered and applied. Most of the time, the necessary information was gleaned through talking to professionals about how they handle particular problems. This knowledge was usually put to use in production rules, which were then carried out by a rule interpreter linked to them. Here, we demonstrate a number of new ideas and approaches that have emerged during the last few years. This paper presents MIKE, PROTÉGÉ-II, and Common KADS as three different modeling frameworks that may be used together or separately.

Using artificial intelligence techniques to support project management

1987 ◽  
Vol 1 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Raymond E. Levitt ◽  
John C. Kunz
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Project Management ◽  
Knowledge Engineering ◽  
Project Planning ◽  
Ai Planning ◽  
Engineering Project ◽  
Knowledge Processing ◽  
Domain Specific ◽  
Knowledge Based

AbstractThis paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

Application of Expert Systems Technology in Water Quality Modeling

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1045-1056 ◽  
Author(s):  
Thomas O. Barnwell ◽  
Linfield C. Brown ◽  
Wiktor Marek
Keyword(s):  
Water Quality ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Stream Water ◽  
Environmental Modeling ◽  
Water Quality Modeling ◽  
Stream Water Quality ◽  
Quality Model ◽  
Knowledge Based ◽  
Available Technology

Computerized modeling is becoming an integral part of decision making in water pollution control. Expert systems is an innovative methodology that can assist in building, using, and interpreting the output of these models. This paper reviews the use and evaluates the potential of expert systems technology in environmental modeling and describes elements of an expert advisor for the stream water quality model QUAL2E. Some general conclusions are presented about the tools available to develop this system, the level of available technology in knowledge-based engineering, and the value of approaching problems from a knowledge engineering perspective.

scholarly journals Sound Localization for Ad-Hoc Microphone Arrays

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3446
Author(s):  
Muhammad Usman Liaquat ◽  
Hafiz Suliman Munawar ◽  
Amna Rahman ◽  
Zakria Qadir ◽  
Abbas Z. Kouzani ◽  
...  
Keyword(s):  
Sound Localization ◽  
Sound Source ◽  
Ad Hoc ◽  
Dimensional Space ◽  
Computation Time ◽  
Location Estimation ◽  
Microphone Arrays ◽  
Sound Signal ◽  
Experimental Arrangement ◽  
Advantages And Disadvantages

Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

scholarly journals Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations, and Recommendations

2021 ◽  
Author(s):  
Lichao Xu ◽  
Szu-Yun Lin ◽  
Andrew W. Hlynka ◽  
Hao Lu ◽  
Vineet R. Kamat ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Data Exchange ◽  
Distributed Simulation ◽  
Distributed Data ◽  
Interactive Simulation ◽  
Integrated Simulation ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
High Level ◽  
Data Passing

AbstractThere has been a strong need for simulation environments that are capable of modeling deep interdependencies between complex systems encountered during natural hazards, such as the interactions and coupled effects between civil infrastructure systems response, human behavior, and social policies, for improved community resilience. Coupling such complex components with an integrated simulation requires continuous data exchange between different simulators simulating separate models during the entire simulation process. This can be implemented by means of distributed simulation platforms or data passing tools. In order to provide a systematic reference for simulation tool choice and facilitating the development of compatible distributed simulators for deep interdependent study in the context of natural hazards, this article focuses on generic tools suitable for integration of simulators from different fields but not the platforms that are mainly used in some specific fields. With this aim, the article provides a comprehensive review of the most commonly used generic distributed simulation platforms (Distributed Interactive Simulation (DIS), High Level Architecture (HLA), Test and Training Enabling Architecture (TENA), and Distributed Data Services (DDS)) and data passing tools (Robot Operation System (ROS) and Lightweight Communication and Marshalling (LCM)) and compares their advantages and disadvantages. Three specific limitations in existing platforms are identified from the perspective of natural hazard simulation. For mitigating the identified limitations, two platform design recommendations are provided, namely message exchange wrappers and hybrid communication, to help improve data passing capabilities in existing solutions and provide some guidance for the design of a new domain-specific distributed simulation framework.

Achieving structured knowledge management with a novel online group decision support system

2020 ◽  
pp. 026666692096984
Author(s):  
Wesley Shu ◽  
Songquan Pang ◽  
Minder Chen
Keyword(s):  
Knowledge Management ◽  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Group Decision Support System ◽  
Face To Face ◽  
Knowledge Conversion ◽  
Group Problem Solving ◽  
The People ◽  
Mixed Factorial Design ◽  
Better Than

Knowledge management (KM) is a complicated process that involves socialization, externalization, combination, and internalization and requires close collaboration among the people involved. Although Nonaka proposed the SECI (Socialization, Externalization, Combination, Internalization) model and the concept of Ba, which provides a process-oriented view of knowledge creation and transfer, practicing it is rather ad hoc. COVID-19 has provided a chance for practitioners to find a new method for KM. In this study, we adapted a group problem-solving system called TeamSpirit and structured it as a Ba for the SECI model. We then compared TeamSpirit with two other implementations of Ba, email and face-to-face communication, to evaluate their effects on knowledge externalization, knowledge combination, and knowledge internalization. Then, we evaluated whether these knowledge-conversion processes could improve knowledge acquisition and intention to share knowledge. A 3 × 2 mixed factorial design experiment was conducted. The results show that (a) TeamSpirit was better than the others, and face-to-face was better than email for each of the three knowledge conversion processes (externalization, combination, and internalization) and (b) the better the team’s knowledge conversion process lead, the stronger its knowledge acquisition and knowledge-sharing intention.

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