Fig 5 - uploaded by Sungchun No
Content may be subject to copyright.
![The Relational Database Schema Diagram [13]](profile/Sungchun-No/publication/286577645/figure/fig1/AS:1127225080463360@1645762810189/The-Relational-Database-Schema-Diagram-13.png)
The Relational Database Schema Diagram [13]
Source publication
![Fig. 5. The Relational Database Schema Diagram [13]](profile/Sungchun-No/publication/286577645/figure/fig1/AS:1127225080463360@1645762810189/The-Relational-Database-Schema-Diagram-13_Q320.jpg)
Recently, there has been a growing need for research to manage the knowledge of an organization effectively using ontology. To increase the effect of knowledge management, the development of a well-defined ontology using various concepts about the knowledge of an organization is needed. There are two approaches in the current methodology for ontolo...
Contexts in source publication
Context 1
... example company in our scenario is drawn up based on the well-known COMPANY database schema in the Elmasri/ Navathe Book [13]. Fig 5 shows the relational database schema that would be extracted into the kernel ontology. ...
Similar publications
In the recent years, a growing popularity of knowledge management and knowledge conceptualization in the form of ontology is observed. However, one of the most important and up-to-date challenges infolds the quality evaluation of knowledge conceptualization. The prospective mistakes and omissions in ontologies have an influence on the exploitation...
In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the...
In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the...
An ontology is a model language that supports the functions to integrate conceptually distributed domain knowledge and infer relationships among the concepts. Ontologies are developed based on the target domain knowledge. As a result, methodologies to automatically generate an ontology from metadata that characterize the domain knowledge are becomi...
Citations
... Ontology was created to share a common understanding of the structure of information among people, to enable the reuse of domain knowledge, to make domain assumptions explicit, to separate domain knowledge from operational knowledge, and to analyze the domain knowledge [21]. Developing ontology from scratch is hard and wastes time, however, the study in [22] state that using an existing ontology can save money and effort. The effort of this study, which adapts the pre-established ontology developed by [6], is motivated by the idea of reusing pre-made ontologies. ...
... C2 is an area where ontology is actively used for the SA. Most studies attempting to utilize ontology for representing the knowledge of C2 systems have proposed a three-layered structure comprising an upper layer, middle layer, and domain [14][15][16][17]. Smith [16,17] proposed a three-layered structure comprising the universal core semantic layer (UCore SL), C2 core ontology, and domain ontologies. ...
... In this model, the C2 core ontology was proposed as a middle layer ontology dealing with only general terms that can be used by a broad stakeholder base within the C2 domain. Ra [15] has proposed a method combining the characteristics of top-down and bottom-up approaches by utilizing the kernel ontology as the middle layer ontology. Kabilan [14] proposed a framework that used SUMO as its upper ontology. ...
... The burden on the ontology interpretation occurs with the manual weight imposing [20,31,32,33,34] Traversal-based Select initial class manually Extract all classes, properties, and instances connected with the initial class Semantic relations are not sufficiently reflected with direct connection Oversized ontology module is extracted with indirect relations Requires manual initial class selection process [15,35,36,37] implied in heterogeneous ontologies because they utilized prior knowledge to find them. Furthermore, most relationships are limited to synthetic interoperability. ...
Data and information produced in network-centric environments are large and heterogeneous. As a solution to this challenge, ontology-based situation awareness (SA) is gaining attention because ontologies can contribute to the integration of heterogeneous data and information produced from different sources and can enhance knowledge formalization. In this study, we propose a novel method for enhancing ontology-based SA by integrating ontology and linked open data (LOD) called a multi-layered SA ontology and the relations between events in the layer. In addition, we described the characteristics and roles of each layer. Finally, we developed a framework to perform SA rapidly and accurately by acquiring and integrating information from the ontology and LOD based on the multi-layered SA ontology. We conducted three experiments to verify the effectiveness of the proposed framework. The results show that the performance of the SA of our framework is comparable to that of domain experts.
... There are several approaches, such as the bottom-up technique [6], top-down technique [7], and the hybrid method [8], to design a decision-based module based on situation reporting using an analyzed set of concepts. A few researchers suggested Mixed Building Methodology (MOBM) based architecture on combining characteristics of both approaches mentioned above to design reporting model [9]. ...
Computer Vision (CV) is a technology that
allows a computer to see. Multiple techniques have been used
to process the images and extract patterns from the input frames. Decision-based systems are developed by using various
CV techniques to report the situation in the real environment.
The proposed study presents object understanding based on an
object’s size, distance, and speed. Firstly the live video stream
is passed to the Convolutional Neural Network (CNN) model.
CNN is a deep learning-based model specially used for image
processing because of its high accuracy and feature extraction
module. The proposed model is based on two steps. In the first step, CNN is trained to detect different objects from images and return a bounding box for each image. Secondly, the size, speed, and distance of the object are calculated.
... Therefore, this paper has suggested the construction of a military ontology as the first step in implementing intelligent ATCIS. The mixed ontology building methodology (MOBM) [2] was applied for ontology construction, and the kernel ontology was defined based on terms extracted from the ATCIS database. Then the bottom-up approach and the top-down approach were applied to extend the kernel ontology, and finally the military ontology was completed. ...
... This work takes three directions: (1) First, extract the entity-relationship (ER) model from the database schema using reengineering, then from that model extract the ontology [8]; (2) given the database schema and ontology, for semantic web applications, extract the mapping rules between them [9]; and (3) generate the ontology structure itself from the relational database schema [10]. The MOBM, a mixed methodology, was proposed based on these works [2]. The MOBM first generates a kernel ontology, which becomes the core, using database information as much as possible and then completes the ontology by applying the bottom-up method and the top-down method to build additional parts of the ontology. ...
... The heart of the MOBM is the utilization of database schema to construct a practical ontology . First, the kernel ontology was built following the mapping rules [2] of the MOBM after extracting the core terms from the database schema. Because the scope of the ATCIS database schema was overly broad, the kernel ontology was created distinguishing the information, operation , and firepower domains. ...
The purpose of this research is to construct a military ontology as the core element for implementing the intelligent Army Tactical Command Information System (ATCIS). Using the military ontology, the system can automatically understand and manage the meaning of military information in the system, and hence it can provide a commander with military knowledge for decision making. To construct the military ontology, we define the core concepts of the ontology based on terms extracted from the ATCIS database and complete the ontology by using the mixed ontology building methodology (MOBM). In addition, we implement intelligent ATCIS as a prototype that provides a military concept navigation service and commanders' decision support service to demonstrate how to use the military ontology in practice.
... 첫째, 온톨로지를 국방분야에 활용한 문헌들을 조사하였다. 둘째, ATCIS에서 사용될 국방 용어들을 ATCIS 데이터베이 스 정보와 전자 교범 등을 분석하여 수집하였고, 혼합형 온톨 로지 구축 방법론[4] ...
ATCIS (Army Tactical Command Information System) provides commanders and staff officers the battlefield information that is reported by tactical echelons under an army corps and the commanders make decisions based on the information by using their experience and specialty in military domain. If ATICS can automatically understand the reported information from rapidly changing battlefield and provide new knowledge that can support decision making, the commanders would be able to make faster and more accurate decision. In this paper, therefore, we propose an intelligent ATCIS using a national defense ontology. To this end, we built the national defense ontology by analyzing the electronic field manuals and ATCIS database, and then we defined military knowledge for decision making as a form of rule by interviewing several staff officers from different fields. In order to show how to apply the ontology and rules to decision making support for the commanders, we implemented a decision support service to estimate the possibility of enemy's provocation by using semantic web technologies.
... There are three modes of development [16], the top level which reflects the determination of general concepts and moving to the specific concepts. The bottom-up method deals with the subclasses identification and then grouping them into global classes, the middle-out method [17] defines most principal concepts and after that knowledge engineer decide to apply the top-down method or the bottom-up method[6] . ...
Ontology is generally defined as an explicit specification of conceptualization which involves the exploration of concepts and its relationships in the domain of interest. Ontology is used to share knowledge across semantic web services, agents and information systems. Therefore the ontology engineer should follow a methodology during the ontology construction to ensure its reliability. This paper presents ontology building methodologies such as Uschold and king [3], Viral Hepatitis Ontology design Methodology [4] and Amaya Berneras et al [9].Moreover the paper presents Rheumatoid and Osteoarthritis ontologies which belong to the medical domain and a proposal for a new methodology applied to build the two medical ontologies. What distinguishes the proposed methodology is the execution of knowledge representation which involves the use of conceptualization and inference rules.
Purpose
The purpose of this paper is to formulate apposite criteria for ontology evaluation and test them through assessments of existing ontologies.
Design/methodology/approach
A literature review provided the basis from which to extract the categories relevant to an evaluation of internal ontology components. According to the ontology evaluation categories, a panel of experts provided the evaluation criteria for each category via Delphi survey. Reliability was gauged by applying the criteria to assessments of existing smartphone ontologies.
Findings
Existing research tends to approach ontology evaluation through comparison with well-engineered ontologies, implementation in target applications and appropriateness/interconnection appraisals in relation to raw data, but such methodologies fall short of shedding light on the internal workings of ontologies, such as structure, semantic representation and interoperability. This study adopts its evaluation categories from previous research while also collecting concrete evaluation criteria from an expert panel and verifying the reliability of the resulting 53 criteria.
Originality/value
This is the first published study to extract ontology evaluation criteria in terms of syntax, semantics and pragmatics. The results can be used as an evaluation index following ontology construction.
Organizations today need to maintain relationships with actors who intervene in the execution of key processes. This creates problems for the correct administration of the organization’s procedures and in administrating a shared jargon, increasing costs related to the procedures. The objective of this article is to present the MONPRO methodology, which supports the management of organizational procedures through the creation of specific ontologies of the organizational domain (including employee jargon and natural language), allowing any person with knowledge in computer science to create ontologies, without needing to be an expert. MONPRO is composed of five phases. The activities in each of them define a series of forms and diagrams that lead the ontology developer to break down the procedure and obtain the necessary resources to build the ontology. To test the methodology, the “Presentation and Defense of the Doctoral Thesis at UC3M”procedure for the Carlos III University of Madrid (Léganis campus), Spain was used. The results can, in general, have a positive impact, reducing costs for a company through their no longer requiring to hire experts to develop procedure ontologies, also reducing by approximately 43.8.
According to the requirements and characteristics of the simulation tutoring system for weapon utilization, a knowledge base depending on the domain ontology was designed. The knowledge for weapon utilization, the knowledge representation, and the method of knowledge representation were introduced. Through a combination of the ontology technology and various existing models, the key points of using specific weapon could be mastered by processing the date, information, and experience from expert. The conceptual model of knowledge was built. A knowledge base for weapon utilization was designed and implemented. The structure and function of the simulation tutoring system and rule-based expert subsystem were introduced. It is proved that the designed knowledge base can be well applied to the system. ©, 2015, Xitong Fangzhen Xuebao/Journal of System Simulation. All right reserved.
