Using Ontology and Rule-Based Reasoning for Conceptual Data Models Synonyms Detection

2019 ◽  
Vol 30 (1) ◽  
pp. 1-21
Author(s):  
Ljubica Kazi ◽  
Zoltan Kazi
Keyword(s):  
Data Model ◽  
System Development ◽  
Data Models ◽  
University Student ◽  
Specific Data ◽  
Entity Relationship Model ◽  
Relationship Model ◽  
Er Model ◽  
Entity Relationship ◽  
Conceptual Data

Conceptual data models can change during the information system development and teamwork phases, which require constantly monitoring with synonyms detection. This study elaborates on an approach for detecting synonyms in an entity-relationship model based on mapping with ontological elements. The use of a specific data model validator (DMV) tool enables formalization of the ontology and ER models, as well as their integration with the set of reasoning rules. The reasoning rules enable mapping between formalized elements of the ontology and ER model, and the extraction of synonyms. Formalized elements and reasoning rules are processed within Prolog for the extraction of synonyms. An empirical study conducted by using university student exams demonstrates usability of the proposed approach. The results show effectiveness in extraction of synonyms in all types of conceptual data model elements.

scholarly journals An Exploration of Individual Differences in Synthesizing Entity-Relationship Data Models

10.28945/2616 ◽  
2003 ◽  
Author(s):  
Vijay V. Raghavan
Keyword(s):  
Individual Differences ◽  
Learning Styles ◽  
Data Model ◽  
National Origin ◽  
Popular Approach ◽  
Er Model ◽  
Written Descriptions ◽  
Entity Relationship ◽  
The Individual ◽  
Conceptual Data

Entity Relationship (ER) modeling (1976) is a popular approach to formulate a conceptual data model for designing properly structured databases. In spite of some criticisms of the model leading to numerous extensions added to the original ER model, it is generally believed to be a method of choice for designing common databases. Not surprisingly, ER Modeling is inextricably a part of all database-design classes. Teachers of this modeling construct often encounter students experiencing problems in synthesizing ER models from verbal or written descriptions. This study explores whether individual differences contribute to such difficulties. Gender, length of Information Technology (IT) experience, length of database experience, length of business experience, national origin and learning styles are hypothesized as the individual diffe r-ences that might contribute to a student’s ability to synthesize a conceptual ER model. Ability to synthesize ER models was evaluated using a textbook type ER modeling problem.

scholarly journals Mapping Common Errors in Entity Relationship Diagram Design of Novice Designers

2021 ◽  
Vol 13 (1) ◽  
pp. 1-19
Author(s):  
Rami Rashkovits ◽  
Ilana Lavy
Keyword(s):  
Data Model ◽  
System Development ◽  
Data Modeling ◽  
Database Schema ◽  
Solo Taxonomy ◽  
Key Factor ◽  
Novice Designers ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Conceptual Data

Data modeling in the context of database design is a challenging task for any database designer, even more so for novice designers. A proper database schema is a key factor for the success of any information systems, hence conceptual data modeling that yields the database schema is an essential process of the system development. However, novice designers encounter difficulties in understanding and implementing such models. This study aims to identify the difficulties in understanding and implementing data models and explore the origins of these difficulties. This research examines the data model produced by students and maps the errors done by the students. The errors were classified using the SOLO taxonomy. The study also sheds light on the underlying reasons for the errors done during the design of the data model based on interviews conducted with a representative group of the study participants. We also suggest ways to improve novice designer's performances more effectively, so they can draw more accurate models and make use of advanced design constituents such as entity hierarchies, ternary relationships, aggregated entities, and alike. The research findings might enrich the data body research on data model design from the students' perspectives.

Engineering Conceptual Data Models from Domain Ontologies

2008 ◽  
pp. 1068-1080
Author(s):  
Haya El-Ghalayini ◽  
Mohammed Odeh ◽  
Richard McClatchey
Keyword(s):  
Data Model ◽  
Domain Knowledge ◽  
System Development ◽  
Data Models ◽  
Mapping Algorithm ◽  
Domain Ontologies ◽  
Conceptual Data ◽  
High Degree ◽  
Quantitative And Qualitative Methods

This article studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of developing information systems. A mapping algorithm has been proposed and embedded in a special purpose transformation engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this article focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities, along with their relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development.

scholarly journals TRANSFORMING EXTENDED ENTITY-RELATIONSHIP MODEL INTO OWL ONTOLOGY IN TEMPORAL DATABASES

2018 ◽  
Vol 34 (1) ◽  
pp. 77-96
Author(s):  
Minh Hoang Lien Vo ◽  
Quang Hoang
Keyword(s):  
Temporal Databases ◽  
Semantic Classification ◽  
Entity Relationship Model ◽  
Relationship Model ◽  
Er Model ◽  
Owl Ontology ◽  
Entity Relationship

Many previous systems were built based on the ER model, so the upgrading and transforming the ER model into ontology for reducing cost is really necessary. There are several studies aim at transforming from ER and EER model into ontology. However, these studies have not classified the semantic of the recursive relationship in ER model, so the semantic of the recursive relationship will be easily lost during the transformation. Also, the studies have not mentioned the designing of the ontology that supports temporal attributes based on the temporal ER model. This paper discusses the semantic classification of the recursive relationship and TimeER model (extended EER in temporal databases) and OWL ontology. And propose a method to transform into OWL ontology.

Engineering Conceptual Data Models from Domain Ontologies

2009 ◽  
pp. 304-316
Author(s):  
Haya El-Ghalayini ◽  
Mohammed Odeh ◽  
Richard McClatchey
Keyword(s):  
Data Model ◽  
Domain Knowledge ◽  
System Development ◽  
Data Models ◽  
Mapping Algorithm ◽  
Domain Ontologies ◽  
Conceptual Data ◽  
High Degree ◽  
Quantitative And Qualitative Methods

This paper studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed and embedded in a special purpose Transformation Engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this paper focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities along with their relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development.

Ternary Relationships

2002 ◽  
pp. 17-33 ◽  
Author(s):  
Trevor H. Jones ◽  
Il-Yeol Song
Keyword(s):  
Database Management System ◽  
Theoretical Development ◽  
Binary Model ◽  
Entity Relationship Model ◽  
Relationship Model ◽  
Competing Models ◽  
Central Argument ◽  
Entity Relationship ◽  
Conceptual Data ◽  
Relational Database Management

Conceptual data modeling is a backbone of most major systems development projects. Within this arena, one of the most widely used techniques is the entity-relationship (ER) or extended entity-relationship model (EER, henceforth also referred to as ER), introduced by Chen (1976). However, there are multiple competing models and notations, each having distinct strengths and weaknesses. Many of the model definitions and underpinnings continue to undergo practical and theoretical development. The abilities of each of the model structures and notations to fully represent the semantics of any given situation are constantly compared, with many issues open to argument. More specifically, certain arguments revolve around the inclusion of binary or N-ary representation of relationships in ER models. A central argument stems from the superior ability of N-ary modeling to reflect the true semantics of any given situation, whereas a binary model provides the simplest constructs for expressing information systems’ logical design and is equivalently represented in a relational database management system (DBMS) (McKee & Rodgers, 1992).

scholarly journals Diseño de un sitio web dinámico para institución educativa

2019 ◽  
pp. 20-25
Author(s):  
Laura Cristina Vázquez-De Los Santos ◽  
Griselda Cortes-Morales ◽  
Alicia Guadalupe Valdez-Menchaca ◽  
Diego Arnulfo Martínez-Perales
Keyword(s):  
Systems Engineering ◽  
Data Model ◽  
System Development ◽  
Educational Institution ◽  
Database Management System ◽  
Relational Model ◽  
Database Models ◽  
Relationship Model ◽  
System Part ◽  
Entity Relationship

The objective of this article is to design a website for an educational institution with a dynamic data model that allows you to easily add, edit and update information. In the methodology, systems engineering concepts will be used during the system development process, documenting each stage. Carrying out the stages of requirements analysis and data model design, considering the parties involved. The Entity Relationship model was designed with the purpose of confirming the logical needs of the information. In addition, the relational model was created, where the attributes of each entity are detailed. MySQL was used as the database management system. Part of the design of the data model includes the way in which it interacts with it, for this the CRUD system is used. With the design of the data models: logical and database models, the script for the creation of the dynamic database was created, which will be used to store all the information relevant to the educational institution. As a result, the correct functionality of the database was guaranteed on the website.

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