Comparing information extraction between instance-based data models and relational data models

Dorani, Kiumars (2023) Comparing information extraction between instance-based data models and relational data models. Masters thesis, Memorial University of Newfoundland.

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Instance-based representation has been developed to overcome the limitations of class- based models for storing data. A class-based data model organizes data into pre- defined classes that represent specific entities within a domain. However, instance- based model introduces two separated layers for representing instance and classes, freeing instances from pre-defined, fixed schemas and enabling more dynamic and exible data representations. Despite the well-established theoretical foundations of instance-based representation, there is little empirical research that investigates its practical usefulness. In this study, we conduct an experiment to compare the effec- tiveness of information extraction between instance-based data models and class-based data models. Participants randomly received data represented using data structured according to one of the models and answered information extraction/retrieval questions. The results show that, depending on the type of information extraction task, one representation supported more effective retrieval than the other, suggesting that the models can be complementary. In complex use cases including extracting infor- mation about relationships of instance/entities and retrieving information involving instances from different classes, the instance-based model outperformed the class- based model. On the other hand, for simpler use cases involving extracting infor- mation about cardinalities of relationships and retrieving information involving only one entity (i.e., instances from a same class), the class-based model proved to be more effective. The findings both provide empirical evidence for the effectiveness and usefulness of the instance-based model and demonstrate how it can complement the class-based model in representing the domain.

Item Type: Thesis (Masters)
Item ID: 16089
Additional Information: Includes bibliographical references (pages 61-64)
Keywords: conceptual modeling, data modeling, instance-based data models, class-based data models
Department(s): Business Administration, Faculty of > Business Administration
Date: August 2023
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Information filtering systems; Data structures (Computer science); Data mining—Statistical methods; Computer simulation

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