PIONEROS LOCALES

The overall plan regarding the application of the 4D-SETL framework is to load the

foundation ontology then to integrate each of the domain ontologies and their associated

instances (datasets). The load order and ontology source is provided in Table 3.6.

Iteration

Load Order Ontology Ontology Source

1 1 Foundation Ontology BORO UML Foundation Ontology Model

2 Temporal Ontology BORO UML Ontology Model

3 Temporal Ontology Instances (Calendar) Dataset programmatically generated 4 Geographic Ontology BORO UML Model (extracted from dataset) 5 Geographic Ontology Instances Postcode Dataset (2.5 M records)

2 6 Standard Industry Codes Ontology (Taxonomic Ranks) BORO UML Model 7 Standard Industry Codes Ontology (Taxonomic

Classes)

SIC 2007 Dataset (of types and types of types)

8 Companies House Ontology BORO UML Model (extracted from dataset) 9 Companies House Ontology Instances Companies House Dataset (3.5 million) 3 10 Company Officers Ontology BORO UML Model (extracted from dataset)

11 Company Officers Ontology Instances Directors Dataset (12.5 million records) Table 3.6: Ontology Load Order and Source

foundation ontology is common to all domain ontologies and the temporal and geographic

(geo-spatial) ontology entities are common to both the Company and Company Officers

datasets. Figure 4.2 depicts these relationships in diagrammatic form.

Figure 3.7: Dataset Relationship Overview

This ordering may benefit from further elaboration, therefore the following example is

provided. The semantic data is held within the warehouse system in graph form. Therefore to

add a domain ontology element requires that it relates to the elements of the foundation, for

example to assert that the new domain object is a Type, requires that the relationship Types

Instance be asserted between the new object to added to the warehouse and the foundation

Types instance (the Type of Types). The same is true of instance level ontology objects; they

must be integrated with the graph that forms the domain model. Therefore the load order Foundational Ontology SIC 2007 Ontology & Dataset 3 Companies House Ontology & Dataset 4 Geographic Dataset 2 Company Officers Ontology & Dataset 5

Calendar Ontology and Dataset 1

DESIGN SCIENCE FIRST ITERATION- THE

CHAPTER 4.

INITIAL DEVELOPMENT AND APPLICATION OF THE 4D-

SETL FRAMEWORK

Introduction

4.1

This chapter describes the execution of the first iteration of the research design-build-evaluate

cycle. The aim of this iteration is to explore the exploitation of a 4D foundational ontology

and a graph database to perform semantic data integration and to assess the effectiveness of

the approach. This aim is realised through a number of objectives, the primary of which is the

development of the 4D Semantic Extract Transform and Load (4D-SETL) framework which

employs a 4D foundational ontology and a graph database to perform the integration.

Following the design and development of 4D-SETL, the framework is applied to Extract,

Transform and Load (ETL) two experimental datasets. The two main artefacts resulting from

the first iteration are the 4D-SETL framework and a prototype data warehouse system

populated with the foundation and two domain ontologies that include a large number of

instance level elements. The effectiveness of the 4D-SETL, and thus the effectiveness of

exploiting foundational perdurantist ontology and graph database for semantic integration, is

evaluated via technical experiment and illustrative scenario.

This chapter is structured as follows. The aim and objectives are outlined in the next section

(4.2) which also provides details of the research design for the design science iteration. This

consists of a list of the design-build-evaluation related activities, the research artefacts

produced and the experimental datasets employed. Section 4.3 provides details and design

sections (4.4 and 4.5) describe the first and second application of 4D-SETL framework,

respectively; and how the 4D-SETL is employed to semantically integrate the two

experimental datasets. Section 4.6 provides an overview of the prototype system instantiation

and the tool-chain that is developed during the course of the design science iteration to

support the framework. Section 4.7 details the outcomes of the iteration through the

evaluation of 4D-SETL assesses the effectiveness of the use of 4D (perdurantist) ontology to

semantically integrate data. Finally section 4.8 discusses the learning and the deficiencies that

will be addressed in the subsequent research iteration.

Design Science Research - Iteration One

4.2

Figure 4.1: Research Implementation Cycle Iteration One

The activities within this iteration serve to explore the research problem and are planned and

executed in accordance with adopted design science research methodology. A diagrammatic This Iteration - Iteration 1 Design: 4D-SETL framework and supporting toolchain Build: 4D- SETLframework and apply to Datasets 1 & 2 Evaluation: Resultant artefacts Iteration 2 Improve Design: 4D-SETL framework and supporting toolchain Build: Incorporate improvements and apply 4D-SETL to Datasets 2 & 3 Evaluation: Resultant artefacts Iteration 3 Improve Design: 4D-SETL framework and supporting toolchain Build: Incorporate improvements and apply 4D-SETL to Dataset 5 Evaluation: Resultant artefacts Knowledge and Artefact quality Time

overview of the first iteration and how it relates to the subsequent iterations is provided in

Figure 4.1.

4.2.1 Aim and Objectives of Iteration One

The objectives of this first design science iteration are realised through activities related to

the design-build-evaluate of the 4D-SETL framework. These objectives serve the main aim

of the research which is to evaluate the effeteness of exploiting both a 4D ontology and a

graph database to facilitate semantic integration. The stage, activity reference and objectives

are listed in Table 4.1.

Stage Activity Objective Section

Reference

Design

1 Design the 4D-SETL framework. Document the ontological architecture including the foundation ontic categories and foundational patterns that will be employed within 4D- SETL

4.3

2 Design the tool chain to support the framework 4.4

Build

3 The First Application of 4D-SETL – Temporal Ontology 4.5 4 The Second Application of 4D-SETL – Geographic Ontology 4.6

Evaluation: 5

a) Technical experiment heuristic testing of the warehouse artefacts b) Illustrative scenario: describing the artefact qualities and advantages.

4.7

Table 4.1: Stages and Activities and Within this Iteration