Most of today’s information systems are highly heterogeneous and complex. High efforts and costs are put into interlinking systems to let systems communicate to each other and thus overcoming heterogeneity. The semantic web plays a significant role in the way it covers and links knowledge, making the web’s content understandable for machine-to-machine interactions. Hereby, ontologies serve as a technology to cover, infer and verify knowledge and making it available to accomplish a common understanding among participating agents.
This paper describes how ontologies are used in practice to support the overcoming of heterogeneity in information systems. After a revision of basic semantic technologies and standards like OWL and SPARQL we discuss a variety of methods and tools of the semantic web. In more detail, we investigate ontology editors, especially the Protégé tool as a well-established open-source application to create, edit and share ontologies. At last, we discover a variety of practical applications where ontologies are of high use.
Table of Contents
1. Introduction
2. Revision of Semantic Concepts
2.1 Knowledge and Semantic Web
2.1.1 DIKW Pyramid
2.1.3 Description Logics
2.2 Ontologies, XML and RDF(S)
2.2.1 Ontologies
2.2.2 XML
2.2.3 RDF(S)
2.3 OWL and SPARQL
2.3.1 OWL
2.3.2 SPARQL
3. Ontology-based Information Integration
3.1 Methods
3.1.1 Mappings
3.1.2 Ontology Integration Architectures
3.1.3 Reasoning, Inferring, Expert Systems
3.2 Tools
3.2.1 Semantic Web Tools
3.2.2 Ontology Editors
4. Ontology Editor - Protégé
4.1 General Background
4.2 Historical Background
4.3 Features
4.4 Internals
4.5 Building Ontologies
4.6 Critical Appraisal
5. Practical Applications
5.1 Industry Solutions
5.2 Established Ontologies
5.3 Biomedical Science and Protégé
6. Conclusion
Objectives and Topics
The primary objective of this report is to analyze the role of ontologies in addressing the challenges of information heterogeneity in modern complex systems. The research investigates how semantic technologies enable machine-to-machine communication and facilitate knowledge integration, with a specific focus on the Protégé ontology editor as a practical tool for development and visualization.
- Theoretical foundation of semantic concepts and the Semantic Web stack.
- Methods for ontology-based information integration and mapping architectures.
- Comprehensive analysis of the Protégé ontology editor, including its history, features, and internals.
- Application of ontologies in industrial and biomedical domains.
Excerpt from the Book
4.1 General Background
Protégé is an open source ontology editor developed by the Stanford Medical Informatics group. On its website [23] it is described as the following: “Protégé is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies.” Originally, Protégé was developed to create knowledge databases for medical research. It is written in Java. Many plugins are available (e.g. visualization, inference, import/export) to expand the tool’s capabilities. A Java API is available for developers. As an ontology editor Protégé concentrates on the editing, validation and visualization of ontologies. Moreover, it is a development environment with many connectors e.g. to the Jena framework. Protégé supports OWL DL and some parts of OWL Full.
Originally, there are two ways to model knowledge in Protégé: with Protégé-frames and Protégé-OWL. Protégé-frames are hierarchical structures of concepts, slots and attributes whereas Protégé-OWL is conceptually based on OWL and thus includes description logics for inference mechanisms and to imply knowledge. Some concepts are necessary to understand how Protégé-frames and Protégé-OWL handle semantics differently: The unique name assumption and the closed world assumption fulfilled only by Protégé-frames and the open world assumption fulfilled by Protégé-OWL. The unique name assumption assumes that different names necessarily correspond to different things unless stated otherwise. On the other hand the closed world assumption refers to the fact that a statement is always wrong when it does not exist in the knowledge base. This proves the fact that Protégé-frames are not capable to infer interrelationships between terms in the knowledge base, whereas Protégé-OWL is always able to infer e.g. equivalent objects by itself. For Protégé-OWL the open world assumption means that everything can be integrated unless no constraints are violated [52].
Summary of Chapters
1. Introduction: Introduces the challenge of information heterogeneity and presents ontologies as a solution for machine-understandable knowledge representation.
2. Revision of Semantic Concepts: Reviews fundamental technologies including the DIKW pyramid, Description Logics, RDF, OWL, and SPARQL as the basis for modern ontologies.
3. Ontology-based Information Integration: Discusses methods and architectural approaches, such as mappings and reasoning, used to overcome data silos.
4. Ontology Editor - Protégé: Provides a detailed examination of the Protégé tool, covering its history, architecture, features, and a critical appraisal of its usability.
5. Practical Applications: Explores real-world use cases in industry and science, highlighting how ontologies and Protégé are utilized in fields like biomedicine.
6. Conclusion: Summarizes the report's findings on the efficacy of ontologies for creating reusable and consistent knowledge in diverse application fields.
Keywords
Ontology Editors, Protégé, Semantic Web Technologies, Heterogeneity, Information Integration, OWL, SPARQL, Knowledge Management, Reasoning, Data Modeling, RDF, Inference, Semantic Meaning, Web Ontology Language, Collaborative Ontology Engineering.
Frequently Asked Questions
What is the core problem addressed in this report?
The report focuses on the challenges of high costs and efforts required to integrate complex, distributed, and heterogeneous information systems to create meaningful, shared knowledge.
What are the primary themes discussed?
The main themes include semantic technologies, the architecture of the Semantic Web, the methodology of ontology-based information integration, and the practical application of ontology editors.
What is the main objective of the research?
The objective is to explain how ontologies facilitate the creation of machine-understandable content and how tools like Protégé support this process in professional and scientific environments.
Which scientific methods are analyzed?
The report examines methods like Global-as-View (GAV) and Local-as-View (LAV) mappings, various ontology integration architectures, and the role of logic-based reasoning engines.
What is covered in the main section of the document?
The main section investigates the Protégé ontology editor, detailing its history, internal software architecture, features like natural language processing, and its role in collaborative environments.
Which keywords best characterize the report?
The report is defined by terms such as Ontology Editors, Protégé, Heterogeneity, Information Integration, and Semantic Web Technologies.
How does Protégé differ from early knowledge management tools?
Unlike early versions that were limited to specific fields like medicine and lacked semantic handling, modern Protégé supports OWL, description logics, and collaborative editing.
What are the identified critical perspectives on Protégé?
The author notes that while Protégé is flexible and powerful, it remains complex, requires highly skilled engineers, can be buggy during manual creation, and may lead to user confusion due to an abundance of plugins.
Why is Protégé particularly relevant in biomedical science?
Protégé was originally developed by the Stanford Medical Informatics group and continues to be used for complex modeling and integration of biomedical knowledge bases.
- Quote paper
- Kevin Rudolph (Author), 2015, The Use of Ontologies in Practice, Munich, GRIN Verlag, https://www.grin.com/document/300018
