The idea of the Semantic Web is to allow internet content to be machine understandable and usable. Also, new knowledge should be inferable from already existing content. To achieve this, a machine must be proficient of merging information from different sources. This is no easy task as the Semantic Web will be just as heterogeneous as the web is today. OWL is a recommendation for a common language that will be used to encode knowledge on the Semantic Web in the form of ontologies. This paper will introduce OWL in general and describe some features that can be used to support merging of OWL files. Later, the process of merging OWL files and its three sub-processes mapping, aligning and combining will be described in detail. Also, some potential problems, which arise in merging, will be discussed. Later follows a quick evaluation of MoA and Prompt, two already available OWL merging tools.
Table of Contents
1 Introduction
2 Concept of the Semantic Web
2.1 Basic Layers – Unicode, URI, XML, RDF
2.2 Ontologies
2.3 Logic, Proof, Trust
3 Terminology
4 OWL
4.1 Basic Elements
4.1.1 Namespaces
4.1.2 Classes / Individuals
4.1.3 Properties
4.2 Import and Mapping Mechanisms
4.2.1 Import
4.2.2 Mapping Classes, Properties and Individuals
5 Merging OWL Ontologies
5.1 Mapping
5.1.1 Mismatches
5.1.2 Mapping Techniques
5.2 Aligning
5.3 Combining
6 Merging Tools: Two Examples
6.1 Testing Environment
6.2 MoA
6.2.1 Algorithm
6.2.2 Test Results
6.3 Prompt
6.3.1 Algorithm
6.3.2 Test Results
6.4 Comparison
7 Conclusion
Objectives and Topics
This paper examines the fundamental challenges of merging ontologies within the Semantic Web to enable machine-understandable information exchange. The primary research goal is to evaluate existing methodologies for mapping, aligning, and combining ontologies, specifically focusing on the practical application of the Web Ontology Language (OWL) and the performance of available merging tools.
- The theoretical foundation of the Semantic Web and its layer architecture.
- Core concepts and syntax of the Web Ontology Language (OWL).
- Detailed breakdown of the ontology merging process: Mapping, Aligning, and Combining.
- Analysis of different types of mismatches, including conceptual and terminological issues.
- Comparative performance evaluation of the merging tools MoA and Prompt.
Excerpt from the Book
5.1.1 Mismatches
Mismatches are some of the major problems that complicate the joint use of independently developed ontologies [22]. They arise when concepts in ontologies overlap, but are not used or encoded in clearly the same way. Mismatches can be divided into three different categories: Language level, conceptualization and terminological mismatches.
5.1.1.1 Language Level Mismatches
Language level mismatches occur when ontologies need to be combined that were written in two different ontology languages [23]. They arise by the use of diverse syntax and expressivity features in different languages.
It can be expected that these mismatches will play a less important role in the future than in the past, because after the release of OWL, most ontologies will be developed in this language. Consequently, this paper will not discuss language level mismatches in greater detail.
Chapter Summary
1 Introduction: Provides an overview of the growth of the internet and the necessity of the Semantic Web and ontology merging for machine intelligence.
2 Concept of the Semantic Web: Outlines the layer model of the Semantic Web, emphasizing the role of standards like RDF and the necessity of ontologies.
3 Terminology: Clarifies essential definitions for ontology merging, distinguishing between mapping, aligning, and combining.
4 OWL: Introduces the Web Ontology Language as the standard for encoding knowledge, covering basic syntax and mapping mechanisms.
5 Merging OWL Ontologies: Explains the three sub-processes of merging and discusses the various types of mismatches that hinder automated systems.
6 Merging Tools: Two Examples: Presents a practical test and comparison of the MoA and Prompt tools regarding their effectiveness in merging OWL ontologies.
7 Conclusion: Summarizes the findings and discusses the future requirements for more robust and usable ontology merging tools.
Keywords
Semantic Web, Ontology Merging, OWL, Ontology Unification, Mapping, Aligning, Combining, Mismatches, MoA, Prompt, Knowledge Representation, Protégé, RDF, Interoperability, Reasoning.
Frequently Asked Questions
What is the central focus of this paper?
The paper focuses on the technical challenges and methodologies associated with merging ontologies on the Semantic Web, specifically within the context of the OWL standard.
What are the primary thematic areas covered?
The main themes include the architecture of the Semantic Web, the syntax and features of OWL, the categorization of ontology mismatches, and the evaluation of specific merging tools.
What is the primary goal of the research?
The goal is to explore how different knowledge bases can be combined to allow machines to infer new information, and to assess the maturity of current tools in handling this task.
Which scientific methods are employed?
The author uses a descriptive analysis of existing theoretical frameworks combined with a comparative, experimental performance test of two software tools, MoA and Prompt, using a common test environment.
What is addressed in the main body of the work?
The main body details the technical language features of OWL, categorizes the types of mismatches (language, conceptual, and terminological), and conducts a comparative analysis of merging software.
Which keywords characterize the work?
Key terms include Semantic Web, Ontology Merging, OWL, Mapping, Aligning, Combining, and Knowledge Representation.
Why are mismatches considered a significant challenge for ontology merging?
Mismatches complicate merging because they arise when concepts represent similar real-world ideas but are modeled with different syntax, scope, or terminology, preventing machines from automatically recognizing their equivalence.
How does the performance of MoA compare to Prompt based on the tests?
MoA performs well in automated mapping due to its linguistic approach, but lacks a graphical interface, whereas Prompt provides an intuitive user interface and flexible manual interaction but is less effective at automatic semantic mapping.
- Quote paper
- Malte Poppensieker (Author), 2004, Ontology Unification/Merging, Munich, GRIN Verlag, https://www.grin.com/document/29389
