In these days our daily life is more and more affected by computers, chips, electronic equipment, et cetera. Owing to this increase of technology, even in common everyday objects, like for example a fridge, it is necessary to find a simple and intuitive way to interact with complex technology. Natural language dialog systems could be the solution, or at least a part of it, how humans and machines could communicate or interact with each other. With the help of dialog systems people could access information and technical functionality of computers in a natural way using linguistic in- and output. One of the main tasks of such dialog systems is to provide fast and appropriate answers to user’s questions or requests. The challenge, therein, is how do we find these answers out of the flood of information.
Table of Contents
1 Introduction
1.1 Objective and Research Questions
1.1.1 Project Goal
1.2 Structure of this Work
2 Linguistics Background
2.1 Human Computer Interaction
2.2 Natural Language Processing
2.3 Dialog and Discourse
2.3.1 Dialog as Discourse
2.3.2 Dialog as a Teleological Activity
2.3.3 Dialog as a Collaborative/Cooperative Activity
2.3.4 Speech Utterances in a Dialog
2.4 Dialog Systems
2.4.1 Evolution
2.4.2 Field of Application
2.4.3 Architecture and Components
2.4.4 Dialog Management and Important Strategies
2.4.5 Design Criteria
2.5 Metadata and Knowledge Representation
3 Semantic Web, SmartWeb, and Ontologies
3.1 Semantic Web
3.2 SmartWeb
3.3 Semantic Similarity Measures
3.4 Ontology
3.4.1 General
3.4.2 Ontologies as a Base of Modeling
3.4.3 Ontologies for Communication
3.4.4 Ontologies as Conceptual Models
3.4.5 Important Aspects/Advantages
3.4.6 RDF and RDFS
3.4.7 Ontology-Languages
3.4.8 Challenges
4 State of the Art of Answer Selection
4.1 Question Answering
4.2 Answer Selection
5 Underlying Algorithms and Corpora
5.1 OntoScore
5.1.1 OntoScore Algorithm
5.2 AnswerScore
5.3 Agents and WrapperAgents
5.4 Jena
5.4.1 Jena API
5.5 Used Ontology Technology
5.5.1 EMMA
5.5.2 DOLCE
5.5.3 SUMO
5.5.4 Sport Event
5.5.5 SWIntO
6 Evaluating Semantic Relatedness for Answer Selection
6.1 OntoScore Study
6.1.1 OntoScore Analysis Part1
6.1.2 Evaluation Score
6.1.3 OntoScore Analysis Part 2
6.2 Conclusion
7 Semantic Density Approach
7.1 ProperScore
7.1.1 General Idea
7.1.2 ProperScore Algorithm
7.2 Implementation
7.3 Results
8 Conclusion
8.1 Conclusion
A Appendix A
A.1 First Data Set
A.1.1 Question Number One
A.1.2 Answer Number One
A.2 Second Data Set
A.2.1 Question Number One
A.2.2 Answer Number One
B Appendix B
B.1 Protégé Screenshots
B.1.1 Sport Event
B.1.2 SWIntO
C Appendix C
C.1 Table of Contents of the CD-ROM
Research Objective and Scope
This thesis focuses on developing, implementing, and evaluating an ontology-based answer selection algorithm for dialog systems. The primary research goal is to improve the 'semantic coherence' calculation and ranking of semantically represented queries and answers to increase user satisfaction and system reliability.
- Ontology-based semantic coherence and relatedness analysis
- Ranking of candidate answers using semantic density approaches
- Evaluation of existing algorithms (e.g., OntoScore) vs. new implementations (ProperScore)
- Application within the SmartWeb project using specific domain ontologies
- Implementation of slot-filler methods for enhanced answer selection precision
Excerpt from the Book
3.4.1 General
In the 17th century, approximately 1613, scientists are engaged in “ontology” as a theory or study of beings, respectively in answering the question, what actually exists and what does not. Thereby, one differentiated formal and material ontologies, whereas formal ontologies concentrate on describing structures and physical laws and material ontologies concern theirselves with categorization of the contents of being.
With Immanuel Kant’s “Critique of pure reason” [Lausser [32]] approaches of ontologies were regarded as critical and they were avoided for any length of time, in accordance to Lausser [32]. Only by the later dissociation of the branch of metaphysics ontologies revived interest in research again. Until now the term ontology is defined as “study of being, determining of order, terms and existence of being” (referring to Duden [48]). Thereby ontology explains the conception of the world and epistemology the conception of our experience of the world.
With language and script human have a sophisticated communication medium that enables a description of the real world. As an explicit specification of this world of terms ontologies in computer science are used more like the generally thought of material ontology originated in philosophy and partially the concepts are heavily changed.
In order to answer the question, what an ontology is adequately, it is necessary to look at its field of applications. In general, however, ontologies are explicit conceptual formalizations of an application area. They suit best the purpose of knowledge representation of interpersonal communication, but above all of computer-based knowledge processing.
Summary of Chapters
Introduction: Provides the foundation and motivation for ontology-based answer selection in dialog systems, outlining the core research questions.
Linguistics Background: Establishes essential concepts regarding human-computer interaction, natural language processing, and the nuances of dialog and discourse.
Semantic Web, SmartWeb, and Ontologies: Explores the technical underpinnings, including the Semantic Web vision, RDF/RDFS structures, and the role of ontologies in modeling knowledge.
State of the Art of Answer Selection: Reviews existing techniques for question answering, highlighting the transition from document retrieval to candidate selection.
Underlying Algorithms and Corpora: Details the algorithmic base (OntoScore, AnswerScore), tools (Jena), and technologies (EMMA, DOLCE, SUMO, SWIntO) utilized in the SmartWeb project.
Evaluating Semantic Relatedness for Answer Selection: Presents an evaluation study of the OntoScore algorithm, identifying limitations in its effectiveness for selecting answers.
Semantic Density Approach: Introduces the new 'ProperScore' algorithm, which incorporates property slot-filling and instance-based scoring to improve answer candidate ranking.
Conclusion: Summarizes the findings and evaluates the performance of the proposed methods compared to baseline implementations.
Keywords
Answer Selection, Dialog Systems, Semantic Web, Ontologies, OntoScore, ProperScore, Semantic Coherence, Semantic Relatedness, SmartWeb, Knowledge Representation, RDF, Wrapper Agents, Natural Language Processing, Question Answering, Slot-Filling
Frequently Asked Questions
What is the primary objective of this thesis?
The main goal is to develop and implement an ontology-based answer selection algorithm for dialog systems that improves the ranking of semantically represented queries and answers.
What are the central thematic fields covered?
The work bridges general linguistics, natural language processing, the Semantic Web initiative, and the practical application of ontologies in dialog system architectures.
How is the effectiveness of answer selection measured?
The work utilizes various algorithmic approaches to calculate semantic relatedness, specifically evaluating the precision of selecting correct answer candidates from a candidate pool.
Which specific scientific methods are employed?
The research relies on path-based semantic coherence measures (OntoScore), RDF-based data manipulation (Jena framework), and a newly developed 'semantic density' approach (ProperScore).
What does the main part of the book address?
The main body focuses on the analysis and evaluation of the OntoScore algorithm and the subsequent development and testing of the ProperScore approach using domain-specific data from the SmartWeb project.
Which keywords define this work?
Key terms include Answer Selection, Semantic Web, Ontology, ProperScore, and Semantic Relatedness.
How does the ProperScore algorithm differ from OntoScore?
Unlike OntoScore, which relies on semantic distance between concepts, ProperScore utilizes property slot-fillers and instance-checking to ensure that specific factual requirements (like literal values) are matched correctly.
Why are wrapper agents important in the context of this study?
Wrapper agents are essential for extracting semantically formatted information from diverse web sources, providing the data necessary for the answer candidate selection process.
- Arbeit zitieren
- Bachelor of Arts / Magister Artium Christian Pretzsch (Autor:in), 2006, Ontology-Based Answer Selection in Dialog Systems, München, GRIN Verlag, https://www.grin.com/document/63949
