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Introduction:

In today’s digital age, vast amounts of information are readily available online, making it increasingly difficult for users to find relevant and accurate information quickly. Traditional keyword-based search engines often return results that are not precisely what the user is looking for, leading to frustration and wasted time. To address this issue, semantic search has emerged as a promising approach that aims to understand the meaning behind words and phrases to deliver more accurate search results.

One essential component of semantic search is building a knowledge graph, a structured representation of knowledge that captures relationships between entities and concepts. By leveraging knowledge graphs, search engines can better understand the context of user queries and provide more relevant and precise search results. This thesis focuses on the development of a knowledge graph for semantic search, aiming to improve the effectiveness and efficiency of information retrieval on the web.

Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of semantic search
2.2 Knowledge graphs and their applications
2.3 Semantic web technologies
2.4 Natural language processing techniques
2.5 Semantic search algorithms
2.6 Evaluation metrics for semantic search systems
2.7 Challenges in building knowledge graphs
2.8 Related work in the field
2.9 Summary of key findings
2.10 Gaps in existing research

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 Entity extraction and linking
3.4 Relationship extraction
3.5 Knowledge graph construction
3.6 Semantic search algorithm design
3.7 Evaluation methodology
3.8 Performance metrics
3.9 Validation and testing
3.10 Summary of methodology

Chapter 4: System Implementation
4.1 Implementation framework
4.2 Data sources and integration
4.3 Entity recognition and disambiguation
4.4 Relationship extraction techniques
4.5 Knowledge graph construction
4.6 Semantic search implementation
4.7 System optimization and scalability
4.8 User interface design
4.9 Testing and validation results
4.10 Analysis of system performance

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for research and practice
5.4 Future research directions
5.5 Conclusion

Thesis Overview:

The exponential growth of data on the web has led to the need for more sophisticated search technologies that can understand the meaning behind words and phrases. Semantic search, which aims to interpret user queries based on context, has gained traction in recent years as a more advanced alternative to traditional keyword-based search engines. One crucial aspect of semantic search is the use of knowledge graphs to represent structured knowledge and relationships between entities.

This thesis focuses on the development of a knowledge graph for semantic search, exploring the design, implementation, and evaluation of a system that leverages knowledge graphs to improve information retrieval efficiency and accuracy. The study begins with an introduction to the problem statement and objectives, followed by a comprehensive review of related literature to establish the theoretical foundation for the research.

The methodology section outlines the system design and implementation process, including data collection, entity and relationship extraction, knowledge graph construction, and semantic search algorithm development. The system implementation chapter details the practical aspects of building the knowledge graph system, from data integration to user interface design and testing.

Finally, the conclusion chapter summarizes the key findings, contributions, and implications of the study, offering insights into future research directions in the field of semantic search and knowledge graph development. By exploring the potential of knowledge graphs in semantic search, this thesis aims to advance the state of the art in information retrieval technologies and provide a valuable resource for researchers and practitioners in the field.

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