International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Enhancing Information Retrieval with SearchEase: A Comprehensive Approach to Efficient Search Palak Tiwari1, Vibhuti Sharma2, Dr. Sudha Tiwari3 , Priyanka Devi4 1,2 BTech Student, Dept of Information Technology, Government Engineering College Bilaspur, Chhattisgarh, India 3,4 Assistant Professor, Dept of Information Technology, Government Engineering College Bilaspur, Chhattisgarh,
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Abstract - Search engines play a crucial role in accessing and retrieving information from vast digital repositories. However, existing search technologies often face challenges in efficiently indexing and searching diverse data types, including text documents and images. In this research paper, we introduce SearchEase, a novel search engine that integrates inverted index search and vector search techniques to enhance information retrieval capabilities. We present the architecture, methodology, implementation details, and experimental results of the SearchEase system. Our findings demonstrate the effectiveness of SearchEase in improving search speed, accuracy, and relevance, offering a promising solution for efficient information retrieval in various domains. Key Words: Information Retrieval, Search Engines, Inverted Index, Vector Search, Natural Language Processing
1.INTRODUCTION In the era of big data, efficient information retrieval is essential for accessing and analyzing vast amounts of digital content. Traditional search engines rely on inverted index search algorithms, which map terms to documents, enabling fast keyword-based searches. However, these methods may struggle with unstructured data and semantic understanding. The SearchEase project addresses these challenges by integrating inverted index search with vector search techniques to provide a comprehensive solution for efficient information retrieval.
2. LITERATURE REVIEW Previous research in information retrieval has explored various indexing and searching techniques, including inverted index search, vector space models, and neural network-based approaches. Introduction to Information Retrieval (Manning, Raghavan, & Schütze, 2008) provides a comprehensive overview of fundamental IR principles, including indexing, retrieval models, and evaluation metrics. The book emphasizes the importance of inverted index structures for enabling fast keyword-based searches, laying the groundwork for subsequent research in the field.[1].Speech and Language Processing (Jurafsky & Martin, 2019) delves into the intersection of natural language processing (NLP) and information retrieval. It discusses techniques for text processing, syntactic analysis, and semantic understanding, offering insights into the challenges and opportunities in building intelligent IR systems.[2]. Mining of Massive Datasets (Rajaraman & Ullman, 2011) explores scalable algorithms and techniques for processing large-scale data. The book discusses distributed computing frameworks and data mining approaches relevant to IR, highlighting the importance of scalability and efficiency in handling massive datasets.[3]. Text Mining: Approaches and Applications (Choudhury et al., 2020) presents a comprehensive overview of text mining techniques and their applications across various domains. The book discusses methods for information extraction, topic modeling, and sentiment analysis, offering valuable insights into the diverse applications of IR in real-world scenarios.[4]. Inverted index search algorithms, such as those used in traditional search engines like Google, rely on indexing terms to documents, facilitating fast keyword-based queries. However, these methods may struggle with semantic understanding and relevance ranking. Vector search techniques, on the other hand, represent documents and queries as high-dimensional vectors in a semantic space, enabling similarity-based searches. Recent advancements in vector search, including word embeddings and deep learning models, have shown promising results in improving search accuracy and relevance. By combining inverted index search with vector search, SearchEase aims to leverage the strengths of both approaches to enhance information retrieval capabilities.
3. METHODOLOGY The SearchEase system comprises several key components, including data ingestion, preprocessing, indexing, and searching. Data ingestion involves extracting text and multimedia content from various sources, followed by preprocessing
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