Web Search Engine, Web Crawler, and Semantics Web

Web Search Engine, Web Crawler, and Semantics Web

1. WEB Search Engine

2. A Web search engine is a specialized computer server that searches for information on the Web. The search results of a user query are often returned as a list (sometimes called hits ). The hits may consist of web pages, images, and other types of files. Some search engines also search and return data available in public databases or open directories. Search engines differ from web directories in that web directories are maintained by human editors whereas search engines operate algorithmically or by a mixture of algorithmic and human input. Web search engines are essentially very large data mining applications . Various data mining techniques are used in all aspects of search engines, ranging from crawling INTRODUCTION

3. The first internet search engines predate the debut of the Web in December 1990: WHOIS user search dates ba c k t o 198 2 , a n d t he k no w bot i n f or m a t ion bot m ul t i- network user search was first implemented in 1989. The first well documented search engine that searched content files, namely FTP files, was Archie, which debuted on 10 September 1990. Prior to September 1993, the World Wide Web was entirely indexed by hand. There was a list of webserves edited by Tim lee and hosted on the CERN webserver. One snapshot of the list in 1992 remains, but as more and more web servers went online the central list could no longer keep up. On the NCSA site, new servers were announced under the title "What's New!".

4. Understanding Search Algorithms A search algorithm is a unique formula that a search engine uses to retrieve specific information stored within a data structure and determine the significance of a web page and its content. Search algorithms are unique to their search engine and determine search engine result rankings of web pages.

5. Enhancing Search Experience Invest in query understanding and query rewriting. ... Focus on your head queries before chasing your tail. ... Size matters — result set size, that is. ... Capture head queries using autocomplete. ... Pay attention to the overall search experience. ... Measure everything, but keep your metrics simple.

6. Challenges and Future Directions As the digital frontier expands, web search engines face numerous challenges. Explore the future directions of search engines as they tackle issues like information overload, fake news, and privacy concerns. We will discuss the potential solutions and advancements that will shape the next generation of web search engines.

7. Conclusion Web search engines have revolutionized how we access information, empowering us with a wealth of knowledge at our fingertips. Through this presentation, we have explored the virtuosities of these powerful tools, from their evolution to their future prospects. Let us embrace the endless possibilities that web search engines offer as we navigate the digital frontier.

8. Unleashing the Potential of the Semantic Web: A Paradigm Shift in Information Organization and Integration

9. The Semantic Web, Web 3.0, the Linked Data Web, the Web of Data…whatever you call it…represents the next major evolution in connecting and representing information. It enables data to be linked from a source to any other source and to be understood by computers so that they can perform increasingly sophisticated tasks on our behalf. This lesson introduces the Semantic Web, putting it in the context of both the evolution of the World Wide Web as we know it today as well as data management in general, particularly in large enterprises.

10. A huge advantage of the semantic web is possessing huge quantities of information, data, and knowledge which is translated to be comprehensible and ready for machines, including virtual assistants, agents, and AI bot . It’s extremely easy to mix different data sets through the RDF data structure, thanks to its simplicity and optional nature. Big data projects will see this as a useful advantage, where the different types of information within a business can sometimes be troublesome to analyze and organize. Advantages Of Semantic Web

11. Ap p l i c at io n s o f th e S e m a n ti c Web The applications of the Semantic Web are vast and diverse. It is used in knowledge management , data integration , smart cities , e-commerce , healthcare , and more. By leveraging semantic technologies, we can improve decision-making, automate processes, and e n ab l e i nn ov a t iv e se r v i ce s .

12. Challenges and Future Directions While the Semantic Web brings immense potential, it also poses challenges. These include data quality , scalability , privacy , and semantic heterogeneity . Overcoming these challenges requires collaboration, standardization, and continuous research to ensure the success and widespread adoption of the Semantic Web.

13. The Art Of Web Crawling

14. Web scraping or web crawling refers to the procedure of automatic extraction of data from websites using software. It is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where data isn’t provided in machine readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as Introduction

15. Seed detector − The service of the Seed detector is to decide the seed URLs for the definite keyword by fetching the first n URLs. The seed pages are identified and assigned a priority depending on the PageRank algorithm or the hits algorithm or algorithm same to that. Crawler Manager − The Crawler Manager is an essential component of the system following the Hypertext Analyzer. The component downloads the files from the global web. The URLs in the URL repository are retrieved and created to the buffer in the Crawler Manager. The URL buffer is a priority queue. It depends on the size of the URL buffer, the Crawler Manager dynamically creates an instance for the crawlers, which will download the files. For more effectiveness, the crawler manager can generate a crawler pool. The manager is also answerable for limiting the speed of the crawlers and balancing the load between them. This is completed by inspecting the crawlers. Crawler − The crawler is a multi-thread Java code, which is adequate for downloading the web pages from the web and saving the files in the document repository. Every crawler has its queue, which influences the list of URLs to be crawled. The crawler retrieved the URL from the queue Components of a Web Crawler

16. Enhancing Document Indexing and Keyword Search

17. Document indexing is a crucial process for organizing and categorizing textual information. It involves assigning relevant keywords to during searches. Effective indexing ensures accuracy and relevance of search results, enhancing overall information retrieval . Various techniquessuch as term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) can be employed to improveindexing. Document Indexing

18. Keyword search is a widely used method forretrieving information from large documentcollections. It involves matching user- provided keywords with indexed documents to identify relevant results. However, traditional keyword search approaches may suffer from ambiguity and lack of context . This slide explores techniques like semantic analysis and query expansion to overcome these challenges and improve the accur a cy of k e ywo r d s e a r ch

19. Efficient Information Retrieval The efficiency of information retrieval (IR) algorithms has always been of interest to researchers at the computer science end of the IR field, and index compression techniques, intersection and ranking algorithms, and pruning mechanisms have been a constant feature of IR conferences and journals over many years. Efficiency is also of serious economic concern to operators of commercial web search engines, where a cluster of a thousand or more computers might participate in processing a single query, and where such clusters of machines might be replicated hundreds of times to handle the query load (Dean 2009 ). In this environment even relatively small improvements in query processing efficiency could potentially save tens of millions of dollars per year in terms of hardware and energy costs, and at the same time significantly reduce greenhouse gas emissions. In commercial data centers, query processing is by no means the only big IR consumer of server processing cycles. Crawling, indexing, format conversion, PageRank calculation, ranker training, deep learning, knowledge graph generation and processing, social network analysis, query classification, natural language processing, speech processing, question answering, query auto-completion, related search mechanisms, navigation systems and ad targeting are also computationally expensive, and potentially capable of being made more efficient. Data centers running such services are replicated across the world, and their operations provide every-day input to the lives of billions of people. Information retrieval algorithms also run at large scale in cloud-based services and in social media sites such as Facebook and Twitter.

20. Conclusion In conclusion, enhancing document indexing and keyword search requires a comprehensive approach that focuses on textual representation and efficient information retrieval. By leveraging advanced techniques like semantic analysis , textual representation models , and optimized retrieval mechanisms , we can overcome challenges and improve search capabilities. This not only benefits users but also enhances the overall efficiency of information retrieval systems.

21. Enhancing Search Quality

22. Objecti v e Evaluate search quality to identify areas for improvement and enhance user experience. Analyze various factors including relevance, accuracy, and efficiency. Implement strategies to optimize search algorithms and ranking methods.

23. Evaluation metrics are used to measure the quality of the statistical or machine learning model. Evaluating machine learning models or algorithms is essential for any project. There are many different types of evaluation metrics available to test a model. These include classification accuracy, logarithmic loss, confusion matrix , and others. Classification accuracy is the ratio of the number of correct predictions to the total number of input samples, which is usually what we refer to when we use the term accuracy.

24. Evaluation Process Conduct offline evaluation using collected data and relevance judgments. Perform online evaluation with A/B testing to assess real-time impact. Iterate and refine search algorithms based on evaluation results.

25. Examining Measures Of Similarity :Cosine Similarity , Jaccard Similarity , and Document Resemblance .

26. Cosine Similarity is a measure used to determine the similarity between two non-zero vectors. It calculates the cosine of the angle between them, resulting in a value between -1 and 1. Higher values indicate greater similarity

27. Jaccard Similarity Jaccard Similarity is a measure used to compare the similarity between two sets. It is calculated by dividing the size of the intersection of the sets by the size of their union. The resulting value ranges from to 1, with 1 indicating complete similarity.

28. Document Resemblance Document Resemblance is a measure used to assess the similarity between two text documents. It considers various factors su c h as wor d fre q uen c y, document length, and term weights. Higher values indicate a h i g her d e g r e e o f r e s e mb l anc e .

29. A Formal Exploration of Hyperlink Ranking in Social Network Analysis, PageRank, Authorities and Hubs, and Link- Based Similarity Search

30. Social Network Analysis Social Network Analysis (SNA) is a powerful framework for studying relationships among entities. In the context of the web, SNA allows us to analyze the structure and dynamics of the hyperlink network. We will examine how SNA techniques can be used to uncover important nodes and communities in the web graph.

31. PageRank Algorithm PageRank is a widely used algorithm for measuring the importance of webpages. It assigns a numerical value to each page based on the quality and quantity of incoming links. We will discuss the underlying principles of PageRank and its significance in web search and ranking.

32. Conclusion In conclusion, hyperlink ranking algorithms play a crucial role in understanding the web's structure and identifying important webpages. Through this presentation, we have explored the concepts of Social Network Analysis , PageRank , Authorities and Hubs , and Link-Based Similarity Search . These algorithms have wide-ranging applications in web search, recommendation s y s t e m s, a n d i n fo r m a t io n r e t ri e v a l , m a k i n g th e m e ssen t i a l t o o ls for understanding and navigating the vast web landscape.

33. THANKS!