Search Engines
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Search engines use webcrawlers or bots to index web pages by following hyperlinks and recording the words on each page. Federated search allows libraries to search across multiple databases from a single search box, providing a more unified and Google-like search experience for library users. Enterprise search tools are used internally by businesses to improve findability for tasks like competitive intelligence. The author argues that library search engines should be more powerful and open to the public in order to create communities and incorporate personalized search features.
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This document discusses search engines and their role in accessing digital information on the internet. It describes how search engines work by building indexes of web pages using software robots that crawl the web. It outlines the types of search engines, including single, niche, and multiple-threaded search engines. It also discusses some common problems with search engines, such as incomplete indexing of the vast internet, outdated or inaccurate content, and technical challenges in uniformly classifying different types of digital information.
Quest Trail: An Effective Approach for Construction of Personalized Search En...
This document discusses developing a personalized search engine for software development organizations. It proposes using semantic analysis and genetic algorithms to personalize search results. Semantic analysis resolves ambiguity in queries by understanding their meaning, while genetic algorithms use machine learning to better understand user preferences over time. Quest analysis is also used to identify the goal or task behind a user's search by analyzing search logs at the quest level rather than query or session levels. Together these approaches aim to increase search relevance for users in software organizations by creating group profiles based on domain or project rather than individual user profiles.
How discovery impacts of users' experiences
In the 21st century the academic library supports both research activities and teaching outcomes of faculty members and students through web-scale discovery services. These discovery services embrace new technologies to provide deep discovery of vast scholarly collections from a one-stop access interface, relying on a central index of pre-harvested data. With unified indexing of full-text library content, users’ experience of search and retrieval is greatly improved.
Discovery is changing the way that library users find and access library materials, especially electronic resources. In the opening part of this presentation, I will share my experiences of using different discovery systems – Summon, Primo and Enterprise – in my current and previous roles, in term of differences, strengths and common areas among these tools. Relevant findings from the literature and latest research reports will be sketched. I will also speak of how technical services teams can support the next generation of discovery systems that will help the progress of the digital library field. The presentation will conclude with the approach of technical services towards future discovery.
Comparison of Semantic and Syntactic Information Retrieval System on the basi...
In this paper information retrieval system for local databases are discussed. The approach is to search the web both semantically and syntactically. The proposal handles the search queries related to the user who is interested in the focused results regarding a product with some specific characteristics. The objective of the work will be to find and retrieve the accurate information from the available information warehouse which contains related data having common keywords. This information retrieval system can eventually be used for accessing the internet also. Accuracy in information retrieval that is achieving both high precision and recall is difficult. So both semantic and syntactic search engine are compared for information retrieval using two parameters i.e. precision and recall.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
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Internet Research: Finding Websites, Blogs, Wikis, and More
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