[Paper Review] IntelligentWeb Agent for Search Engines

Avinash Bhute, B. B. Meshram|arXiv (Cornell University)|Oct 17, 2013

Web Data Mining and Analysis3 references3 citations

TL;DR

This paper proposes an IntelligentWeb Agent framework to enhance search engine performance by automating web content mining through intelligent agents, crawlers, and robots. It addresses limitations in current search engines—such as slow retrieval and low-quality results—by enabling systematic, automated harvesting of web data, including structured information like email addresses, with a focus on improving efficiency and relevance in information retrieval systems.

ABSTRACT

In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. Search engines are retrieve the efficient information. We collected data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. The trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information and users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results. Web agents, programs acting autonomously on some task, are already present in the form of spiders, crawler, and robots. Agents offer substantial benefits and hazards, and because of this, their development must involve attention to technical details. This paper illustrates the different types of agents,crawlers, robots,etc for mining the contents of web in a methodical, automated manner, also discusses the use of crawler to gather specific types of information from Web pages, such as harvesting e-mail addresses

Motivation & Objective

• To address the growing challenge of inefficient and inaccurate information retrieval on the web due to exponential web growth.
• To identify user dissatisfaction with current search engines, particularly regarding slow response times and poor result quality.
• To explore the role of autonomous agents, crawlers, and robots in systematically mining and indexing web content.
• To propose a framework for intelligent agents that enhance search engine capabilities through automated, methodical data collection.
• To evaluate the feasibility and benefits of deploying such agents for targeted information harvesting, such as email addresses and structured data.

Proposed method

• Utilizes web crawlers and robots to autonomously traverse and index web pages in a systematic, automated manner.
• Employs agents to perform specific data mining tasks, such as harvesting email addresses and extracting structured content from web pages.
• Applies a multi-source data collection approach, drawing from current and projected web metrics (users, hosts, websites) to model growth trends.
• Integrates agent-based architectures to improve search engine efficiency by reducing reliance on manual or reactive search methods.
• Leverages existing technologies and frameworks for agent deployment, focusing on scalability and adaptability to evolving web content.
• Emphasizes technical design considerations to balance benefits and risks associated with autonomous agents in information retrieval.

Experimental results

Research questions

• RQ1 How can intelligent agents improve the efficiency and accuracy of web search and information retrieval?
• RQ2 What are the key technical challenges and risks in deploying autonomous agents for web crawling and data mining?
• RQ3 To what extent do current search engines fail to meet user expectations in terms of speed and result quality?
• RQ4 How can agents be designed to systematically extract specific types of information (e.g., email addresses) from unstructured web content?
• RQ5 What role do web growth trends play in necessitating more intelligent, automated search solutions?

Key findings

• The paper identifies that 85% of internet users rely on search engines, yet remain dissatisfied due to slow retrieval and low-quality results.
• Exponential growth in web users, hosts, and websites underscores the need for scalable, automated solutions like IntelligentWeb Agents.
• Crawlers and agents are shown to be effective in systematically harvesting structured data such as email addresses from web pages.
• The integration of autonomous agents into search systems can significantly improve retrieval speed and result relevance.
• The authors highlight that while agents offer substantial benefits, their development requires careful attention to technical and security considerations.
• The study concludes that agent-based systems represent a necessary evolution in search engine technology to keep pace with web growth and user demands.