Intelligent Semantic Web Search Engines: A Brief Survey (1102.0831v1)

Abstract: The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.

• The paper provides a comprehensive review of semantic search engines, emphasizing their potential to overcome traditional keyword-based limitations.
• It evaluates various intelligent models, including general purpose, file-system based, and interactive systems that leverage semantic technologies.
• The study discusses challenges in query formulation and user intention identification while outlining future research directions in semantic web technologies.

Intelligent Semantic Web Search Engines: A Brief Survey

The paper "Intelligent Semantic Web Search Engines: A Brief Survey" by G. Madhu, Dr. A. Govardhan, and Dr. T. V. Rajinikanth, published in the International Journal of Web & Semantic Technology (IJWesT) in January 2011, provides an extensive review of the landscape of intelligent and semantic web search engines. The authors highlight the advancements, challenges, and potential future directions in this area, dividing their discussion into several core components.

Introduction and Background

The authors begin by contextualizing the concept of the Semantic Web, an extension of the current Web that enables precise descriptions of information using structured vocabularies. Utilizing technologies like RDF, RDFS, and OWL, the Semantic Web supports enhanced discovery, automation, and interoperability. This semantic infrastructure is critical in overcoming the limitations of traditional search engines like Google, Yahoo, and Bing, which predominantly operate on keyword-based information retrieval.

Current Web and Its Limitations

The paper identifies significant challenges in present-day web search mechanisms:

1. Lack of Structured Content: Current web content lacks a coherent semantic structure, limiting machines' understanding of user information.
2. Ambiguous Information: Poor interconnection of information leads to ambiguous search results.
3. Challenges in Automatic Information Transfer: There is a deficit in effective automatic information exchange.
4. Scalability and Trust: Traditional search engines struggle to ensure trust and handle massive volumes of content.
5. Incapability of Machines: Absence of a universal format hinders machines from comprehending provided information.

Semantic web technologies, with their structured data representation, can mitigate these issues by enhancing the quality and relevance of retrieved information.

Intelligent Search Engines

The authors review various intelligent search engines developed to address these challenges. For example, the research by Fu-Ming Hung and Jenn-Hwa Yang integrates description logic inference systems with digital library ontologies to improve search efficiency. Agent-based systems, proposed by Inamdar and Shinde, leverage software agents to enhance web mining by considering contextual user behavior.

Types of Semantic Search Engines

Several types of semantic search engines are examined:

1. General Purpose Semantic Search Engines: Engines like Hakia aim to provide meaning-based search results rather than popularity-based results. Their proprietary QDEXing technology processes diverse digital artifacts using semantic ranking.
2. File-System Based Semantic Search Engines: Eureka, designed by Bhagwat and Polyzotis, uses an inference model to build links between files, ranking them according to semantic importance.
3. Interactive Models: Dan Meng and Xu Huang discuss an interactive search engine model based on user preferences, which enhances the quality of information retrieval using artificial intelligence methods.

Performance Metrics and Common Issues

The paper discusses the common issues faced by current semantic search engines:

1. Low Precision, High Recall: Some engines fail to significantly improve precision while lowering recall.
2. User Intention Identification: Effective semantic search relies heavily on accurately identifying user intent, which remains a challenge.
3. Query Formulation: Users often fail to formulate precise queries due to limited domain-specific knowledge and inadequate inclusion of synonyms or term variations.

Conclusion

The survey concludes by outlining the potential for future research to focus on dynamic and static knowledge structures, improving precision, user intention identification, and addressing experimental validation issues. The advancements in intelligent semantic search technologies demonstrate significant potential for more efficient and effective information retrieval, along with improved user satisfaction.

References

The paper concludes with a comprehensive list of references, contributing to the understanding and future exploration of intelligent semantic web search engines and their implications for the broader field of information retrieval and artificial intelligence.

This paper offers valuable insights into the current state of semantic web search engines and outlines the direction for future research and development. The emphasis on addressing practical challenges and exploring robust intelligent search methodologies underscores the ongoing evolution in the domain of semantic web technologies.