Semantic Clustering of Search Engine Results

Area of Science:

• Information Retrieval
• Natural Language Processing
• Data Mining

Background:

• Traditional search result clustering often relies on keyword matching, leading to clusters that may not accurately reflect document content.
• The need for more sophisticated methods to group search results based on semantic understanding is crucial for improving user experience and information access.

Purpose of the Study:

• To propose and evaluate a novel search result clustering approach that leverages document semantics.
• To demonstrate the effectiveness of semantic similarity over lexical similarity in forming meaningful clusters.
• To enhance the precision of search result clustering through a meaning-based methodology.

Main Methods:

• Developed a novel clustering approach focusing on the semantics of retrieved documents.
• Incorporated both lexical and semantic similarities for robust document comparison.
• Applied an activation spreading technique to generate semantically coherent clusters.
• Implemented a prototype system for experimental validation.

Main Results:

• Experimental results indicate that the proposed semantic clustering approach achieves remarkable precision.
• The method successfully groups documents based on underlying meaning, outperforming term-based clustering.
• The activation spreading technique effectively identifies semantically related documents for clustering.

Conclusions:

• The novel semantic clustering approach offers a significant improvement over traditional term-based methods.
• Leveraging document semantics is key to generating more accurate and meaningful search result clusters.
• The proposed method provides a precise and effective solution for organizing information retrieved from search engines.