How Different Semantic Search Engines Work
The paper “The Technique of Different Semantic Search Engines” provides an extensive overview of semantic search technology, its evolution, and various semantic search engines. Semantic search represents an advanced search technique that improves search precision by understanding the intent and contextual meaning of search terms, rather than just matching keywords.
Key aspects covered:
1. Semantic Search Process:
– Consists of five steps: query input, analysis, data matching, accessing matched data, and returning relevant content
– Uses linguistic semantics principles and considers context, intent, and meaning
2. Historical Development:
– Origins trace back to Michel Bréal in 1983
– Robert Floyd’s 1967 work on programming language semantics
– Tim Berners-Lee’s contribution to Semantic Web development
3. Comparison with Traditional Keyword Search:
– Semantic search provides more contextually relevant results
– Uses ontology for keyword relationships
– Considers end words and punctuation marks
– Employs semantic web languages (XML, OIL, DAML+OIL, RDF, OWL)
4. Major Semantic Search Engines analyzed:
– Hakia: Uses OntoSem and QDEX technologies
– Kngine: Employs deep learning and unsupervised learning
– Kosmix: Topic-wise web browsing
– DuckDuckGo: Feature-rich disambiguation capabilities
– Powerset: Natural language processing focus
– Sensebot: Text mining and summarization
– Cognition: Focuses on natural language processing
– Swoogle: Indexes RDF-based documents
– Factbites: Emphasizes factual and informative content
5. Statistical Analysis:
– Comparative study between DuckDuckGo and Google
– DuckDuckGo showed higher relevancy (76%) compared to Google (69%)
– Better precision in semantic search results
6. Advantages:
– Improved semantic relationships in query routing
– Better understanding of user intent
– Enhanced conceptual similarity matching
– More accurate content planning and structured data handling
– Superior natural language processing capabilities
The paper concludes that semantic search represents the future of web search technology, with ongoing research focusing on natural language query translation, automatic semantic annotations, and ontology management. It highlights the need for further development in creating more effective search engine technologies compatible with worldwide web standards.
https://www.ijrte.org/wp-content/uploads/papers/v9i1/A2249059120.pdf
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