Improving the Relevance, Speed, and Computational Efficiency of Semantic Search through Database Indexing: A Review
In a large dataset, finding top most relevant matches to fulfill search queries and similar application becomes an increasing challenge. Facing this challenge, researchers and engineers has done extensive work in both regime of algorithm design and system implementation to create practical solutions...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2023
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| Acceso en línea: | https://www.intechopen.com/chapters/87865 |
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| Sumario: | In a large dataset, finding top most relevant matches to fulfill search queries and similar application becomes an increasing challenge. Facing this challenge, researchers and engineers has done extensive work in both regime of algorithm design and system implementation to create practical solutions that fuel powerful tools. Techniques like caching, search index, fuzzy indexing has been widely adopted in both SQL and XML data models, then provided to the database user, the application programmer, as a product. In this chapter, we review data indexing algorithms that efficiently compute semantic search query results, and discuss systems that implement them. We compare the pros and cons of each algorithm, in terms of time and space complexity, as well as implementation difficulty. We also analyze trade-offs in real-world system design examples, in hope to provide the readers more insights when they want to efficiently find an answer through semantic search query, or even build their own application using these databases. |
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| ISBN: | 978-1-83769-180-7 |
| DOI: | 10.5772/intechopen.112232 |