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Semantic search

From Wikipedia, the free encyclopedia

Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query.[1] Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.

Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web.[2] Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.[3] The articulation enhances content relevance and depth by including specific places, people, or concepts relevant to the query.[4]

Knowledge Graphs

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Tools like Google’s Knowledge Graph provide structured relationships between entities to enrich query interpretation.[5]

Vector Representations (Embeddings)

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Models like BERT or Sentence-BERT convert words or sentences into dense vectors for similarity comparison.[6]

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Semantic ontologies like OWL, RDF, and Schema.org organize concepts and relationships, allowing systems to infer related terms and deeper meanings.[7]

Hybrid Search Models

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Combines lexical retrieval (e.g., BM25) with semantic ranking using pretrained transformer models for optimal performance.[8]

Applications

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  • Web Search: Google and Bing integrate semantic models into their ranking algorithms.
  • E-commerce: Intent-based product searches improve conversion and discovery.[9]
  • Enterprise Search: Corporate systems use it for document retrieval, customer support, and knowledge management.[10]
  • Healthcare and Legal Research: Facilitates retrieval of case law, research articles, and clinical data.[11][12]

Challenges

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  • Ambiguity and Polysemy (e.g., "jaguar" as an animal or a car brand)
  • Bias in Training Data[13]
  • Computational Costs of deep semantic models[14]
  • Multilingual Performance[15]

Future Directions

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  • Conversational Search and voice interfaces
  • Multimodal Search: Incorporating video, image, and text together[16]
  • Explainability and ethical transparency in semantic systems

See also

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References

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  1. ^ Bast, Hannah; Buchhold, Björn; Haussmann, Elmar (2016). "Semantic search on text and knowledge bases". Foundations and Trends in Information Retrieval. 10 (2–3): 119–271. doi:10.1561/1500000032. Retrieved 1 December 2018.
  2. ^ Dong, Hai (2008). A survey in semantic search technologies. IEEE. pp. 403–408. Retrieved 1 May 2009.
  3. ^ Ruotsalo, T. (May 2012). "Domain Specific Data Retrieval on the Semantic Web". The Semantic Web: Research and Applications. Eswc2012. Lecture Notes in Computer Science. Vol. 7295. pp. 422–436. doi:10.1007/978-3-642-30284-8_35. ISBN 978-3-642-30283-1.
  4. ^ Nowak, Ken (2024). What is semantic seo?. WeAreKinetica. Retrieved 21 June 2024.
  5. ^ Singhal, A. (2012). Introducing the Knowledge Graph: things, not strings. Google Blog. https://blog.google/products/search/introducing-knowledge-graph-things-not/
  6. ^ Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP 2019. https://arxiv.org/abs/1908.10084
  7. ^ Bodenreider, O. (2004). The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32(suppl_1), D267–D270.
  8. ^ Lin, J., et al. (2021). Pretrained Transformers for Text Ranking: BERT and Beyond. https://arxiv.org/abs/2010.06467
  9. ^ Amazon Science. (2021). Using neural retrieval for semantic product search. https://www.amazon.science/blog/using-neural-retrieval-for-semantic-product-search
  10. ^ IBM. (2020). Using AI and machine learning for smarter enterprise search. https://www.ibm.com/blogs/research/2020/11/ai-enterprise-search/
  11. ^ Wang, Q., et al. (2020). COVID-19 literature retrieval with semantic search. Nature, 582, 560–561.
  12. ^ Chalkidis, I., et al. (2020). LEGAL-BERT. https://arxiv.org/abs/2010.02559
  13. ^ Bender, E. M., et al. (2021). On the Dangers of Stochastic Parrots. FAccT 2021. https://dl.acm.org/doi/10.1145/3442188.3445922
  14. ^ Schwartz, R., et al. (2019). Green AI. Communications of the ACM, 63(12), 54–63.
  15. ^ Pires, T., Schlinger, E., & Garrette, D. (2019). How multilingual is Multilingual BERT? https://arxiv.org/abs/1906.01502
  16. ^ Radford, A., et al. (2021). CLIP: Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020
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