Using Oracle’s AI Vector Search to Enable Concept-Based Querying across Structured and Unstructured Data
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P115Keywords:
Oracle AI, Vector Search, Concept-Based Querying, Structured Data, Unstructured Data, Embeddings, Semantic Search, Knowledge ManagementAbstract
The tremendous growth of organised and unorganised information in enterprise has resulted in serious problems about how to access information and manage knowledge. Non-lucrative traditional search systems favorable to searching by keywords do not always find the semantic connection between the various types of data with a consequent incomplete or irrelevant search. In order to overcome this shortcoming, Oracle has built a vector search engine, driven by artificial intelligence, which uses deep learning embeddings to facilitate concept-based querying. This paper discusses how the Oracle AI Vector Search can be used in enterprise data ecosystems to better the accuracy of retrieval, further the semantic understanding, and connect the structured and unstructured data. We communicate the architecture, methodologies and implementation strategies underlining and give experimental results that feature the improving nature over the traditional means. This can be applied with semantic searches, similarity detection, as well as recommendation systems using more refined semantic representations
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