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Need to ingest unstructured data into your RAG pipeline? Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Brian King’s Post
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Chief Data Scientist | Data Governance, Machine Learning Specialist | Ph.D. in Computational Sciences and Informatics
A lot of key points covered in the attached figure - search for data vs search into data (Thanks to John Stegeman and Ole Olesen-Bagneux for solidifying this for me early on), data plane vs information plane vs knowledge plane (Knowledge Mining was the term of 2003 that pulled me into pursuing Statistics, Machine Learning and Informatics - Thanks to the late Dr. Michalski and Dr. Kirk Borne, Ph.D.) Ping me if you would like to discuss these and similar data centricity topics - anytime! - Hitachi Vantara Federal | Jonathan Ferguson | Michael Donahue, MBA
🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise ontology to data that instantiate its concepts is equivalent to building a virtual knowledge graph. A knowledge graph is a semantic layer that allows for searching first for the data (search for things) and only then into the data (search for strings). 🤖 🧑 This type of semantic search simplifies life both for business users, who can access data in a way that aligns with their mental model of the processes that generated the data, and for intelligent agents, who need the right context to operate in unfamiliar domains. 👇 To leverage an enterprise ontology for accessing data, it is first necessary to connect it to the data. For this purpose, it is possible e to use: 1️⃣ ontologies such as #DPROD as a basis to represent all metadata related to the developed data products 2️⃣ ontologies such as #R2RML or, more generally, #RML, to create mappings between the data product schemas, the concepts of the enterprise ontology, and the physical structures in which the data are stored. #TheDataJoy #knowledgeGraph #ontology #graphRAG #semanticSearch
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🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise ontology to data that instantiate its concepts is equivalent to building a virtual knowledge graph. A knowledge graph is a semantic layer that allows for searching first for the data (search for things) and only then into the data (search for strings). 🤖 🧑 This type of semantic search simplifies life both for business users, who can access data in a way that aligns with their mental model of the processes that generated the data, and for intelligent agents, who need the right context to operate in unfamiliar domains. 👇 To leverage an enterprise ontology for accessing data, it is first necessary to connect it to the data. For this purpose, it is possible e to use: 1️⃣ ontologies such as #DPROD as a basis to represent all metadata related to the developed data products 2️⃣ ontologies such as #R2RML or, more generally, #RML, to create mappings between the data product schemas, the concepts of the enterprise ontology, and the physical structures in which the data are stored. #TheDataJoy #knowledgeGraph #ontology #graphRAG #semanticSearch
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Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
-
How to Improve RAG Quality by Storing Knowledge Graphs in Vector Databases #llm #knowledgegraph #rag #vectordatabase #machinelearning https://lnkd.in/e6tpS6Af
Improve RAG Quality With Knowledge Graphs - DZone
dzone.com
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Supercharge Your RAG application With #Couchbase Vector Search and Unstructured.io
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
https://www.couchbase.com/blog
