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Enhancing Knowledge Discovery: Implementing Retrieval Augmented Generation with Ontotext Technologies

Ontotext

So we can easily integrate the information from both textual documents and structured RDF entities into an LLM-driven application. Example use case: Ontotext Knowledge Graph For illustration, we will use a project developed internally by Ontotext that we call “Ontotext Knowledge Graph” or OTKG for short.

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How Pharma Companies Can Scale Up Their Knowledge Discovery with Semantic Similarity Search 

Ontotext

First of all, this solution is able to ingest large amounts of various documents in various formats and to automatically extract and classify pairs of questions and answers. From this processed data a knowledge graph (KG) is created. Then it returns the top 10 most similar Q&A pairs from the database.

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Ontotext Marketing Gets a Boost from Knowledge Graph Powered LLMs

Ontotext

We expose this classified content by flexible semantic faceted search with the help of metaphacts’ knowledge graph platform metaphactory. These steps help pave the way to integrate the knowledge graph with large language models (LLMs) and provide state-of-the-art knowledge discovery and exploration.

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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

Various initiatives to create a knowledge graph of these systems have been only partially successful due to the depth of legacy knowledge, incomplete documentation and technical debt incurred over decades. Coding Assistance Gen AI also helps with coding, including code documentation, code modernization, and code development.

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Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

So, there must be a strategy regarding who, what, when, where, why, and how is the organization’s content to be indexed, stored, accessed, delivered, used, and documented. Labeling, indexing, ease of discovery, and ease of access are essential if end-users are to find and benefit from the collection. Do not forget the negations.

Strategy 266
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Understanding Social And Collaborative Business Intelligence

datapine

Discovery and documentation serve as key features in collaborative BI. This kind of analysis leads to feedback that can aid in improving the decision-making process, letting companies document the best practices and monitor the data that’s the most useful in this scenario. However, collaborative BI helps in changing that.

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Bridging the Gap Between Industries: The Power of Knowledge Graphs – Part I

Ontotext

Here again knowledge graphs organize and link large amounts of data on aircraft design, manufacturing, maintenance and performance. By linking this data, they facilitate tasks like asset management, predictive maintenance, documentation management, mission planning, risk management, aircraft design and optimization, and anomaly detection.