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Motivated by our marketing team’s aim to simplify content discovery on our website, we initiated the Ontotext Knowledge Graph (OTKG) project. We envisioned harnessing the power of our products to elevate our entire content publishing process, thereby facilitating in-depth knowledge exploration. What is OTKG?
Very few are in production – and this topic has gone from research to applications very quickly. Cleaning, refining, and aligning your data to shared meaning is the right strategic approach. The post Large Language Models and Data Management appeared first on Ontotext. The technology is very new and not well understood.
This blog post goes through the basics of the joint solution delivered by Ontotext and metaphacts to speed up this journey. At the heart of our solution are Ontotext’s GraphDB and metaphacts’ metaphactory. Using metaphactory, you can easily validate and further refine your semantic model. To Sum It Up.
This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. In the second stage LLM interaction , the Eligibility Design Assistant enables users to refine the criteria and obtain criteria recommendations.
In our previous post, we covered the basics of how the Ontotext and metaphacts joint solution based on GraphDB and metaphactory helps customers accelerate their knowledge graph journey and generate value from it in a matter of days. Today, users from the general public, journalists, etc. LinkedLifeData Inventory Pre-loaded In GraphDB.
In this post, we present several of the key benefits they offer and support them with case studies of Ontotext clients and other examples. and product (What?). Thanks to their modularity, ontologies can be easily modified, expanded or refined to accommodate new concepts, regulations or market developments as the industry evolves.
The stated breadth of its scope and the ongoing efforts at refining its content and broadening its scope present an inherent challenge to its use. Since then the focus has been on the data domain as described in the EDMC’s current description of the product. Ontotext’s GraphDB. Give it a try today!
This blog post will present a NLQ integration for Ontotext GraphDB in LangChain: a framework designed to simplify the creation of applications using LLMs. The generated SPARQL query is wrong in this case, because the WHERE clause results in the Cartesian product between the male and the female characters.
This semantic layer helps computers understand the concepts of a company, supplier, process, and product, and how they are interconnected. What key factors could we optimize, and can you equally optimize the process, product, and supplier? The post The Importance of the Semantic Knowledge Graph appeared first on Ontotext.
As I will explore below, we have brought this same investment in usability to our newest product, Graphite Knowledge Studio, to embrace the same diverse user community we have long served through Graphite. The post Breakthrough Moments in Enterprise Taxonomy Management appeared first on Ontotext.
There are a number of approaches Ontotext uses to transform documents and data of all flavours (e.g. Ontotext has years of experience working with clients to tailor a solution that best addresses the organization’s needs. The distinguishing feature of Ontotext is that these NLP processes are integrated with a knowledge graph.
This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. In this blog post, we dive into the capabilities of Ontotext’s semantic technology products and solutions that facilitate NLQ.
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