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Fulfil the promise of a controlled, ubiquitous and universal language across all of your data with the platforms optimised GraphQL layer supporting federation and stitching/mesh. Are you an existing GraphDB user who is planning to build an API layer over GraphDB? Custom code also tends to over-fetch data that is not required.
Here, we want to talk about our flagship product GraphDB – an enterprise-ready RDF database optimized for the development and operations of knowledge graphs. Now, thanks to some of our latest releases, GraphDB allows those who need to work in SQL to access the power of their organization’s knowledge with SQL. Mapping UI.
Much awaited and long overdue, the only fully benchmarked graph database on the market, GraphDB , is now available on AWS Marketplace ready for enterprise adoption. Why GraphDB on AWS? GraphDB robust cloud-based solution, coupled with the underlying IaaS, provides better service RPO/RTO over on-premise solutions.
But to develop a robust data and content infrastructure, it’s important to partner with the right vendors. We offer a seamless integration of the PoolParty Semantic Suite and GraphDB , called the PowerPack bundles. Why PoolParty and GraphDB PowerPack Bundles?
Our friends from the GraphQL Federation have pledged their support. A single GraphQL model acts as a language and grammar that aids communication between developers, domain experts and clients. Web Annotation GraphQL Service. Find Annotations with Droid tags. The Jedi will be overwhelmed. Count Dooku.
Over the years, Ontotext’s leading semantic graph database GraphDB has helped organizations in a variety of industries with their data and knowledge management challenges. Along with building up a stable customer base of knowledge graph users in various industries throughout the years, GraphDB has also generated a thriving community.
Rebel GraphQL developers strike from a hidden base and have won their first victory against the evil SPARQL empire. Star Wars can be queried and mutated using GraphQL, with its galaxy of developer tools. Ontotext’s GraphDB is an enterprise-ready, high performance, scalable and simple to use database. it’s complex?
Those are bold plans, because in 2022 we received recognition for what we’ve achieved through investment to help us expand, accelerate growth and engage the market with the technology we’ve been developing for 20 years. The first 18 years: Develop vision and products and deliver to innovation leaders.
Those are bold plans, because in 2022 we received recognition for what we’ve achieved through investment to help us expand, accelerate growth and engage the market with the technology we’ve been developing for 20 years. The first 18 years: Develop vision and products and deliver to innovation leaders.
In this article we will discuss some of the features of GraphDB that support such an alignment. The Financial Industry Business Ontology (FIBO) is a conceptual model of the financial industry that has been developed by the Enterprise Data Management Council (EDMC). Loading FIBO in GraphDB. it supports RDF 1.1,
Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. As a start, such a platform would need to support two major design patterns: semantic knowledge hub and semantic data fabric.
In 2020, we continued to develop our leading database engine for management of knowledge graphs, GraphDB , and expanded it with a lot of new functionalities. GraphDB Empowers Scientific Projects to Fight COVID-19 and Publish Knowledge Graphs. Check out our 5 releases for this year – 9.1 , 9.2 , 9.3 , 9.4
The Financial Industry Business Ontology (FIBO) is a standard that is being developed and published by the Enterprise Data Management Council that attempts to capture business domain knowledge using sophisticated knowledge representation techniques and linked open data technologies. Introduction. This is a nontrivial task. Historical Context.
We will also describe several different ways to import your on-premise data in Ontotext’s RDF database for knowledge graphs GraphDB together with some examples how to do it. Kafka is a scalable, fault-tolerant system for processing and storing such data and can be used to reliably import data into GraphDB.
The financial services sector was also interested but needed to implement projects faster and there were not many successful mission-critical implementations. SeeNews : Your flagship product is GraphDB. Apart from this application, GraphDB can also provide vulnerability analysis. What would be the potential impact?
of Ontotext’s GraphDB has lots of new bells and whistles that will ensure that it remains the market leader for semantic databases. GraphDB has always been compliant with W3C standards and an active member of the semantic web community. What does it mean for GraphDB clients? Version 9.0 Why going Open Source? The Plugins.
In this part of our series GraphDB in action , we highlight cutting-edge research where GraphDB has been used to power solutions built with semantic data. The paper introduces KnowWhereGraph (KWG) as a solution to the ever-growing challenge of integrating heterogeneous data and building services on top of already existing open data.
What is needed is a technology that can extract and retain the meaning of any new knowledge as well as being able to provide provenance for each underlying fact supporting the scientific conclusions. They also developed a large-scale knowledge graph for an early hypothesis testing tool. How Ontotext Helps. Tried and Tested.
The event attracts individuals interested in graph technology, machine learning and natural language processes in numerous verticals, including publishing, government, financial services, manufacturing and retail. Its remarkable capabilities shine even brighter when delivered jointly with partners.
It is also flexible enough to support adaptation to other large-scale knowledge bases and perform zero-shot entity linking. You can also use it as a service to integrate in your own products or we can feature it as part of a broader custom solution we build for you. Out of the box, the model links to Wikidata IDs.
The other half of our value proposition is Ontotext GraphDB. GraphDB is a best-of-breed RDF database for knowledge graphs that allows linking diverse data, indexing it for semantic search, and enriching it via text analysis to build big knowledge graphs. Here we talk about metadata management, catalog of catalogs, and so on.
This is the power of Zenia Graph’s services and solution powered by Ontotext GraphDB. Reduce costs and make development faster: Traditional data integration methods can be expensive and time-consuming. Click here to learn more about Zenia Graph and its portfolio of GraphDB-based solutions !
These include ETL processes, searching, accessing, data cleansing, data creation, semantic data integration , and the IT infrastructure to support it. And, although we have services like the EMBL-EBI Ontology Xref service providing a schema of all the various mappings between resources, these still need to be maintained and kept up-to-date.
It will illustrate how users with varying levels of technical knowledge, particularly the less tech-savvy ones, can benefit from the Graphwise GraphDB-based approach to retrieval augmented generation (RAG) , underpinned by large language model (LLM) agents. A dedicated service in DBKF detects these automatically.
As all this progresses, the scientific community races against time to respond to the pandemic by developing diagnostic tests, therapies, pre-clinical and clinical research and vaccines. Therefore, Ontotext is making its small but powerful contribution by supporting global COVID-19 related initiatives with its technology.
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