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The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprisedata? What is it?
Paradoxically, even without a shared definition and common methodology, the knowledge graph (and its discourse) has steadily settled in the discussion about data management, dataintegration and enterprise digital transformation. Maximize the usability of your data. One thing is for sure, though.
Among these problems, one is that the third party on market data analysis platform or enterprises’ own platforms have been unable to meet the needs of business development. With the advancement of information construction, enterprises have accumulated massive data base. Data Warehouse. Data Analysis.
Here, I will draw upon our own experience from client projects and lessons learned to provide a selection of optimal use cases for knowledge graphs and semantic solutions along with real world examples of their applications. Linked Data, subscriptions, purchased datasets, etc.).
Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. ” With new business lines, leading to new tools, a lot of diverse and siloed data inevitably enters enterprise systems. Another term Sumit introduced was datastrophy.
It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. This model is used in various industries to enable seamless dataintegration, unification, analysis and sharing. Want to learn more about how knowledge graphs can help your business?
The age of Big Data inevitably brought computationally intensive problems to the enterprise. Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics. Get these wrong and chances are your enterprise processes and systems will suffer.
Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of dataintegration, data and service-level management. This provides a solid foundation for efficient dataintegration.
As 2019 comes to an end, we at Ontotext are taking stock of the most fascinating things we have done to empower knowledge management and knowledgediscovery this year. In 2019, Ontotext open-sourced the front-end and engine plugins of GraphDB to make the development and operation of knowledge graphs easier and richer.
FROCKG (Fact Checking for Large EnterpriseKnowledge Graphs) is a Eurostars-2 project that aims to develop efficient approaches to ensure the veracity of facts contained in enterpriseknowledge graphs. Semantic DataIntegration With GraphDB. Today, users from the general public, journalists, etc.
Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprisedata management requirements across several verticals. With the help of natural language processing (NLP), text documents can also be integrated with knowledge graphs.
There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. But until they connect the dots across their data, they will never be able to truly leverage their information assets.
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