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Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. Decide and act on the delivered insights and knowledge. Do you present your employees with a present for their innovative ideas? Can you find them all?
Although there is still no single, universally accepted definition, there have been various attempts at it – such as in Towards a Definition of Knowledge Graphs. Yet, the concept of knowledge graphs still lives without an agreed-upon description or shared understanding. Maximize the usability of your data.
The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. In this post you will discover the aspects of the Semantic Web that are key to enterprise data, knowledge and content management. Source: tag.ontotext.com. What is it? Which Semantic Web?
Buildings That Almost Think For Themselves About Their Occupants The first paper we are very excited to talk about is KnowledgeDiscovery Approach to Understand Occupant Experience in Cross-Domain Semantic Digital Twins by Alex Donkers, Bauke de Vries and Dujuan Yang. Ground Control to Major Knowledge Graph!
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. Data Visualization.
Well, it’s all thanks to knowledge graphs. Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. This model is used in various industries to enable seamless dataintegration, unification, analysis and sharing.
Seen through the three days of Ontotext’s Knowledge Graph Forum (KGF) this year, complexity was not only empowering but key to the growth of knowledge and innovation. ” With new business lines, leading to new tools, a lot of diverse and siloed data inevitably enters enterprise systems. Cunningham.
What Makes a Data Fabric? Data Fabric’ has reached where ‘Cloud Computing’ and ‘Grid Computing’ once trod. Data Fabric hit the Gartner top ten in 2019. This multiplicity of data leads to the growth silos, which in turns increases the cost of integration. It is a buzzword.
Poor data management, data silos, and a lack of a common understanding across systems and/or teams are the root cause that prohibits an organization from scaling the business in a dynamic environment. As a result, organizations have spent untold money and time gathering and integratingdata.
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.
Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. Linked Data, subscriptions, purchased datasets, etc.).
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. Data normalization is an essential step in the data preparation process.
This often leaves business insights and opportunities lost among a tangled complexity of meaningless, siloed data and content. Knowledge graphs help overcome these challenges by unifying data access, providing flexible dataintegration, and automating data management.
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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