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We rather see it as a new paradigm that is revolutionizing enterprise dataintegration and knowledgediscovery. It is these two important types of data, which, taken together, implement the Semantic Web vision bringing forward innovative ways of tackling data management and dataintegration challenges.
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. standards modeled in a knowledge graph!
Worse, and according to Gartner, upward of 80% of enterprise data today is unstructured which further exacerbates the loss of knowledge, insights, and the wisdom needed to make effective business choices. As a result, organizations are looking for fresh dataintegration approaches to challenge the mindset with which we created them.
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.
This might be sufficient for information retrieval purposes and simple fact-checking, but if you want to get deeper insights, you need to have normalized data that allows analytics or machine interaction with it. Although there are already established reference datasets in some domains (e.g. Semantic DataIntegration With GraphDB.
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.
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