Reading time: 5 - 10 minutes
Large enterprises have identified knowledge graphs as a solid foundation for making data FAIR and unlocking the value of their data assets. Data fabrics built on FAIR data drive digital transformation initiatives that put companies ahead of the competition.
But while the benefits of knowledge graphs have become clear, the road to their implementation has often been long and complex, and success has relied on the involvement of seasoned knowledge graph experts.
This blog post goes through the basics of the joint solution delivered by Ontotext and metaphacts to speed up this journey.
Reading time: 7 - 13 minutes
Raul Palma leads the data analytics and semantics department at the Poznan Supercomputing and Networking Center (PSNC), where he coordinates the R&D activities and the center’s participation in various EU projects around these topics. In this guest post for the metaphacts blog, Raul explains how knowledge graph technology can address data integration challenges in the agri-food sector, showcasing it through a few use cases. He describes how he leveraged metaphactory to build a domain-specific application - FOODIE - that delivers intuitive access to distributed, heterogeneous data sources and allows end users to extract meaningful insights.
FOODIE is an agriculture knowledge hub delivered as a Web application built on top of metaphactory Knowledge Graph platform. The application enables an integrated view and access over multiple datasets which have been collected from various and heterogeneous sources relevant to the agriculture sector, transformed, and published as Linked Data / in a Knowledge Graph.
Reading time: 4 - 8 minutes
Determining compatibility between individual entities is an essential process for many businesses, across various industries and business models; from industrial configuration, supply chain, bill of materials, evaluating terms in contracts, or even for match making apps. The process may sometimes require the user to check hundreds of thousands or millions of possible combinations, to assess whether components fit together, or if components meet specified requirements. Additional factors may also need to be taken into account, for example, regulations or customer budgets. Traditional approaches are inefficient for modern day applications due to the large volumes of data, heterogeneity of data formats, complexity of customer specifications, and concerns over scalability.