Written by Dmitry Pavlov on . Posted in Search
Extracting meaningful and actionable insights from data is only possible if data is easily and intuitively accessible to and searchable for users. But as data accumulates, finding the right bit of information becomes challenging.
Knowledge Graphs have proven extremely powerful in surfacing previously unknown insights and relations in the data. They enable unprecedented query expressiveness and allow to make all instance data and its related metadata searchable, accessible and shareable.
metaphactory is an excellent example of leveraging Knowledge Graphs to bring together information distributed across siloed sources and departments, ultimately empowering end users to unlock the value of data, especially when it comes to search.
This blog post provides an overview of the metaphactory search components. To make the experience as concrete as possible, we provide examples using the most prominent publicly available knowledge graph: Wikidata. For that purpose, we are hosting an instance of our metaphactory platform on top of Wikidata for you to experience. The specific implementations described in this post are based on examples from the Life Sciences domain, but can be adjusted to match information needs across other usage scenarios or verticals.