Reading time: 10 - 12 minutes
When it comes to leveraging your enterprise data, having a wealth of quality data is only half the battle. The other half is having the right tools and technology to help you extract valuable insights from it and uncover new opportunities. That’s why we were eager to introduce metaphactory's Next-Generation Semantic Search (Next-gen Search), as part of the metaphactory 5.0 release.
Investigative knowledge graph exploration & targeted problem solving with metaphactory’s pathfinding interface
Reading time: 4 - 8 minutes
Finding paths in a graph is a well defined space in mathematics and computer science. The Seven Bridges of Königsberg problem from 1736 - which asked to devise a roundtrip through the city of Königsberg in Prussia while crossing each bridge in the city only once - is one of the most famous real world problems and resulted in the foundations of today's graph theory.
While the term pathfinding might often be associated with finding the shortest path (for example, in a geographical context or in computer networks), the seven bridges problem is a good example showing that the shortest path is not necessarily the optimal or desired path for a given problem or information need.
Reading time: 6 - 12 minutes
This blog post refers to metaphactory components that have been deprecated. Please refer to our blog post on next-gen semantic search for up-to-date-information.
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.
Reading time: 3 - 6 minutes
Our mission at metaphacts has always been to ease the onboarding into the world of enterprise knowledge graphs. With our product metaphactory we provide an end-to-end platform to support that mission and enable our clients in unlocking the value of their data assets. Since we first published metaphactory in 2015, with every new release we have introduced new features and capabilities to enable rich end-user experiences in interacting with knowledge graphs.
Through our blog, we want to continuously share some of the recent developments, examples, best practices and make the power of knowledge graph technologies more accessible for you.
Just today we released metaphactory 3.6, so this is a great opportunity to start this blog with showing you some cool new additions to our product. With our most recent release, we have introduced a series of new components and enhancements that help provide a more intuitive user experience and user interaction. These new components cater to user needs across all platform target user groups: end users, developers focused on building end-user oriented applications, as well as knowledge graph experts.