metaphacts Blog
Our blog strives to deliver content, ideas & inspiration to guide & support you on your journey into the world of Knowledge Graphs
An Interconnected System for Reference Data
Reading time: 8 - 15 minutes
Publishing FAIR data in the humanities sector
Reference data is a crucial element of data curation in the cultural heritage and humanities sector. Using reference data brings multiple benefits, such as consistent cataloguing, easier lookup and interaction with the data, or compatibility with other data collections that use the same reference data. Overall, the use of reference data can support the publication of FAIR data - data that is findable, accessible, interoperable and reusable.
In museum collection management, for example, various thesauri can be used as reference data to ensure the accurate and consistent cataloguing of items in a controlled manner and according to specific terminologies. Thesauri exist for various areas of expertise. One example is the Getty Art and Architecture Thesaurus® (AAT) which describes the different types of items of art, architecture and material culture, such as "cathedral" as a type of religious building. Authority data has also been published to support the unique identification of specific entities such as persons, organizations, or places, for example, "Cologne cathedral" as a specific instance of the type "cathedral". Such authority data sources include The Integrated Authority File (GND) or the Union List of Artist Names® Online (ULAN) and are specifically important for disambiguating over entities with the same name, e.g., Boston, the town in the UK, and Boston, the city in the USA.
Digital humanities projects often combine several research directions and use materials that cover multiple disciplinary areas. This makes the implementation of reference data difficult, as several reference data sources need to be used to cover all aspects and facets of a project. Moreover, technical access to reference data is inconsistent, with systems using different interfaces and APIs, which makes integration challenging.
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.
GraphDB & metaphactory Part I: Generating Value from Your Knowledge Graph in Days
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.
Building a Knowledge Graph Application is easier than ever with metaphactory’s intuitive wizards
Reading time: 5 - 10 minutes
Many enterprises have identified Knowledge Graphs as the foundation for unlocking the value of their data assets, easing knowledge discovery and surfacing previously unknown insights and relations in their data. But while the benefits of Knowledge Graphs have become clear, the road to implementation has often been long and complex. Success in making these benefits tangible to the actual business users who interact with and rely on this data on a daily basis has required the involvement of seasoned knowledge graph experts.
In this blog post, we provide an introduction into metaphactory's intuitive and interactive wizards which support application engineers in quickly and visually setting up and configuring search and authoring interfaces that cater to specific end-user information needs. The wizards (introduced recently with the metaphactory 4.2 release ) are one of the pillars of metaphactory's low-code approach for building knowledge graph applications. They allow application engineers to focus entirely on translating end-user information needs into intuitive, model-driven interfaces without getting caught up in the technical details of the semantic technologies stack.
Searching with metaphactory - An Overview
Reading time: 6 - 12 minutes
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.