In this blog post, we’ll explore how to build a massive knowledge graph from existing information or external sources in a repeatable and scalable manner. We’ll go through the process step-by-step, and discuss how the Graph-Massivizer project supports the development of multiple large knowledge graphs and the considerations you need to take when creating your own graph. Keep reading!

This post has also been published on the AWS Database blog. 

In this post, we show you how to get started with knowledge graphs using the metaphactory platform backed by Amazon Neptune. Offered by AWS Partner Network (APN) Select Technology Partner metaphacts GmbH, metaphactory helps you build knowledge graphs and the smart applications that use them.

This post has also been published on the Digital Science TL;DR website. 

Despite the increasing availability of public data, why are so many pharma and life sciences organizations still grappling with a persistent knowledge divide? This discrepancy was a focal point at the recent BioTechX conference in October, Europe's largest biotechnology congress that brings together researchers and leaders in pharma, academia and business. In this post, we explore the need to connect data from different sources and all internal corporate data through one, integrated semantic data layer.

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.

In this blog post, we dive into how knowledge graphs play an important role in IKEA's recommendation systems, based on our experience attending two presentations by IKEA at the 2023 Knowledge Graph Conference.

In this blog post, we'll discuss the powerful knowledge graph-based solution that transformed NLB's library and resource management, and how you, too, can leverage these tools to support your organization's data-driven use case! 

This article is the first in a series of two where we discuss our perspective on what is considered a semantic knowledge graph, why it's important, and share how they can drive your enterprise goals forward.

In this blog post, we discuss metaphactory’s app mechanism that supports the composable enterprise approach and look at the following: the components included in an app, the development-staging-production lifecycle of an app, the architecture of an app and an app example.

In a previous blog post we provided a high-level overview on security-related topics for using metaphactory in an enterprise environment. This post will dive deeper into authentication and authorization using single sign-on and also cover how the authentication process can be integrated with databases or external services such as third-party REST endpoints.

The terms "ontology" and "vocabulary" are often used interchangeably. However, more often than not, this leads to confusion among customers who want to semantically model their domain and results in questions about whether there is in fact a distinction between the two and whether both are needed to implement a knowledge graph.

The meta-layers that these terms describe have been captured by different standards (OWL and SKOS respectively) and we at metaphacts believe that there is value in treating both as individual but complementary assets in their own right.

For many of our customers, metaphactory is a key component of their data landscape: their knowledge graph ties together various data silos, provides a semantic access layer based on semantic models, and becomes one of the key systems to support decisions and processes from research to sales.

Keeping all involved systems up and running is a big task that requires many different skills. Besides the operational perspective of working with infrastructure, development, and deployment processes, security plays a growing part in this story.

Forms are a key instrument for collecting and authoring data, while knowledge graphs are the de facto standard for modeling and representing human knowledge. metaphactory delivers a highly configurable semantic form component that empowers you to build user-friendly form interfaces based on the semantic model (ontology¹) in the underlying knowledge graph. End users can use these form interfaces to edit existing data, create new data, and interlink resources in the knowledge graph.

In this blog post, we will provide an introduction to semantic forms in metaphactory and discuss how they support data authoring use cases on top of knowledge graphs. To demonstrate how metaphactory's semantic forms work in practice, we will look at a practical example and will augment the Nobel Prize Dataset with information about scholarly articles. The Nobel Prize Dataset is a public dataset available as a Semantic Knowledge Graph, i.e., it is published in RDF and described by an OWL ontology. We extended the ontology with SHACL shapes to also model relevant constraints which can be utilized within the forms. The ontology and dataset include information about all Laureates (Persons, Organizations) who have received a Nobel Prize Laureate Award in a certain Category, or a share thereof, ever since the inception of the Nobel Prize². Our aim will be to extend this information to include details about scholarly articles published by these laureates.

This blog post is co-authored by Felix Engel and Stefan Schlager. Felix and Stefan work for the department of Biological Anthropology at the University of Freiburg where they lead the development of AnthroGraph – an application that researchers can use to model anthropological data as knowledge graphs and intuitively explore, visualize and find information. In this guest post for the metaphacts blog, they explain how metaphactory was used as the development framework for AnthroGraph and how the resulting application can support the standardization of research data and the creation of reliable, curated and reusable collections of osteological research, ultimately allowing researchers to collaborate across disciplines and perform large-scale analyses.

This blog post introduces metaphactory's vocabulary management features, which extend the platform's knowledge modeling capabilities and support knowledge graph experts, domain experts and business users in creating and editing SKOS vocabularies to capture business-relevant terms. We'll start out by defining what vocabularies are and looking at the use cases they can serve. Then, we'll look at specific vocabulary management features supported in metaphactory. Finally, we'll look at a specific use case and integrate an existing thesaurus into metaphactory and use the platform's semantic structured search component to explore terms and to connect data through relations between entities.

Timely access to consumable, contextual, and actionable knowledge is crucial for any step in the decision-making process and the key enabler of decision intelligence. However, decision makers and decision support systems are still faced with the everlasting challenge that data relevant to and required for addressing their specific information needs is stored in distributed and database- or application-specific silos.

With metaphactory, we serve customers of various sizes and across multiple industries, but no matter whether we're talking about a clinical trial scoping or a bill of materials use case, customers are looking for solutions to address hybrid information needs. That means that end users usually have questions or information needs that are not limited to one single data source or just RDF graph data, but involve simultaneously dealing with a multitude of data sources, a multitude of data modalities and a multitude of data processing techniques.

In our previous post, we covered the basics of how the Ontotext and metaphacts joint solution based on GraphDB and metaphactory helps customers accelerate their knowledge graph journey and generate value from it in a matter of days.

This post looks at a specific clinical trial scoping example, powered by a knowledge graph that we have built for the EU funded project FROCKG, where both Ontotext and metaphacts are partners. It demonstrates how GraphDB and metaphactory work together and how you can employ the platform's intuitive and out-of-the-box search, visualization and authoring components to empower end users to consume data from your knowledge graph.

You can also listen to our on-demand webinar on the same topic or check out our use case brief.

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.

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.

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.