In this guest blog post, Tanuja Gupta, Manager: Knowledge Graphs and Explainable AI at MAN and previously Solutions Architect and Knowledge Graph Ambassador at Scania (both part of TRATON GROUP), explains how knowledge graphs have helped Scania—a world-leading provider of transport solutions— and TRATON GROUP—one of the world’s largest commercial vehicle manufacturers—overcome data challenges and create a more connected, consumable, and actionable data environment across the enterprise and its sister brands.

In this article, Richard Wallis, a distinguished Linked Data and Semantic Web expert and thought leader, shares insights from the recent BIBFRAME Workshop in Europe where he presented a novel knowledge graph-powered solution created for a major national library. He also explores the common challenges that libraries currently face and the opportunities that technologies like linked data and knowledge graphs can offer for managing and connecting massive data volumes, as well as improving the experience for library users.

In this blog post, we’ll explore the importance of establishing policies and frameworks that govern the creation and management of ontologies within your organization. We also look at how metaphactory’s ontology management helps to facilitate proper governance.

Traditional HR recruiting often feels like searching for a needle in a haystack. Companies are inundated with resumes, and candidates are overwhelmed by the sheer number of job listings. Time-consuming manual processes, coupled with the challenge of finding the perfect candidate-job match, hinder efficiency and lead to suboptimal hiring decisions and missed opportunities. To revolutionize this critical function, Zenia Graph co-founders, Aurelije Zovko and Nina Zovko introduce their HR Recruiting Accelerator, a cutting-edge solution powered by the synergy of knowledge graphs and large language models.

If your organization doesn’t already have an enterprise information architecture in place—it should, and if you do have one, it should be based on a semantic model. In this article, we’ll explain what an “enterprise information architecture” is and how it can support your enterprise with decision intelligence, knowledge democratization and enterprise-wide optimization.

In the ever-evolving world of pharmaceuticals, an intriguing strategy has gained traction: drug repositioning. Also known as drug repurposing, reprofiling, redirecting, or switching— this approach involves finding new uses for existing medications. While it comes with its own set of challenges, drug repositioning offers several advantages, including a potential solution to the current slowdown in new drug discovery.

In this blog post, guest author Veronika Heimsbakk, knowledge graph lead at Capgemini, shares her approach to creating semantic knowledge models for clients. Read this guide to learn how she works together with clients to build semantic knowledge models from the ground up and discover practices you can apply to your own semantic modeling initiatives.

This blog post is a recap of a presentation held at the 2023 Knowledge Graph Symposium about BKW Energie's smart meter operations, the data challenges they experienced and how knowledge graphs supported this complex use case.

This post originally appeared on the Ontotext website and is republished and edited with the permission of our partners at Ontotext. In this blog post, we explore Ontotext’s latest knowledge graph-powered solution that works with LLMs to transform raw news content into actionable data for events impact assessment and risk and opportunity detection. 

In this blog post, we discuss the capabilities of the new Dimensions Knowledge Graph and how it helps organizations overcome persistent data challenges in the pharma space and power use cases across the entire pharma value chain. Keep reading!  

In this blog post, we talk about how we created a knowledge graph-powered out-of-box metaphacts Resource Hub that integrates with our content management solution. We also discuss how you can achieve a similar integration with your CMS while reviewing the KompAKI knowledge hub, as another example.

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.

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.

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.