GraphAware Blog

We are proud to announce the release of Hume version 2.15. This release introduces several new Hume features, as well as Orchestra features and Amazon SQS support.

So far, we have learned about collaborative filtering, content-based, and session-based recommendations. None of these approaches takes the situational context under consideration. Factors such as mood, occasion, location, company, etc., can affect user preferences and needs. Context-aware recommendations take these conditions into account to provide more relevant recommendations.

Advanced Expand is one of our favourite features. It allows you to explore your graph visually - without needing to write a single line of code. In this blog post, we dive a little bit deeper into this feature.

Even a long journey starts with the first step. Making sense of all the data your company generates daily and all the external data sources you may need can definitely be a long journey. Hume Orchestra makes it easy to convert multiple distributed data sources into a single, connected source of truth: the knowledge graph.

We are proud to announce the release of Hume version 2.14. This release introduces the first iteration of the new federated search functionality and the Hume API. We also made various improvements to Hume Orchestra and Visualisations.

Knowledge Graphs (KGs) have become the backbone of multiple applications, including search engines, chatbots, and question and answering tools, where interactivity plays a crucial role.

Graph visualisation is just what it sounds like - a visual representation of your data as a graph. A graph is a structure of objects that are connected. Thus graph visualisation is the visualisation of entities (nodes), and relationships among them.

With today’s blog, we will dive a little bit into front-end application development. When creating complex applications, as we do in GraphAware, we need to handle large and complicated application states to present our users with correct user interfaces. Making application state predictable and explainable is one of the key challenges for the successful agile frontend development team. As we often like to point out, graphs are proving to be a successful solution to many existing problems. So, could we learn from graphs to handle application state in a more predictable manner?

Graph-Powered Machine Learning has already introduced us to content-based recommendations and collaborative filtering. These are the two most used approaches to providing recommendations. However, they both need information about the users to do so. What if you do not have user information? That’s where session-based recommendations come in.

We are proud to announce the 2.13 release of Hume. Advanced Expand has been extended to include much more functionality, so you build a large variety of queries visually. We also boosted the capabilities of Alerts.