Knowledge graphs are a pivotal building block for designing intelligent systems for empowering advanced analytics and decision making. Knowledge Graphs Applied is a practical guide complete with techniques, code samples and use cases, allowing you to leverage the connected nature of various data sources and simultaneously incorporate human knowledge. Knowledge graphs facilitate creation of solutions which are highly valued by engineers, data scientists and CEOs alike.
What’s inside
- Model business specific knowledge graphs with an iterative top-down approach
- Craft knowledge graphs starting from ontologies, taxonomies, and structured data
- Use machine learning algorithms to develop and hone your graphs
- Build knowledge graphs from unstructured text data sources
- Reason on the knowledge graph and apply machine learning algorithms
Meet the authors
All the authors have extensive experience in the domain of building and analyzing knowledge graphs. Together they cover expertise in engineering, research, data science and consultancy, all delivered for and with clients operating in a wide span of industries all around the world. Currently they are contributing to building an enterprise-level product for mission-critical graph analytics – GraphAware Hume.
Alessandro Negro
Chief Scientist
Fabio Montagna
Lead Machine Learning Engineer
Knowledge Graphs Applied is a practical guide to putting knowledge graphs into action. It’s full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets.
