Semantic Modeling for Data

Preface

Knowledge graphs, ontologies, taxonomies, and other types of semantic data models have been developed and used in the data and artificial intelligence (AI) world for several decades. Their use captures the meaning of data in an explicit and shareable way, and enhances the effectiveness of data-driven applications. In the past decade, the popularity of such models has particularly increased. For example, the market intelligence company Gartner included knowledge graphs in its 2018 hype cycle for emerging technologies; and several prominent organizations like Amazon, LinkedIn, BBC, and IBM have been developing and using semantic data models within their products and services.

Behind this trend, there are two main driving forces:

• Data-rich organizations increasingly realize that it’s not enough to have huge amounts of data. In order to derive value from it, you actually need this data to be clean, consistent, interconnected, and with clear semantics. This enables data scientists and business analysts to focus on what they do best: extracting useful insights from it. Semantic data modeling focuses exactly on tackling this challenge.

• Developers and providers of AI applications increasingly realize that machine learning and statistical reasoning techniques are not always enough to build the intelligent behavior they need; complementing them with explicit symbolic knowledge can be necessary and beneficial. Semantic data modeling focuses exactly on building and providing such ...