Panos Alexopoulos - Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas

by Panos Alexopoulos

Copyright 2020 Panos Alexopoulos. All rights reserved.

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[LSI]

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 its 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 knowledge.

Several languages, methodologies, platforms, and tools are available for building semantic models, coming from different communities and focusing on different model aspects (e.g., representation, reasoning, storage, querying, etc.). However, the overall task of specifying, developing, putting in use, and evolving a semantic model is not as straightforward as one might think, especially as the models scope and scale increases. The reason is that human language and thinking is full of ambiguity, vagueness, imprecision, and other phenomena that make the formal and universally accepted representation of data semantics quite a difficult task.

This book shows you what semantic data modeling entails, and what challenges you have to face as a creator or user of semantic models. More importantly, it provides you with concrete advice on how to avoid dangers (pitfalls) and overcome obstacles (dilemmas). It teaches you some fundamental and enduring semantic modeling principles that remain true, no matter which particular framework or technology you are using, and shows you how you can apply these in your specific context.

After reading this book, you will be able to critically evaluate and make better use of existing semantic models and technologies, make informed decisions, and improve the quality and usability of the models you build.