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Chapter 13 Conclusion: Some Personal Thoughts and Opinions 
 
This tutorial is just the entry point to a technology that is entering the Slope of Enlightenment in the 
Gartner technology hype cycle [Gartner Hype Cycle]. Tim Berners-Lee published his paper on the 
Semantic Web [Berners-Lee 2001] way back in 2001. At least in my experience for most large US 
corporations the excitement around Machine Learning seemed for a while to eclipse serious interest in 
OWL, SPARQL, and other Semantic Web technologies in the United States. Then influential technology 
companies such as Google [Singhal 2012], Facebook [Olanof 2013], and Amazon [Neptune 2017] started 
to embrace the technology using the term Knowledge Graphs [Noy 2019] and the corporate world is 
finally realizing that machine learning and knowledge graphs are complimentary not competitive 
technologies.  
The term knowledge graph itself can be used in different ways. The best definition I’ve heard is that an 
ontology provides the vocabulary (i.e., essentially the T-Box) and a knowledge graph is an ontology 
combined with data (A-Box). Although in the corporate world I often hear people simply talk about 
knowledge graphs without much interest in the distinction between the vocabulary and the data.  
There are a number of vendors emerging who are using the technology in very productive ways and are 
providing the foundation for federated knowledge graphs that can scale to hundreds of millions of triples 
or more and provide a framework for all corporate data. I’ve listed several in the bibliography but those 
are only the ones I’ve had some experience with. I’m sure there are many others. One of the products I’ve 
had the best experience with is the AllegroGraph triplestore and the Gruff visualization tool from Franz 
Inc. Although Allegro is a commercial tool, the free version supports most of the core capabilities of the 
commercial version. I’ve found the Allegro triplestore easy to use on a Windows PC with the Docker tool 
to emulate a Linux server.  
I first started working with classification-based languages when I worked at the Information Sciences 
Institute (ISI) and used the Loom language [Macgregor 91] to develop B2B systems for the US 
Department of Defense and their contractors. Since then, I’ve followed the progress of the technology, 
especially the DARPA knowledge sharing initiative [Neches 91] and always thought there was great 
promise in the technology. When I first discovered Protégé it was a great experience. It is one of the best 
supported and most usable free tools I’ve ever seen, and it always surprised me that there weren’t more 
corporate users leveraging it in major ways. I think we are finally starting to see this happen and I hope 
this tutorial helps in a small way to accelerate the adoption of this powerful and robust tool.