<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=752538731515435&amp;ev=PageView&amp;noscript=1">

MarkLogic and Semantics

Many people have probably heard the term NoSQL database in the past couple years, but an industry leading enterprise noSQL database has emerged by the name of MarkLogic. Ok, great…so what is MarkLogic and what is the power of a database like MarkLogic?

One of the best explanations of MarkLogic that I’ve heard is that it is a product that has three major features in one. It is a search platform, database, and application server all rolled into one. That still doesn’t really do it justice however. There is much more under the hood than meets the eye, especially around security and ACID compliance. I won’t get into all of those things in this post, but I do want to highlight a very powerful feature called Semantics.

Semantics
Semantics allows the data user to gain insights into their information using context and relationships.

MarkLogic1.png

Semantics boiled down is Subject, Predicate, and Object, also known as triples. In this simple example below, you can easily see that the data represents the fact that John was born in 1945. This is known as a triple and also known as a fact.

MarkLogic2.png




Shown below is a graph which is essentially an interconnected web of information. MarkLogic makes it possible to quickly and efficiently to traverse a graph by using creative indexing and sophisticated search algorithms.

You might be asking at this point, what can triples be used for. If we expand this graph even further to include places that they grew up or schools that they attended, you might find that John and Mary went to the same school together or lived on the same block. We might even be able to infer that it is a good likelihood that John and Mary know each other based on an expanded set of triples.

Another scenario might be applied to fraud detection. Let’s suppose that John had a criminal background and made fraudulent insurance claims. If a triple existed that said John and Mary were married or were in a business relationship together, an insurance company could flag any insurance claims made by Mary as requiring further scrutiny.

MarkLogic3.png

SNL 40 App
A great example of a system build on top of MarkLogic is the SNL 40 App. The folks at Saturday Night Live were tasked with coming up with an interactive experience for their audience to celebrate their 40th anniversary. The SNL 40 App is a powerful application built on top of the MarkLogic database.

If a user decides to base their search on an individual comedian like Will Ferrell, the results are based on the semantic relationships of Will to many of the characters or people that he imitated throughout the years. Semantics plays a role in other searches like eras, seasons, sketches, characters, and more. There are searching capabilities that cross metadata by typing in keywords like “tiny hands.” There is even a predictive analytics feature that provides the user with recommendations of videos to watch.

How Can I Learn More?
I encourage everyone to download the SNL 40 App to see how it works. If you would like to see a website that is built on top of MarkLogic, take a look at the Founders Online site.

There are also a quite a few free reads posted on the MarkLogic site, but here are a couple links that can help you learn more about NoSQL and Semantics:

NoSQL for Dummies

Semantics for Dummies

References
http://www.marklogic.com/blog/semantics-solving-todays-complex-big-data-challenges/#
https://developer.marklogic.com/blog/making-new-connections-ml-semantics

 

Share:
Herb Lichtenberg

About Author Herb Lichtenberg

Herb Lichtenberg is a former Director of Business Development at Omni. He has over 30 years of experience in the technology industry with a focus on understanding business needs, application development and partner channel management.



Disclaimer:

Omni’s blog is intended for informational purposes only. Any views or opinions expressed on this site belong to the authors, and do not represent those held by people or organizations with which Omni is affiliated, unless explicitly stated.

Although we try to the best of our ability to make sure the content of this blog is original, accurate and up-to-date, we make no claims of complete accuracy or completeness of the information on this site/s to which we link. Omni is not liable for any unintended errors or omissions, or for any losses, injuries, or damages from the display or use of this information. We encourage readers to conduct additional research before making decisions based on the information in this blog.