How Does GraphDB Tidy Up Information Pieces?
The way GraphDB tidies information is by putting a label on each and every piece of the information and then storing the labelled pieces in places where they can be very easily reached.
To understand how GraphDB works, imagine a robot with a magnetic arm that is set to automatically clean the mess in your room and thus makes it easy for you to find any toy within seconds, fetching it to you when you ask for it or showing you where you can get it by yourself.
First, the robot uses the magnetic arm to pick pieces of information. Then the robot labels and loads the pieces onto a train car. The labels contain more descriptions (adults call this semantic metadata) and the train cars help the piece of information go anywhere, anytime when needed (adults call them URIs). Train cars usually travel in triples (two things connected by a third) and this is also why GraphDB is sometimes called a triplestore. Because it stores data in triples – two train cars connected by a coupling that is labeled, too.
For example, the statement: “Mike has a Tyrannosaurus Rex” would travel in two train cars – Mike and Tyrannosaurus Rex loaded in each and coupled with a coupling, named “has” (couplings serve adults to express relationships between things, in this case possession).
GraphDB tidies up not only to help people get rid of the mess but also to discover things. When all the pieces of information are stored in the train cars, they can be very easily found and assembled in many combinations. Guess what assembles them! A locomotive (adults call it a SPARQL query). Whenever someone needs some kind of information, they send a locomotive to connect the needed train cars (with the information pieces in them) and bring them the right information in seconds.
Example of GraphDB Tidying Up Your Room
If GraphDB was a physical robot, and not a program on your computer and it had to clear a mess of toys in your room instead of a mess of information pieces, it would tidy it up in 3 steps:
- First, the robot will use its special magnetic arm to get the toys and classify them.
- Second, the robot will add detailed labels on each and every toy and load the toys in train cars.
- Third, the robot will systematize them to be neatly classified and used when needed.
After the robot declutters your room and organizes everything in these three steps you will be able to do a lot more with your toys. You will be able to ask the robot to find a toy with whatever words you might want to use. For example, you can ask for the toy you played with two days ago, or for the dinosaur you played with yesterday, that has green wings and blows fire. And there’s even more. You will also be able to ask the robot to give you all the toys that don’t belong to you. If the robot has labeled a toy with a label: “Belongs to Tom”, the robot will automatically know whether a toy is yours or not (adults call this inference).
Example of GraphDB Tidying Up Your Daddy’s Documents
Let’s say your daddy is a journalist. He is writing an article about Tyrannosaurus Rex. Throughout his research he has gathered tons of information about dinosaurs and in particular about Tyrannosaurus Rex. But the thing is, with so many facts and statements on his Kindle, and on his voice recorder, he has a really hard time finding the specific ones he needs for the article. In this case, GraphDB will help him store and organize everything in one place and then help him quickly and easily find the most relevant information for his article – facts, images, sounds, similar articles, related topics.
Thus your GraphDB will give your daddy access to any type of information from anywhere – from his computer and from the Web. Daddy will be able to explore, connect and find new facts and statements about Tyrannosaurus Rex.
To recap, when all the information pieces are labelled and stored in their places, they become ready to travel across the Web and across computers and connect with other pieces of information (adults call these train cars, labeled and loaded with information pieces – semantically enriched smart data).
How Does GraphDB Help?
When one uses GraphDB, they can quickly and easily find things and also do a bunch of other cool stuff such as:
- connect facts and statements from many sources (adults call this Data Integration and Interlinking);
- use facts to create new facts (adults call it Reasoning);
- uncover hidden links (grown up kids call this Relationship Discovery);
- track where data came from (adults call this Data Provenance);
- search with all kinds of terms and questions (adults call this Semantic Search);
- represent facts and the relationships between them in easy-to-understand graphics (adults call this Data Visualization).
By and large, this is what GraphDB does and can be used for. For more detailed explanation, suitable for grown-up kids, check Ontotext’s Fundamentals on the subject: What is RDF Triplestore?