The third characteristic is the notion of consensuated knowledge, it’s a shared vocabulary. The description being explicit is very important. That sounds a lot like a graph, doesn’t it? Most of the modeling languages used for ontologies, are based on RDF which is actually a graph model. I’m talking about entities connected to other entities. It’s not like a natural language text description it has to be an enumeration of the entities that belong to these domain and how they relate to each other. The second characteristic is the ontology is an explicit description of a domain. It has to be machine readable, that’s a key point. The first characteristic of an ontology is it has to be a formal representation. An ontology needs to have these three characteristics. We create entity relationship models, we create models when using modeling tools, we create models when we write something on a white board. We build models for many things, we create models when we are going to create a database. However, it has three characteristics that make it a bit different and a bit particular. It’s a representation of a particular domain. That became quite popular in the early 2000s.Īn ontology is a form of representing knowledge in a domain model. Now, we have general purpose applications that use that knowledge. They wanted smarter data that we could do smarter things with. Proponents of the Semantic Web suggested instead of publishing a natural language text, to publish better structured data, self describing data, data described in terms of well defined semantics. At the other end was a human that needed to be able to understand that text. What you had at the other end was a client not an application. The data published was pretty dumb, it was text. If you remember, the web at the time was purely for human consumption. There’s a number of benefits and a great example of this idea of knowledge representation and reasoning that Tim Berners Lee shared about the Semantic Web. By doing that, you reuse knowledge that you don’t have to replicate in any application. The idea with knowledge representation is to make your data smarter in a way that you are able to move some of the application logic out of it and make it data. In any solution, you will find your application logic being a fat component and your data tends to be simple data. Another use is inferencing which is actionable knowledge of fragments.įinally, Barrasa shares a video example of ontologies and graphs using Neo4j and NeoSemantics. Interoperability is the definition of shared vocabulary. In Neo4j, there are two main uses of ontologies. Barrasa also gives an example of ontology using which compares to a Google search. The FIBO ontology fragment describes a number of finance terms and relationships. An example of knowledge presentation would be publishing structured data, self-described data or data described in terms of well defined semantics as opposed to natural language text.īarrasa give an example of ontology using a fragment of the FIBO ontology. Knowledge representation is the idea to make ones data smarter in a way that you are able to move some of the application logic out of it and make data. These all fall under the ontology umbrella. Ontology is an umbrella term that could also represent knowledge representation and reasoning (KR), natural language, machine or automated learning, speech, vision, robotics and problem solving. Ontology is a form of representing knowledge in a domain model. In today’s talk, he speaks from his background in semantic technologies.īarrasa starts with a brief introduction to ontology. Jesús Barrasa is the director of Telecom Solutions with Neo4j. Editor’s Note: This presentation was given by Jesús Barrasa at GraphConnect New York City in September 2018.
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