We live and breathe semantic search. It’s what we think about each and every day. We start and end each day thinking about how we can help you do structured data faster, smarter, more accurately and at scale. Our Schema App platform is unique in the market, in its architecture and schema.org specialty. If you want something comprehensive, automated and grows with your business it’s built for you.
Not just a structured data widget, it’s built on semantic web technology. Why does this matter? Because it’s built on semantic technology it has built in flexibility that allows us to do inferences, quick fixes and better maintenance than most platforms out there. Everything from the RDF Graph database to the APIs and User Interface is written in RDF and SPARQL. Schema.org is a lightweight ontology, and our applications use ontologies as well. Graph Databases, RDF, SPARQL and Ontologies are the most flexible technologies making our platform ultimately flexible.
We can import your dataset without even knowing what’s in the data, then as we uncover the data meanings we add schema.org mappings as necessary. That means we can produce a working prototype in less time, can make iterations on those faster than traditional ETL and relational data-warehouse tools. Maintenance is also easier because of the URIs (Uniform Resource Identifier) and additive ontology modeling. How do we do this? Graph databases are schema-less which means you can put data in and bind your schema as you need it. This makes data integration pipelines easier to start, iterate and maintain.
We have business rules built in and the ability to add your custom rules, in less than 5 minutes. We have rules derived from Google’s Structured Data Tools applied to classes, e.g. schema.org/CreativeWork and any of its descendants. The business rules, are categorized and put in their own Graph. This means that the business rules can be mix and matched, allowing us to include / exclude sets of rules or application logic.
It is very easy for us to update your data, business rules and data processing when schema.org changes. Everything in our system uses URIs, so if schema.org changes the definition of a Class or Property, the URI of that concept is seen universally across our application logic, data processing flows, customer business rules, customer data so that we can do a global replace in under 5 minutes.
We are able to import the latest Schema.org vocabulary while augmenting with fixes we have discovered from our large userbase. The Schema.org data model is represented in RDFa, which uses a series of Subject-Predicate-Object statements known as triples. A RDF Graph Database can easily consume these and then build on these.
We express all of our RDF content in JSON-LD, a developer friendly syntax that is the new W3C standard for linked data on the web.
A benefit of RDF based data models, is the ability to reuse your entities in multiple statements. For instance, an Organization may be the value (object) of 12 Article properties. As we have it, you could use the same definition of Organization for each of these statements. Defining and reuse of concepts builds out your knowledge graph, preparing your site for the future direction of Google.
Business rules ensure it gets applied properly. Our crawler will report on what you have deployed and you can leverage our Google Analytics integration to track how entities and properties on webpages perform vs the status quo or historical results.
Our intent is that you can leverage our platform with limited to no use of a Developer. To this end, we have made it so that you can share your data with us easily and have your website consume our output of JSON-LD through different integration points.
Outgoing JSON-LD data