Architecture

The framework consists of three levels:

1. knowledge base: It uses Jena TDB as triple store for storing meta-information of ontologies (i..e namespace and URL), list of the registered data and models web services, and thesaurus of standard vocabularies. It can create a URIs for the elements that are not serviced. It stores these types of information under four main concepts space, time, standard vocabularies, and resources
   
   
2. Knowledge management: It contains four functional components
   1. Resources harvester: it is context-based recommendation system that works with models and data
      1. for a model can propose workflows based on the I/O
      2. for a dataset can provide its contextual relationships with other data
      3. can suggest models in based on the available data collection
         
         
         
   2. Logic ingestion: 
      1. ingests new standard vocabularies thesaurus and infers their relationships based on schema matching and string matching
      2. Linked vocabularies network 
         
         
         
   3. Reasoner:
      1.  validates and extracts facts from the ontologies stored in the knowledge-base, we started by using Pellet reasoner but we plan to add more reasoners such as KAON2
         
         
         
   4. Semantic processor: 
      1. SPARQL query 
      2. Provide semantic mediation based on SKOS standards 
      3. Semantic alignment between resources
      4. Composition of RDF tags 
   
   
   
3. Web services:
   1. Knowledge discovery:
      1. Registers resources and  their meta-information  
      2. Takes search statements from external resources  
         
         
   2. Semantic tagging:
      1. Retrieves the URL of an ontology or standard name thesauri and can manipulate the elements of an ontology (add, edit, delete)
      2. Collects tags from registered resources (e.g. SEAD, DataOne)
         
         
   3. Data alignment:
      1. checks the consistency of the exchanged items between a model and another model or data  
         
         
   4. Ontology mapping: 
      1. Collects Vocabularies from the Semantic Mediawiki and match them with the registered ontologies  
      2. Recommends relationships between controlled vocabularies