Level I - Model Mapping
- Make it easy to map Object-Oriented models to Entity-Relationship models to Semantic-Network models. I.E. implement OO persistence layer in the style of the EAV approach to semantic network databases. Implement auto-translation of data in traditional E/R tables into EAV records. Implement auto loading of data into OO model from arbitrary EAV tuples (and therefore arbitrary relational tables). In other words, automated persistence with automatic data mapping.
- Make it easy to accept ontologies and data from multiple sources; i.e. not just relational database. Example data sources could be: Web searches, Enterprise Silo systems, etc. In other words, build common adapters and mediators to broaden the reach of the "language" beyond structured local databases.
- "Consider the source". Make it easy to associate fuzzy logic factors to data-assertions and ontology-assertions of all granularities, based on the source of the data, the ontology, and even the assertions themselves. Examples are: for any given attribute value, "say's who?", "said when", "how reliable is this source?", "how reliable is this source for this attribute?", "who says that this attribute even applies to this class of thing", "how reliable is the source about the ontology definitions?". I want to be able to encode: "Sam is 89% trustworthy about colors", "Joe lies about AGEs", "Harry is 100% reliable when he says that Joe lies about AGEs", etc.
- Make it easy to handle attribute values that are themselves fuzzy. I.E. Probabilistic attribute values, conflicting values, cluster values, vague values, time varying values, outdated values, missing values, values whose availability is defined by some set of limits on the effort expended in finding the value (e.g. find all values of phone for joe blow that can be found within 10 seconds real time).