There are philosophical differences and practical differences. They are trying to build an AI scientist ground-up (love it). I would like to optimise for front-line use cases in a lean, user-driven manner. The common denominator technologically is knowledge extraction (text to subject-predicate-object triples), ontological mapping (e.g, these relationships express the same thing, these references are synonyms of this compound, etc.), and reasoning (comes free once you have information properly extracted and mapped with a schema in place thanks to knowledge graph implementations such as GraKn).
Practically speaking: Aristo takes questions in unstructured text, and answers in unstructured text. I'm interested in providing mechanistic queries and comprehensive, highly-structured result sets.
For a question such as, "what are the biological consequences of increasing the activity of molecule A", I want tabular and filterable results (where the number of rows depends on the volume of underlying data and the degrees of separation you carry the inference to). For this reason (alongside their current limitation to elementary science) I argue that Aristo is not currently a relevant resource for researchers and students looking to query and survey biomedical relationships.
The solution I'm aiming at takes a structured query and returns structured results. E.g, a query: [entity: molecule A, direction: increase] generates a list of direct and inferred consequences. It is more like a logic-driven search engine over structured information than it is a question answering system.
Practically speaking: Aristo takes questions in unstructured text, and answers in unstructured text. I'm interested in providing mechanistic queries and comprehensive, highly-structured result sets.
For a question such as, "what are the biological consequences of increasing the activity of molecule A", I want tabular and filterable results (where the number of rows depends on the volume of underlying data and the degrees of separation you carry the inference to). For this reason (alongside their current limitation to elementary science) I argue that Aristo is not currently a relevant resource for researchers and students looking to query and survey biomedical relationships.
The solution I'm aiming at takes a structured query and returns structured results. E.g, a query: [entity: molecule A, direction: increase] generates a list of direct and inferred consequences. It is more like a logic-driven search engine over structured information than it is a question answering system.