The phrase "a generic knowledge base" is correct and usable in written English.
It can be used when referring to a broad or general collection of information or data that is not specific to any particular subject or context.
Example: "The software is designed to draw from a generic knowledge base to provide users with relevant information across various topics."
Alternatives: "a general knowledge repository" or "a broad information database."
This solution is a countermeasure to the problem of ontology types and object properties heterogeneity the problem appearing as a natural consequence of the heterogeneity of data sources being used to develop a domain-generic knowledge base such as DBpedia [7].
Of course, a student still needs a knowledge base.
To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies.
Using a generic knowledge management framework we were able to configure and populate a productively used, project specific systems biology knowledge base within 6 month with similar, software development based integration projects being reported to take between 2-5 years [ 13, 23].
We describe a novel computational technique for extracting reaction data and building a generic reaction knowledge base (GRKB) to provide chemists with useful and well-organized knowledge.
In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively.
Thus, the adaptive framework manages the configuration of resources at all three layers (resource, environment, application) by utilizing the defined descriptors and generic implementations of a monitor, an analyzer, a planner, an executor, and a knowledge base component.
For a given metabolite, Meteor predicts the possible transformation steps using a reasoning engine that has a knowledge base composed of generic rules [ 20, 21].
As one of the most important phases of Su-Field analysis, the last step is normally implemented manually with the help of a knowledge base of physical effects, which link generic technical functions with specific applications and systems.
