GRAPHxx: DSML engineering for knowledge graph building and streamlining with GraphRAG

Abstract Symbolic AI has been facing long-term adoption obstacles and a slow uptake caused by the limited availability of frictionless tooling—that can support not only knowledge engineers, but also novice users and educators, with designing and presenting graph exemplars that can be visually communicated, edited and ad hoc processed. There’s still a shortage of tools that can democratize knowledge graph (KG) creation, something that is increasingly needed—firstly by educators trying to discuss examples with novices without stumbling on OWL jargon at the earliest step, but also for more recent integration cases such as (i) GraphRAG, where private KGs are called to augment or ensure factuality of large language model services; or (ii) in search engine optimization (SEO), where SEO practitioners lacking knowledge engineering background must embed Schema.org graph data into their Web content. Most visual KG tools are visualizers of KGs created by other means—either in OWL-centric ontology editors posing high expertise barriers, or converted from available serializations, or lifted from legacy data sources. The few actual KG editors are mostly OWL editors neglecting to support the creation of schemaless graph datasets as well as of flexible combinations of graph data and schema fragments. This paper reports on a Design Science effort adopting metamodeling means, traditionally employed for the engineering of domain-specific modeling languages, toward defining a KG development and integration method that facilitates both the visual design of KG exemplars and their operationalization. We aim to balance a diagrammatic look and feel with machine readability of the semantic content being produced—further streamlined in an architecting proposition for integration with LLM services and for the production of Schema.org graph snippets. The DSML was deployed and evaluated as a tool implemented on the ADOxx metamodeling platform, using RDF and LangChain as mediators that streamline the content toward triplestores and LLM services.