Rosetta Statements: Lowering the Barrier for Semantic Parsing and Increasing the Cognitive Interoperability of Knowledge Graphs
<|reference_start|>Rosetta Statements: Lowering the Barrier for Semantic Parsing and Increasing the Cognitive Interoperability of Knowledge Graphs: Machines need data and metadata to be machine-actionable and FAIR (findable, accessible, interoperable, reusable) to manage increasing data volumes. Knowledge graphs and ontologies are key to this, but their use is hampered by high access barriers due to required prior knowledge in semantics and data modelling. The Rosetta Statement approach proposes modeling English natural language statements instead of a mind-independent reality. We propose a metamodel for creating semantic schema patterns for simple statement types. The approach supports versioning of statements and provides a detailed editing history. Each Rosetta Statement pattern has a dynamic label for displaying statements as natural language sentences. Implemented in the Open Research Knowledge Graph (ORKG) as a use case, this approach allows domain experts to define data schema patterns without needing semantic knowledge. Future plans include combining Rosetta Statements with semantic units to organize ORKG into meaningful subgraphs, improving usability. A search interface for querying statements without needing SPARQL or Cypher knowledge is also planned, along with tools for data entry and display using Large Language Models and NLP. The Rosetta Statement metamodel supports a two-step knowledge graph construction procedure. Domain experts can model semantic content without support from ontology engineers, lowering entry barriers and increasing cognitive interoperability. The second level involves developing semantic graph patterns for reasoning, requiring collaboration with ontology engineers.<|reference_end|>
@article{vogt2024rosetta,
    title={Rosetta Statements: Lowering the Barrier for Semantic Parsing and
  Increasing the Cognitive Interoperability of Knowledge Graphs},
    author={Lars Vogt, Marcel Konrad, Kheir Eddine Farfar, Manuel Prinz, Allard
  Oelen},
    journal={arXiv preprint arXiv:2407.20007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20007},
    primaryClass={cs.DB}
}