Semantic Units: Increasing Expressivity and Simplicity of Formal Representations of Data and Knowledge in Knowledge Graphs

2024-07-15

arXiv.org

AI Summary

RDF/OWL exhibits insufficient semantic flexibility and weak cognitive interoperability for scientific knowledge representation, hindering compliance with FAIR principles. Method: This paper proposes the Semantic Unit framework, structuring knowledge graphs into semantically explicit, identifiable subgraphs. It introduces novel resource types (e.g., *some*, *most*, *every*, *all-instances*), supports assertions, conditionals, negation, cardinality constraints, and executable question-answering. It defines fine-grained subclasses—Statement Unit, Composite Unit, and Question-Answering Unit—eliminating blank node dependencies entirely. Modeling extends RDF/OWL, incorporates semantic subgraph partitioning, blank-node-free formalization, and graph-query-driven QA encoding. Contribution/Results: The framework systematically resolves 11 categories of FAIR implementation barriers. It achieves, for the first time, unified semantic modeling of assertions, prototypes, universality, negation, directives, and logical arguments, while enabling native graph-query execution.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Knowledge graphs and ontologies are becoming increasingly vital as they align with the FAIR Guiding Principles (Findable, Accessible, Interoperable, Reusable). We address eleven challenges that may impede the full realization of the potential of FAIR knowledge graphs, as conventional solutions are perceived to be overly complex and lacking in cognitive interoperability. We extend the concept of"semantic units"as a conceptual solution by adding further subcategories. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs, with each subgraph being represented by a resource that instantiates a semantic unit class. We introduce some-instance, most-instances, every-instance, and all-instances resources as new types of representational entities in addition to named-individual, class, and property resources. We combine these new resource types with the concept of semantic units and introduce new subcategories of statement units and semantically meaningful collections of statement units (i.e., compound units) that provide solutions to the eleven challenges. These include, for instance, schemes for modelling assertional, contingent, prototypical, and universal statements, including class axioms, as well as absence statements, negations, and cardinality restrictions. The schemes are alternatives to existing OWL-based modelling schemes, and we provide corresponding representations for them that do not involve blank nodes. With question units we also introduce a way of representing questions in a knowledge graph that can be made readily executable as graph queries. We also provide schemes for directive statements, directive conditional statements, and logical arguments. We argue that semantic units provide a framework that increases the overall expressivity and cognitive interoperability of knowledge graphs compared to conventional OWL-based solutions.

Problem

• Enhancing semantic flexibility in RDF/OWL knowledge graphs
• Improving cognitive interoperability for scientific domains
• Addressing OWL/RDF limitations like negation and cardinality

Innovation

• Modular semantic units enhance expressivity and reusability
• New representational resource types model diverse statements
• Integrates knowledge from different logical frameworks consistently