Ontology-Structured Knowledge Graphs

• Ontology-structured knowledge graphs are graph-based representations that combine formal ontologies, explicit schema definitions, and contextual metadata to support deductive reasoning and robust data integration.
• They are constructed and optimized using manual, automated, and LLM-assisted pipelines that enhance schema alignment, query performance, and error correction.
• Applications span data integration, geospatial analytics, and predictive modeling, with ongoing research addressing scalability, interoperability, and semantic quality assurance challenges.

Ontology-structured knowledge graphs (KGs) are graph-based representations of facts that are structured and governed by a formal ontology, comprising explicit schemata, logical constraints, and identity and context mechanisms. In such KGs, the ontology articulates classes, properties, their relationships, and axioms, providing logical semantics that enable deductive reasoning, semantic querying, and interoperation across heterogeneous data sources. By augmenting nodes and edges representing factual assertions with rich schema and contextual metadata, ontology-structured KGs enable robust integration, advanced analytics, and transparent knowledge management across domains.

1. Foundations: Ontology Integration, Schema, Identity, and Context

The defining characteristic of an ontology-structured knowledge graph is the explicit formalization of schema, identity, and context (Hogan et al., 2020). The schema, typically defined in OWL (Web Ontology Language) or Description Logic, provides the semantic backbone: classes (concepts), subclass hierarchies ($C \sqsubseteq D$), properties (relations with domains and ranges), and rich axioms (disjointness, equivalence, cardinality). For example, subclass chains are formalized as $$\text{Food Festival} \sqsubseteq \text{Festival} \sqsubseteq \text{Event}$$, supporting deductive closure under inference.

Identity is managed by persistent, globally unique IRIs, with $owl:sameAs$ axioms implemented to reconcile multiple IDs for the same entity across sources, for example:

$$\mathrm{wd:Q2887}~owl:sameAs~\mathrm{turismo:Santiago}$$

Contextual extension is a key differentiator: the validity of a fact is qualified by temporal, geographic, or provenance-sensitive dimensions, modeled explicitly via direct annotations (e.g., time intervals as properties), statement reification, or higher-arity constructs. A reified fact may use a "qualifier node" to link subject, predicate, object, and context properties, supporting principled context tracking within the graph.

2. Methodologies: Construction, Optimization, Refinement

Ontology-structured KGs are built and maintained through a variety of human-in-the-loop and (semi-)automated processes:

• Manual and Crowd-sourced Construction: Human experts or collaborative efforts curate ontologies and populate KGs with high-quality assertions.
• Extraction from Text/Semi-structured Data: Automated pipelines use NLP (Named Entity Recognition, Relation Extraction) and mapping frameworks (e.g., R2RML, CSV-W) to triplify structured sources while enforcing ontology compliance (Hogan et al., 2020).
• Schema Optimization Algorithms: For property-graph-based systems, ontological information can optimize query performance: merging nodes through union/inheritance detection and replicating list properties to reduce traversals. Formal rules like Jaccard similarity for property alignment,

$$JS(c_i.P_i, c_j.P_j) = \frac{|c_i.P_i \cap c_j.P_j|}{|c_i.P_i \cup c_j.P_j|}$$

guide schema transformation. Optimization is typically posed as a cost-benefit problem under storage constraints, sometimes reduced to $0$-$1$ Knapsack and solved with FPTAS (Lei et al., 2020).

• Automated Completion and Correction: Refinement uses both inductive (link prediction via embeddings that minimize $||\mathbf{e}_s + \mathbf{r} - \mathbf{e}_o||$) and deductive (logical constraint checking, OWL reasoner) mechanisms. Correction mechanisms include detecting and repairing inconsistencies via minimal hitting sets or triple removals.
• Human-in-the-loop and LLM-driven Pipelines: LLMs assist or automate many steps, including competency question formulation, entity/relation extraction, and schema alignment (e.g., via Wikidata property mapping). LLMs are guided via modular ontologies and prompt engineering to produce triples conforming to the schema, and fine-tuning (e.g., LoRA) or adaptive chain-of-thought algorithms further reduce hallucinations and error rates (Norouzi et al., 3 Nov 2024, Abolhasani et al., 30 Nov 2024).

3. Query Languages, Visualization, and User Accessibility

SPARQL is the lingua franca for ontology-structured KGs, supporting both data querying (SELECT, ASK, CONSTRUCT, DELETE, INSERT) and schema-aware reasoning through entailment regimes (Cuddihy et al., 2017, Hogan et al., 2020). Advanced toolkits (e.g., SemTK's "nodegroups") allow users to visually assemble queries, trigger automated pathfinding (modified A* for class linkage), and export optimized SPARQL queries.

For non-Semantic-Web experts, frameworks such as OBA auto-generate REST APIs from ontologies, translating OWL semantics to JSON/RESTful interfaces using OpenAPI schemas and bidirectional JSON-LD transformations (Garijo et al., 2020).

Visualization leverages ontology design patterns (ODPs) annotated with OPLa to present KGs through conceptual groupings, thematic paths, and multilevel graphical frames. This approach reduces cognitive load and systematically organizes data exploration along foundational semantic axes (Asprino et al., 2021).

4. Quality Metrics, Impact, and Comparative Structural Analysis

Ontology structure quality is evaluated on several axes (Seo et al., 2022):

Metric	Definition / Formula	Significance
Instantiated Class Ratio	$ICR = \frac{N(IC)}{N(C)}$	Usage of defined classes
Instantiated Property Ratio	$IPR = \frac{N(IP)}{N(P)}$	Usage of defined properties
Class Instantiation	$$CI(Class) = \sum_{i=1}^n \frac{ir(c_i)}{2^{d(c_i)}}$$	Granularity, subclass granularity
Subclass Property Acquisition	$$SPA = \frac{\sum N(P_{subclass} - P_{superclass})}{N(C)}$$	Subclass expressiveness
Subclass Property Instantiation	$SPI(Class) = N(T_{class\_unique}) / N(T_{Class})$	Effective property utilization
Inverse Multiple Inheritance	$IMI = \frac{1}{\frac{1}{N(C)}\sum nsup(C_i)}$	Simplicity of class hierarchy

High-quality KGs exhibit extensive class/property use, well-segmented but not overly complex hierarchies, and active utilization in instance data. Comparative studies show that pure scale (num. triples/classes) is often less informative than these utilization metrics.

5. Synergistic Neural-Symbolic Approaches and Advances

Recent methodologies integrate ontological constraints with neural models for completion, question answering, and knowledge base construction:

• Ontology-Enhanced KGC with LLMs: Hybrid approaches like OL-KGC extract ontological rules as textual logic, embed graph structure via adapters, and prepend both (as prefix tokens) to the LLM input. LLMs are then fine-tuned so reasoning becomes aware of explicit symbolic constraints, resulting in state-of-the-art triple validation and interpretability (Guo et al., 28 Jul 2025).
• Retrieval-Augmented Generation Grounded by Ontology: The use of ontology-based RAG enables LLMs to generate answers and mapping rationales supported by retrieved ontology subgraphs (e.g., biomedical code mapping), with all updates reflected by KG refresh rather than LLM retraining (Feng et al., 26 Feb 2025).
• LLM-Aided Semi-Automatic KG Construction: LLMs generate and refine competency questions, extract relations, and align to existing ontologies (e.g., Wikidata schema), with vector similarity for property mapping and human/LLM validation for schema expansion (Feng et al., 30 Dec 2024). Pipelines are designed for scalability and minimal intervention.
• Interoperability and Integration: Use of shared, language-agnostic primitives, clear distinction of concepts/types, and reified abstract objects facilitate cross-lingual and multi-domain integration (Saba, 2023). Ontology-constrained KGs are interoperable with linked public resources via standardized properties and provenance (Shimizu et al., 2022).

6. Technical and Practical Applications

Ontology-structured KGs are deployed across diverse applications:

• Data Integration and Virtualization: Enterprise KGs unify multiple heterogeneous sources through an ontology-governed virtualization layer, supporting both population and query (Gorshkov et al., 2021).
• Geospatial Analytics: Modular ontologies support alignment of heterogeneous layers (e.g., climate, hazard, demographic data via S2 Grid Cells), collective querying, and event-specific briefings (Shimizu et al., 17 Oct 2024).
• Predictive Analytics: Markov models are integrated with KGs governed by ontologies (e.g., BFO/CCO), enabling predictive state transitions and looped ingestion of inferred probabilities as information content entities (Sculley et al., 26 Jul 2025).
• Design Reasoning: Rule-based workflows transform context-poor, structured legacy data into FBS-ontology-aligned KGs, supporting advanced, automated querying and design knowledge reuse (Sahadevan et al., 8 Dec 2024).

7. Future Directions and Research Challenges

Emergent themes for ontology-structured KGs include:

• Joint Deductive-Inductive Refinement: Integrating logical entailment (from DLs/OWL reasoners) with inductive learning (embeddings, GNNs) for improved analytics and consistent graph completion (Hogan et al., 2020).
• Scalable, Automatable, and Updatable Pipelines: LLMs offer scalable automation but require precise prompt and module engineering, with human-in-the-loop for validation amidst persistent risks like hallucinations (Norouzi et al., 3 Nov 2024, Kommineni et al., 13 Mar 2024). Efficient adaptation to ontology/schema updates without LLM retraining is a current focus (Feng et al., 26 Feb 2025).
• Advanced Query and Visualization Languages: There is a need for semantic- and context-aware query languages (e.g., recursive or annotation-rich SPARQL), as well as interactive pattern-based visualization tools (Asprino et al., 2021).
• Semantic Quality Assurance: Automated techniques for detecting and correcting schema bias, redundancy, and inconsistency are becoming crucial, as well as metrics that more precisely measure utilization and meaningfulness.
• Interoperable, Language-Agnostic Structures: Adoption of robust, language-independent primitives and careful modeling of concepts/types is vital for cross-domain, multilingual KG integration (Saba, 2023).

Ontology-structured knowledge graphs represent an overview of logic-based formalization, scalable engineering, and practical alignment with the complexities of real-world knowledge management, forming the basis for reliable inference, integration, and automated reasoning in knowledge-intensive applications.