# OntoKG: Ontology-Oriented Knowledge Graph Construction with Intrinsic-Relational Routing

arXiv: https://arxiv.org/abs/2604.02618 · HTML: https://mlsanity.com/paper/2604.02618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [A Study of the Quality of Wikidata](https://mlsanity.com/paper/2107.00156/) (2021)
- [CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset](https://mlsanity.com/paper/2310.16225/) (2023)
- [Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema](https://mlsanity.com/paper/2412.20942/) (2024)
- [Wikidata-lite for Knowledge Extraction and Exploration](https://mlsanity.com/paper/2211.05416/) (2022)
- [Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy](https://mlsanity.com/paper/2511.04926/) (2025)

## Abstract (arXiv)

Organizing a large-scale knowledge graph into a typed property graph requires structural decisions -- which entities become nodes, which properties become edges, and what schema governs these choices. Existing approaches embed these decisions in pipeline code or extract relations ad hoc, producing schemas that are tightly coupled to their construction process and difficult to reuse for downstream ontology-level tasks. We present an ontology-oriented approach in which the schema is designed from the outset for ontology analysis, entity disambiguation, domain customization, and LLM-guided extraction -- not merely as a byproduct of graph building. The core mechanism is intrinsic-relational routing, which classifies every property as either intrinsic or relational and routes it to the corresponding schema module. This routing produces a declarative schema that is portable across storage backends and independently reusable. We instantiate the approach on the January 2026 Wikidata dump. A rule-based cleaning stage identifies a 34.6M-entity core set from the full dump, followed by iterative intrinsic-relational routing that assigns each property to one of 94 modules organized into 8 categories. With tool-augmented LLM support and human review, the schema reaches 93.3% category coverage and 98.0% module assignment among classified entities. Exporting this schema yields a property graph with 34.0M nodes and 61.2M edges across 38 relationship types. We validate the ontology-oriented claim through five applications that consume the schema independently of the construction pipeline: ontology structure analysis, benchmark annotation auditing, entity disambiguation, domain customization, and LLM-guided extraction.