# Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation

arXiv: https://arxiv.org/abs/2604.03496 · HTML: https://mlsanity.com/paper/2604.03496/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://mlsanity.com/paper/2407.01523/) (2024)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment](https://mlsanity.com/paper/2502.06472/) (2025)
- [AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://mlsanity.com/paper/2505.23628/) (2025)
- [Entity Alignment with Noisy Annotations from Large Language Models](https://mlsanity.com/paper/2405.16806/) (2024)
- [LLM-empowered knowledge graph construction: A survey](https://mlsanity.com/paper/2510.20345/) (2025)
- [LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://mlsanity.com/paper/2408.13727/) (2024)
- [Research Trends for the Interplay between Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2406.08223/) (2024)
- [EntGPT: Entity Linking with Generative Large Language Models](https://mlsanity.com/paper/2402.06738/) (2024)
- [Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation](https://mlsanity.com/paper/2412.00608/) (2024)
- [Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation](https://mlsanity.com/paper/2505.02737/) (2025)
- [LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing](https://mlsanity.com/paper/2505.24163/) (2025)
- [Context Graph](https://mlsanity.com/paper/2406.11160/) (2024)

## Abstract (arXiv)

Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose TRACE-KG (Text-dRiven schemA for Context-Enriched Knowledge Graphs), a framework that jointly constructs a context-enriched knowledge graph and an induced schema without assuming a predefined ontology. TRACE-KG captures conditional relations through structured qualifiers and organizes entities and relations using a data-driven schema that serves as a reusable semantic scaffold while preserving full traceability to the source evidence. Experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs and offers a practical alternative to both ontology-driven and schema-free construction pipelines.