@inproceedings{zeinalipour-etal-2026-graph,
title = "From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs",
author = "Zeinalipour, Kamyar and
Severini, Silvia and
Borghini, Alessia and
Cardarelli, Sara and
Maggini, Marco and
Gori, Marco",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.140/",
pages = "2095--2111",
ISBN = "979-8-89176-394-4",
abstract = "Knowledge Graphs (KGs) are the backbone of reliable industrial data strategies, yet verbalizing them with Large Language Models (LLMs) often leads to unacceptable risks for high-stakes applications, such as hallucinations or omitted relations. To enforce strict semantic fidelity in KG-to-text generation, we introduce a self-supervised round-trip pipeline. The system verbalizes KG triples into text and immediately attempts to reconstruct the original graph from that text; only verbalizations that enable perfect graph recovery are retained. This creates a closed feedback loop that guarantees the generated text is semantically equivalent to the source data. Experiments confirm that our automated round-trip consistency score correlates strongly with expert judgment, effectively acting as a scalable proxy for human review. Furthermore, we show that standard LLMs can bootstrap their own KG-extraction and generation capabilities by fine-tuning on this trusted synthetic data. Our approach yields significant improvements in triple-extraction accuracy and verbalization faithfulness without relying on costly manual annotation or massive teacher models, offering a practical path to deploying trustworthy, KG-grounded AI systems."
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<abstract>Knowledge Graphs (KGs) are the backbone of reliable industrial data strategies, yet verbalizing them with Large Language Models (LLMs) often leads to unacceptable risks for high-stakes applications, such as hallucinations or omitted relations. To enforce strict semantic fidelity in KG-to-text generation, we introduce a self-supervised round-trip pipeline. The system verbalizes KG triples into text and immediately attempts to reconstruct the original graph from that text; only verbalizations that enable perfect graph recovery are retained. This creates a closed feedback loop that guarantees the generated text is semantically equivalent to the source data. Experiments confirm that our automated round-trip consistency score correlates strongly with expert judgment, effectively acting as a scalable proxy for human review. Furthermore, we show that standard LLMs can bootstrap their own KG-extraction and generation capabilities by fine-tuning on this trusted synthetic data. Our approach yields significant improvements in triple-extraction accuracy and verbalization faithfulness without relying on costly manual annotation or massive teacher models, offering a practical path to deploying trustworthy, KG-grounded AI systems.</abstract>
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%0 Conference Proceedings
%T From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs
%A Zeinalipour, Kamyar
%A Severini, Silvia
%A Borghini, Alessia
%A Cardarelli, Sara
%A Maggini, Marco
%A Gori, Marco
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zeinalipour-etal-2026-graph
%X Knowledge Graphs (KGs) are the backbone of reliable industrial data strategies, yet verbalizing them with Large Language Models (LLMs) often leads to unacceptable risks for high-stakes applications, such as hallucinations or omitted relations. To enforce strict semantic fidelity in KG-to-text generation, we introduce a self-supervised round-trip pipeline. The system verbalizes KG triples into text and immediately attempts to reconstruct the original graph from that text; only verbalizations that enable perfect graph recovery are retained. This creates a closed feedback loop that guarantees the generated text is semantically equivalent to the source data. Experiments confirm that our automated round-trip consistency score correlates strongly with expert judgment, effectively acting as a scalable proxy for human review. Furthermore, we show that standard LLMs can bootstrap their own KG-extraction and generation capabilities by fine-tuning on this trusted synthetic data. Our approach yields significant improvements in triple-extraction accuracy and verbalization faithfulness without relying on costly manual annotation or massive teacher models, offering a practical path to deploying trustworthy, KG-grounded AI systems.
%U https://aclanthology.org/2026.acl-industry.140/
%P 2095-2111
Markdown (Informal)
[From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs](https://aclanthology.org/2026.acl-industry.140/) (Zeinalipour et al., ACL 2026)
ACL