Open Access

ARTICLE

Dual-Perspective Evaluation of Knowledge Graphs for Graph-to-Text Generation

Haotong Wang^#,*, Liyan Wang^#, Yves Lepage

Graduate School of Information, Production and Systems, Waseda University, Fukuoka, 808-0135, Japan

* Corresponding Author: Haotong Wang. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2025, 84(1), 305-324. https://doi.org/10.32604/cmc.2025.066351

Received 28 March 2025; Accepted 28 April 2025; Issue published 09 June 2025

Abstract

Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs (KGs) remains challenging. Prior research exhibits a narrow focus on structural statistics, such as the shortest path length, while the correctness of graphs in representing the associated text is rarely explored. To address this gap, we introduce a dual-perspective evaluation framework for KG-text data, based on the computation of structural adequacy and semantic alignment. From a structural perspective, we propose the Weighted Incremental Edge Method (WIEM) to quantify graph completeness by leveraging agreement between relation models to predict possible edges between entities. WIEM targets to find increments from models on “unseen links”, whose presence is inversely proportional to the structural adequacy of the original KG in representing the text. From a semantic perspective, we evaluate how well a KG aligns with the text in capturing the intended meaning. To do so, we instruct a large language model to convert KGs into natural language and measure the similarity between generated and reference texts. Based on these computations, we apply a Top-K union method, integrating the structural and semantic modules, to rank and select high-quality KGs. We evaluate our framework against various approaches for selecting few-shot examples in graph-to-text generation. Experiments on the Association for Computational Linguistics Abstract Graph Dataset (ACL-AGD) and Automatic Content Extraction 05 (ACE05) dataset demonstrate the effectiveness of our approach in distinguishing KG-text data of different qualities, evidenced by the largest performance gap between top- and bottom-ranked examples. We also find that the top examples selected through our dual-perspective framework consistently yield better performance than those selected by traditional measures. These results highlight the importance of data curation in improving graph-to-text generation.

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