Automatic semantic modeling of structured data sources with cross-modal retrieval

文献类型： 外文期刊

第一作者： Xu, Ruiqing

作者： Xu, Ruiqing;Chu, Hailong;Zhang, Yitao;Zhang, Hong-Yu;Wang, Yulong;Liu, Youfa;Feng, Zaiwen;Mayer, Wolfgang;Zhang, Hong-Yu;Feng, Zaiwen;Feng, Zaiwen;Feng, Zaiwen;Feng, Zaiwen;Feng, Zaiwen;Feng, Zaiwen

作者机构：

关键词： Semantic model; Ontology; Cross-modal retrieval; Attention mechanism; Graph representation learning

期刊名称：PATTERN RECOGNITION LETTERS （ 影响因子：5.1； 五年影响因子：4.8 ）

ISSN： 0167-8655

年卷期： 2024 年 177 卷

页码：

收录情况： SCI

摘要： Analyzing and modeling the implicit semantic relationships in data sources is the key to achieving integration and sharing of heterogeneous data information. However, manual modeling of data semantics is a laborious and error-prone task that demands significant human effort and expertise. The paper proposes a novel explainable representation learning-based method that adopts an attention-based table-graph cross-modal retrieval model as a rating function during incremental search for automatic semantic modeling. Our supervised model utilizes the graph representation learning technique to extract latent semantics from data and aims to retrieve the most reliable semantic model for structured data sources. Experimental results demonstrate the effectiveness and robustness of our method.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序