多关系频繁模式发现能够直接从复杂结构化数据中发现涉及多个关系的复杂频繁模式，避免了传统方法的局限。有别于主流基于归纳逻辑程序设计技术的方法，提出了基于合取查询包含关系的面向语义的精简化多关系频繁模式发现方法，具有理论与技术基础的新颖性，解决了两种语义冗余问题。实验表明，该方法在可理解性、功能、效率以及可扩展性方面具有优势。

Multi-relational data mining is one of rapidly developing subfields of data mining. Multi-relational frequent pattern discovery approaches directly look for frequent patterns that involve multiple relations from a relational database. While the state-of-the-art of multi-relational frequent pattern discovery approaches is based on the inductive logical programming techniques, we propose an approach to semantically condensed multi-relational frequent pattern discovery based on conjunctive query containment in terms of the theory and technique of relational database. With the novelty of the groundwork, the proposed approach deals with two kinds of semantically redundant problems. In theory and experiments, it shows that our approach improve the understandability, function, efficiency and scalability of the state-of-the-art of multi-relational frequent pattern discovery approaches.

杨炳儒（1943－），男，天津市人，北京科技大学教授，博士生导师，主要研究方向为知识发现与推理机制、柔性建模与集成技术