Abstract: At present the insufficient formal business knowledge participation in the process of On-Line Analytical Processing (OLAP) results in restriction and limitation to in-depth analysis. To overcome the limitations, a new approach for building an OLAP system was proposed based on domain ontology. Firstly, by analyzing the limitations of the existing ontology construction methods and using the similarity evaluation algorithm based on multiple features and weighted pattern of entity classes, a semi-automatic domain ontology construction method in which global top-level domain ontology was designed by experts after local ontologies were generated from databases was put forward to implement formalized description of mine-production domain knowledge. And then the key indicators of mine-production capacity were chosen as measurements and Multi-Dimensional Ontology (MDO) with business semantic concepts was built. Finally, the method was tested by a practical project of metal mine decision making system. The experimental results show that the proposed method can dynamically integrate heterogeneous information resource of mine production process and facilitate the unambiguous interpretation of query results, and discover association rules and implicit knowledge through the advantages of formalization expression and reasoning of domain ontology. Meanwhile, by high frequency and general concept views, it avoids query duplication and improves the performance of traditional OLAP systems.

Key words: On-Line Analytical Processing (OLAP), domain ontology, semantic knowledge, Multi-Dimensional Ontology (MDO), mine production

摘要： 传统联机分析处理(OLAP)系统存在着形式化业务知识参与不足的状况,对深度推理分析造成了制约和局限。为了克服上述缺点,提出一种领域本体驱动的OLAP系统构建方法。首先,通过分析现有本体构建方法的局限性,依托实体类多特征加权相似度判断算法,提出先全局设计后局部抽取的半自动本体构建模式,实现矿山生产领域知识形式化;接着在此基础上,以矿山生产能力关键指标为度量,完成负载业务概念多维本体(MDO)建模;最后,在实际矿山决策系统项目建设中,进行了方法检验。实验结果表明,该方法充分发挥领域本体形式化表达与推理优势,有效整合多源异构信息和明晰多维分析过程,实现内隐知识与关联规则深度挖掘;同时借助高频通用概念视图定义,避免重复维度建模,改进了传统OLAP效能。

关键词: 联机分析处理, 领域本体, 语义知识, 多维本体, 矿山生产