In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge are used in the field of semantic retrieval and machine translation. The field of discovery and information systems can also be integrated with ontology learning techniques. Among several ontology learning tricks, multi-dividing ontology learning is the most popular one which proved to be in high efficiency for the similarity calculation of tree structure ontology. In this work, we study the multi-dividing ontology learning algorithm from the mathematical point of view, and an approximation conclusion is presented under the linear representation assumption. The theoretical result obtained here has constructive meaning for the similarity calculation and concrete engineering applications of tree-shaped ontologies. Finally, linear combination multi-dividing ontology learning is applied to university ontologies and mathematical ontologies, and the experimental results imply that the higher efficiency of the proposed approach in actual applications.

中文翻译：

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线性组合环境下本体学习算法的逼近分析

在过去的十年中，研究人员一直非常重视本体在其相关特定领域的应用。同时，将学习算法应用于许多本体算法也是一个热门话题。例如，本体学习技术和知识被用于语义检索和机器翻译领域。发现和信息系统领域也可以与本体学习技术集成在一起。在几种本体学习技巧中，多重划分本体学习是最流行的一种，被证明在树结构本体的相似度计算中效率很高。在这项工作中，我们从数学的角度研究了多元本体学习算法，并在线性表示假设下给出了近似结论。这里获得的理论结果对于树形本体的相似度计算和具体工程应用具有建设性意义。最后，将线性组合多划分本体学习应用于大学本体和数学本体，实验结果表明，该方法在实际应用中具有较高的效率。