Doshi Prashant, Kolli Ravikanth, Thomas Christopher
LSDIS Lab, Dept. of Computer Science, University of Georgia, Athens, GA 30602,
Web Semant. 2009 Apr 1;7(2):90-106. doi: 10.1016/j.websem.2008.12.001.
We present a new method for mapping ontology schemas that address similar domains. The problem of ontology matching is crucial since we are witnessing a decentralized development and publication of ontological data. We formulate the problem of inferring a match between two ontologies as a maximum likelihood problem, and solve it using the technique of expectation-maximization (EM). Specifically, we adopt directed graphs as our model for ontology schemas and use a generalized version of EM to arrive at a map between the nodes of the graphs. We exploit the structural, lexical and instance similarity between the graphs, and differ from the previous approaches in the way we utilize them to arrive at, a possibly inexact, match. Inexact matching is the process of finding a best possible match between the two graphs when exact matching is not possible or is computationally difficult. In order to scale the method to large ontologies, we identify the computational bottlenecks and adapt the generalized EM by using a memory bounded partitioning scheme. We provide comparative experimental results in support of our method on two well-known ontology alignment benchmarks and discuss their implications.
我们提出了一种用于映射涉及相似领域的本体模式的新方法。本体匹配问题至关重要，因为我们正目睹本体数据的分散式开发和发布。我们将推断两个本体之间匹配关系的问题表述为最大似然问题，并使用期望最大化（EM）技术来解决它。具体而言，我们采用有向图作为本体模式的模型，并使用EM的广义版本来得出图节点之间的映射。我们利用图之间的结构、词汇和实例相似性，并且与先前的方法不同之处在于我们利用这些相似性的方式，以得出一个可能不精确的匹配。不精确匹配是在无法进行精确匹配或精确匹配计算困难时，在两个图之间找到最佳可能匹配的过程。为了将该方法扩展到大型本体，我们识别出计算瓶颈，并通过使用内存受限的分区方案来调整广义EM。我们在两个著名的本体对齐基准上提供了支持我们方法的对比实验结果，并讨论了它们的意义。
