BK-based matching exploits external background knowledge resources (BK) to fill the semantic gap between the ontologies to align. Existing BK-based matchers implement the indirect matching approach in their internal architecture, which makes any adaptation or reuse of the code difficult. Indeed, to improve a particular step in the BK-based matching process, it is necessary to code the whole process from scratch which requires a lot of time and effort. To overcome this issue, we propose a flexible framework called Generic BK-based Matcher (GBKOM). GBKOM is an extension that can be added to any existing matcher. It is a configurable framework that implements the BK-based matching process, with a rich set of parameters making it customizable and suitable for performing experimental evaluations.

GBKOM has participated, with YAM++ as a direct matcher, in the OAEI 2017 and OAEI 2017.5 campaigns, where it has been successful on the biomedical benchmarks, and top-ranked in several tasks. Furthermore, we have performed experiments with two other direct matchers (i.e., LogMap and LogMapLite) to show that the effectiveness of GBKOM is independent of the direct matcher used.

基于BK的匹配利用外部背景知识资源（BK）来填补要对齐的本体之间的语义鸿沟。现有的基于BK的匹配器在其内部体系结构中实现了间接匹配方法，这使得对代码的任何改编或重用都变得困难。实际上，为了改进基于BK的匹配过程中的特定步骤，有必要从头开始对整个过程进行编码，这需要大量的时间和精力。为了克服这个问题，我们提出了一个灵活的框架，称为基于BK的通用Matcher（GBKOM）。GBKOM是可以添加到任何现有匹配器的扩展。它是一个可配置的框架，可实现基于BK的匹配过程，并具有丰富的参数集，使其可自定义并适合进行实验评估。

GBKOM以YAM ++作为直接匹配者参加了OAEI 2017和OAEI 2017.5活动，在生物医学基准测试中取得了成功，并在多项任务中名列前茅。此外，我们已经使用其他两个直接匹配器（即LogMap和LogMapLite）进行了实验，以表明GBKOM的有效性与所使用的直接匹配器无关。