Entity matching is a critical task in integrating and linking entities across different Knowledge Graphs (KGs). Each entity matching task involves a pair of KGs, and the nature of these KGs, such as their size, schema, data quality, and domain, can categorize them into different Linking Problem Types (LPTs). Selecting the most appropriate matcher for different types of LPTs can substantially enhance the accuracy and effectiveness of entity matching. This research aims to empirically evaluate matchers for each LPT and develop a framework to systematically associate matchers with specific LPTs, enhancing both accuracy and efficiency in the entity matching process.