One of the main challenges in ontology matching is to match ontologies with high accuracy. Therefore, ontology matching systems typically use multiple basic matchers, each targeting a specific ontology component for the matching process. However, optimizing the combination of these matchers remains an open problem. In this paper, we present CroMatcher 2.0, an improved ontology matching system that aims to overcome these challenges. We introduce two new basic matchers. The first matcher determines correspondence between entities by comparing strings obtained from entity IDs and annotations using the English lexical database to find similarities between tokens of the compared strings, considering their mutual relations (synonyms, hypernyms, etc.). The second matcher determines the correspondence between entities using the special mediator ontology, which is very valuable for ontology matching as it can contain additional information about the compared ontologies. In this paper, we tested this matcher on the Ontology Alignment Evaluation Initiative Anatomy track by using the Uberon mediator ontology, which contains a lot of information about anatomical structures. We also introduce a new weighted aggregation method (Autoweight 3.0) that automatically determines the weighting factors of the basic matchers in the parallel composition. CroMatcher 2.0 was evaluated in three test cases of the Ontology Alignment Evaluation Initiative (Benchmark, Anatomy, and Conference) and showed competitive performance compared to other state-of-the-art systems. The results position CroMatcher 2.0 among the best ontology matching systems for these datasets and confirm the effectiveness of the newly introduced methods.