When ontology description data by different authors would become widespread in the world, we will be faced with the difficulties of the ontology alignment (OA) problem required for integration and interoperability of ontologies. The OA problem is the problem to find couples of semantically same classes / properties between two ontologies, and includes points of different naming of classes / properties, polysemous naming of classes / properties, different granularity of classes / properties, different hierarchical structures, and so on. We applied our semantic category matching (SCM) tool to the ontology alignment problems. Our approach found pairs of semantically corresponding categories from two different classification hierarchies such as Yahoo directory or library classification as UDC or NDC, based on natural language processing, similarity searching of huge vector spaces, and structural consistency analysis. We tackled problems of the EON2004 Ontology Alignment Contest. For examples, the Contest's random name problems (#201, #202) could not be solved using conventional character string resemblance techniques. However, when we applied SCM to these problems, the results showed that SCM had improved the accuracy as compared to the conventional method (F-measure: 0.021=>0.949, 0.021=>0.580), and exceeded the accuracy average in all problem areas by over 10 % as compared to conventional methods. Our team participated as a competitor in EON OA Contest and could obtain satisfactory results.