The “古今釋林” (Gugjin Seoknim) compiled by the Korean scholar Yi Yifeng (1732-1801) is a comprehensive and systematic compilation of various language dictionaries from the pre-19th century Joseon period that recorded Hanja (Chinese character) vocabulary. This analysis focuses on the entries found in “古今 釋林·東韓譯語·釋親”, which includes words related to Hanja characters. These entries consist of words that have the same literal meaning as their Hanja characters, such as “阿父, 别室”, words that borrow Hanja characters’ phonetics for annotation, like “阿兒, 南人”, and words that borrow Hanja characters’ forms and meanings to create vocabulary expressing native Korean culture, like “兩主, 冷背拜”. This paper investigates the phonetic Hanja character words borrowed for annotation in the Korean language and combines them with phonetic analysis, revealing the transitional phase of using Hanja for phonetic representation and the coexistence of Hanja and the Korean language during that time. The adaptation of Hanja characters’ forms and meanings to create Korean terms enriches our understanding of the Hanja vocabulary created during the Joseon period. By comparing and analyzing the similarities and differences in the meanings and definitions of Hanja characters in both Korean and Chinese, this study offers a deeper and more comprehensive analysis of the diversity of Hanja’s usage during the Joseon period and its impact on modern Chinese and modern Korean.

This paper considers a paternity and kinship analysis system(PKAS) being currently used in real sites. A knowledge-based expert system is proposed to improve the performance of PKAS in terms of accuracy, speed, training time, and satisfaction, which are common measures for evaluation. The knowledge base, one of the most important components in the knowledge-based expert system(KBES), consists of a rule made from random matching algorithm, decision rules of allele types and guide rules of options. The last two rules are learning incrementally from sample data. The results show that PKAS armed with the expert system ensures the better performance with regard to these criteria than the existing system. Especially as far as speed is concerned, as the sample size increases, it outperforms the existing one. As the number of samples increases, while processing time increases nearly exponentially in the existing PKAS, it does linearly in our proposed system.