Abstract: We have developed an automated linking scheme for PUBMED citations with GO terms using SVM (Support Vector Machine), a classifica tion algorithm. The PUBMED database has been essential to life science re searchers with over 12 million citations. More recently GO (Gene Ontology) has provided a graph structure for biological process, cellular component, and molecular function of genomic data. By text mining the textual content of PUBMED and associating them with GO terms, we have built up an ontological map for these databases so that users can search PUBMED via GO terms and conversely GO entries via PUBMED classification. Conse quently, some interesting and unexpected knowledge may be captured from them for further data analysis and biological experimentation. This paper reports our results on SVM implementation and the need to parallelize for the training phase.