Generating Semantic Networks to the PubMed Giovani Rubert Librelotto 1 , Mirkos Ortiz Martins 1 , Henrique Tamiosso Machado 1 , Juliana Kaizer Vizzotto 1 , Jos´ e Carlos Ramalho 2 , and Pedro Rangel Henriques 2 1 UNIFRA, Centro Universit´ario Franciscano, Santa Maria - RS, 97010-032, Brasil {librelotto, mirkos, htmachado, juvizzotto}@gmail.com 2 Universidade do Minho, Departamento de Inform´atica 4710-057, Braga, Portugal {jcr, prh}@di.uminho.pt Abstract. This paper presents a topic map approach to PubMed in order to create a knowledge representation for this information system. PubMed is a free search engine that gives very full coverage of the re- lated biomedical sciences. With more than 17 millions of citations since 1865, PubMed users have several problems to find the papers desired. So, it is necessary to organize these concepts in a semantic network. To achieve this objective, we use the Metamorphosis system, choosing the keywords from MeSH ontology. This way, we obtain an ontological index for PubMed, making easier to find specific papers. 1 Introduction Daily, a lot of data is stored into PubMed system. There is a problem that orga- nization requires an integrated view of their heterogeneous information systems. In this situation, there is a need for an approach that extracts the information from their data sources and fuses it in a semantically network. Usually this is achieved either by extracting data and loading it into a central repository that does the integration before analysis, or by merging the information extracted separately from each resource into a central knowledge base. Topic maps are an ISO standard for the representation and interchange of knowl- edge, with an emphasis on the findability of information. A topic map can rep- resent information using topics (representing any concept), associations (which represent the relationships between them), and occurrences (which represent re- lationships between topics and information resources relevant to them). They are thus similar to semantic networks and both concept and mind maps in many respects. According to Topic Map Data Model (TMDM) [GM05], Topic Maps are abstract structures that can encode knowledge and connect this encoded knowl- edge to relevant information resources. In order to cope with a broad range of scenarios, a topic is a very wide concept. This makes Topic Maps a convenient model for knowledge representation. This paper described the integration of data from PubMed information system using the ontology paradigm, in order to generate an homogeneous view of those