Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

This paper describes the publication and linking of (parts of) PAROLE SIMPLE CLIPS (PSC), a large scale Italian lexicon, to the Semantic Web and the Linked Data cloud using the lemon model. The main challenge of the conversion is discussed, namely th

This paper describes an heuristic-based approach to enhance existing WordNets with freely available bilingual resources. The approach has been applied to the Arabic WordNet using the AraMorph bilingual dictionary as bilingual resource, but its guidelines are quite general to be effectively applied to other languages. The English words extracted from the bilingual resource are checked against Princeton WordNet in order to quantify their coverage and to select only those words which share the same set of synsets. This strongly reduces the number of Arabic words of the pairs. These latter are then checked against the Arabic WordNet to make new words emerge and - possibly - add new synonyms.