In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.

This extended abstract presents a work in progress of using terminological resources from the biomedical domain to systematically study the change of domain terminology over time. In particular we investigate term replacement. In order to study term replacement over time, semantic knowledge like conceptual granularity of a term is necessary. We analyze three popular biomedical terminology resources (UMLS, CTD, SNOMED CT) and show how information provided there can be used to extract lexically distinctive synonym sets that exclude variants. We use the entire PubMed dataset to chronologically study occurrences of extracted synonyms. Our experiments on the disease subsets of three terminologies reveal that the phenomenon of term replacement can be observed in around 60% of the extracted synonym sets.

Scientists typically need to take a large volume of information into account in order to deal with re-occurring tasks such as inspecting proceedings, finding related work, or reviewing papers. Our work aims at filling the gap between text documents and a structured representations of their content in the domain of resilience computing by combining computer linguistics and ontological methods. The results of our research include: a thesaurus of the domain, automatic clustering of the domain documents, a domain ontology, and a tool for constructing ontologies with the aid of domain thesauri.