  The goal of case-based retrieval is to assist physicians in the clinical
decision making process, by finding relevant medical literature in large
archives. We propose a research that aims at improving the effectiveness of
case-based retrieval systems through the use of automatically created
document-level semantic networks. The proposed research tackles different
aspects of information systems and leverages the recent advancements in
information extraction and relational learning to revisit and advance the core
ideas of concept-centered hypertext models. We propose a two-step methodology
that in the first step addresses the automatic creation of document-level
semantic networks, then in the second step it designs methods that exploit such
document representations to retrieve relevant cases from medical literature.
For the automatic creation of documents' semantic networks, we design a
combination of information extraction techniques and relational learning
models. Mining concepts and relations from text, information extraction
techniques represent the core of the document-level semantic networks' building
process. On the other hand, relational learning models have the task of
enriching the graph with additional connections that have not been detected by
information extraction algorithms and strengthening the confidence score of
extracted relations. For the retrieval of relevant medical literature, we
investigate methods that are capable of comparing the documents' semantic
networks in terms of structure and semantics. The automatic extraction of
semantic relations from documents, and their centrality in the creation of the
documents' semantic networks, represent our attempt to go one step further than
previous graph-based approaches.
