Multi-way Association Extraction from Biological Text Documents Using Hyper-Graphs

There has been a considerable amount of recent research in extraction of various kinds of binary associations (e.g., gene-gene, gene-protein, protein-protein, etc) using different text mining approaches. However, an important aspect of such associations is identifying the context in which such associations occur (e.g., "gene A activates protein B in the context of disease C in organ D under the influence of chemical E"). Such contexts can be represented appropriately by a multi-way relationship involving more than two objects rather than usual binary relationships. Such multi-way relations naturally lead to a hyper-graph representation of the knowledge. The hyper-graph based knowledge extraction from biological literature represents a computationally difficult problem due to its combinatorial nature. In this paper, we compare two different approaches to such hyper-graph extraction: one based on an exhaustive enumeration of all hyper-edges and the other based on an extension of the well-known A Priori algorithm.