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- W2167732602 abstract "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." @default.
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- W2167732602 date "2008-01-01" @default.
- W2167732602 modified "2023-09-26" @default.
- W2167732602 title "Multi-way Association Extraction from Biological Text Documents Using Hyper-Graphs" @default.
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- W2167732602 doi "https://doi.org/10.1109/bibm.2008.10" @default.
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