This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.
|Title of host publication||2008 IEEE international conference on data mining workshops|
|Number of pages||8|
|Publication status||Published - 1 Jan 2008|
|Event||IEEE international conference on data mining workshops - Pisa, Italy|
Duration: 15 Dec 2008 → 19 Dec 2008
|Conference||IEEE international conference on data mining workshops|
|Abbreviated title||ICDMW '08|
|Period||15/12/08 → 19/12/08|
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- hidden vector state model
- PPI ontology
- protein-protein interactions extraction
- information extraction