Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

Yulan He, Keiichi Nakata, Deyu Zhou

Research output: Chapter in Book/Published conference outputOther chapter contribution


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.
Original languageEnglish
Title of host publication2008 IEEE international conference on data mining workshops
Number of pages8
ISBN (Electronic)978-0-7695-3503-6
Publication statusPublished - 1 Jan 2008
EventIEEE international conference on data mining workshops - Pisa, Italy
Duration: 15 Dec 200819 Dec 2008


ConferenceIEEE international conference on data mining workshops
Abbreviated titleICDMW '08

Bibliographical note

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  • hidden vector state model
  • PPI ontology
  • protein-protein interactions extraction
  • information extraction


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