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


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.

Publication DOI: https://doi.org/10.1109/ICDMW.2008.11
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Event Title: IEEE international conference on data mining workshops
Event Type: Other
Event Dates: 2008-12-15 - 2008-12-19
Uncontrolled Keywords: hidden vector state model,PPI ontology,protein-protein interactions extraction,information extraction
ISBN: 978-0-7695-3503-6
Last Modified: 24 Apr 2024 07:27
Date Deposited: 24 Jan 2013 14:24
Full Text Link: http://ieeexplo ... rnumber=4734001
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Other chapter contribution
Published Date: 2008-01-01
Authors: He, Yulan (ORCID Profile 0000-0003-3948-5845)
Nakata, Keiichi
Zhou, Deyu



Version: Accepted Version

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