Robust knowledge extraction over large text collections

Automatic knowledge extraction over large text collections has been a challenging task due to many constraints such as needs of large annotated training data, requirement of extensive manual processing of data, and huge amount of domain-specific terms. In order to address these constraints, this study proposes and develops a complete solution for extracting knowledge from large text collections with minimum human intervention. As a testbed system, a novel robust and quality knowledge extraction system, called RIKE (Robust Iterative Knowledge Extraction), has been developed. RIKE consists of two major components: DocSpotter and HiMMIE. DocSpotter queries and retrieves promising documents for extraction. HiMMIE extracts target entities based on a Mixture Hidden Markov Model from the selected documents from DocSpotter. The following three research questions are examined to evaluate RIKE: 1) How accurately does RIKE retrieve the promising documents for information extraction from huge text collections such as MEDLINE or TREC? 2) Does ontology enhance extraction accuracy of RIKE in retrieving the promising documents? 3) How well does RIKE extract the target entities from a huge medical text collection, MEDLINE? The major contributions of this study are1) an automatic unsupervised query generation for effective retrieval from text databases is proposed and evaluated, 2) Mixture Hidden Markov models for automatic instances extraction are proposed and tested, 3) Three Ontology-driven query expansion algorithms are proposed and evaluated, and 4) Object-oriented methodologies for knowledge extraction system are adopted. Through extensive experiments, RIKE is proved to be a robust and quality knowledge extraction technique. DocSpotter outperforms other leading techniques for retrieving promising documents for extraction from 15.5% to 35.34% in P@20. HiMMIE improves extraction accuracy from 9.43% to 24.67% in F-measures.