title: A multi-level text mining method to extract biological relationships.
abstract: Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For a corpus of thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81%. The approach is both adaptable and scalable to new problems as opposed to rule-based methods.
authors.affiliation: Department of Computer and Information Science, Indiana University Purdue University Indianapolis, Indianapolis, IN, 46202, USA. mpalakal@cs.iupui.edu
authors.forename: Mathew
authors.initials: M
authors.lastname: Palakal
authors.forename: Matthew
authors.initials: M
authors.lastname: Stephens
authors.forename: Snehasis
authors.initials: S
authors.lastname: Mukhopadhyay
authors.forename: Rajeev
authors.initials: R
authors.lastname: Raje
authors.forename: Simon
authors.initials: S
authors.lastname: Rhodes
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D020869:Gene Expression Profiling / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D049490:Systems Biology / Q000379:methods*; D018875:Vocabulary, Controlled