Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining
- 作者：Muhammad Abulaish ; Lipika Dey
- 关键词：Text mining ; Ontology ; Biological relation extraction ; Biological query processing
- 刊名：Data and Knowledge Engineering
- 出版年：2007
- 期刊代码：44_0169023x
- 类别：cp
- 出版时间：May 2007
- 卷：61
- 期：2
- 页码：228-262
- 文件大小：2613 K
摘要
The rapid growth of the biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a biological ontology, and thereafter mines possible biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring biological relations among a pair of biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy biological relation. The fuzzy biological relations extracted from a text corpus along with other relevant information components like biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate biological queries at different levels of specificity.