Combining Semantics, Context, and Statistical Evidence in Genomics Literature Search

—We present an information retrieval model for combining evidence from concept-based semantics, term statistics, and context for improving search precision of genomics literature by accurately identifying concise, variable length passages of text to answer a user query. The system combines a dimensional data model for indexing scientific literature at multiple levels of document structure and context with a rule-based query processing algorithm. The query processing algorithm uses an iterative information extraction technique to identify query concepts, and a retrieval function for systematically combining concepts with term statistics at multiple levels of context. We define context by variable length passages of text and different levels of document lexical structure including terms, sentences, paragraphs, and entire documents. Our results demonstrate improved search results in the presence of varying levels of semantic evidence, and higher performance using retrieval functions tha...

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Jay Urbain, Nazli Goharian, Ophir Frieder
• Keywords: BIBE 2007, Bioinformatics, Query Processing Algorithm, Retrieval Functions, Variable Length Passages