Passage relevance models for genomics search

ABSTRACT

We present a passage relevance model for integrating semantic and statistical evidence of biomedical concepts and topics in context using the framework of a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field, and the probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

REFERENCES

• 1: Hersh W., et al. (2007). TREC 2007 Genomics Track Overview. The Sixteenth Text REtrieval Conference Proceedings.
• 2: Steyvers, M. (2006). Probabilistic Topic Models. In Landauer, T., McNamara, D., Dennis, S., & Kintch W. (eds), Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum.
• 3: Urbain, J., Goharian, N., & Frieder, O. (2007, November). IIT TREC 2007 Genomics Track: Using Concept-Based Semantics in Context for Genomics Literature Passage Retrieval. The Sixteenth Text REtrieval Conference (TREC 2007) Conference Proceedings.
• 4: Jay Urbain , Nazli Goharian , Ophir Frieder, Probabilistic passage models for semantic search of genomics literature, Journal of the American Society for Information Science and Technology, v.59 n.12, p.2008-2023, October 2008 [doi>10.1002/asi.v59:12]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Experimentation, Theory

Keywords:
dimensional modeling, genomics search, graphical models, information retrieval, passage retrieval, relevance modeling, semantic search