Obvio
Obvio (spanish for obvious) is the name of the project on semantics-based techniques for Literature-Based Discovery (LBD) using Biomedical Literature. The goal of Obvio is to uncover hidden connections between concepts in text, thereby leading to hypothesis generation from publicly available scientific knowledge sources.
Contents
Overview
Obvio is driven by assertions extracted from structured text (called semantic predications) as well as assertions obtained from structured knowledge sources (such as the UMLS).
Project Team |
Graduate Students: Delroy Cameron, Tu Danh, Sreeram Vallabhaneni, Hima Yalamanchili |
Application
Question Answering
Reachability
One application of semantic predications is in the field on biomedical Question Answering(QA). The QA task put forth by the Text REtrieval Conferences (TREC) offer an opportunity to determine whether semantic predications can yielded relevant information given complex information needs.
Literature-based Discovery (LBD)
Swanson's Hypotheses
Much of the early research focused almost entirely on Information Retrieval (IR) techniques, such as term and concept co-occurrence. Only recently has significant attention been devoted to semantics-based techniques that exploit the meaning of associations between concepts. While generally more intuitive, the feasibility of such semantics-based approaches has not been fully established. It is reasonable to expect that if semantics-based techniques are adequate for discovering new knowledge, they ought to be sufficient for recovering existing knowledge. In this work we investigate the applicability of semantics-based techniques for recovering and decomposing Swanson's Raynaud Syndrome--Fish Oil hypothesis using semantic predications, background knowledge and graph-based algorithms for path extraction and subgraph creation.
RS-DFO Hypothesis
Below is a presentation, and various datasets and experimental results for download and reuse
Datasets and Experimental Results
- Dataset
- Baseline (B1)
- Original PDFs of the 65 articles cited by Swanson's RS-DFO paper (30.5MB)
- ASCII text with end-of-line text wrapping fixed
- Text in Medline format for parsing by SemRep
- SemRep Relations Output
- SemRep Relations Output (vascular reactivity)
- SemRep Extracted Predications
- Manually Identified Predications (vascular reactivity)
- Baseline (B2)
- Baseline (B1)
- Experimental Results
Publications
- D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan, Semantic Predications for Complex Information Needs in Biomedical Literature, 5th International Conference on Bioinformatics and Biomedicine BIBM2011, Atlanta GA, November 12-15, 2011 (acceptance rate=19.4%)