Difference between revisions of "Obvio"
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### [http://knoesis.wright.edu/researchers/delroy/projects/swanson-rfo/swanson-rfo-semrep-relations-baseline-1.zip SemRep Relations Output] | ### [http://knoesis.wright.edu/researchers/delroy/projects/swanson-rfo/swanson-rfo-semrep-relations-baseline-1.zip SemRep Relations Output] | ||
### [http://knoesis.wright.edu/researchers/delroy/projects/swanson-rfo/swanson-rfo-semrep-preds-baseline-1.zip SemRep Extracted Predications] | ### [http://knoesis.wright.edu/researchers/delroy/projects/swanson-rfo/swanson-rfo-semrep-preds-baseline-1.zip SemRep Extracted Predications] | ||
+ | ### [http://knoesis.wright.edu/researchers/delroy/projects/swanson-rfo/swanson-rfo-subgraphs.pptx Generated Subgraphs] | ||
## Baseline (B2) <br /> | ## Baseline (B2) <br /> | ||
# Experimental Results | # Experimental Results | ||
− | ## Experiment I ( | + | ## Experiment I (Subgraphs) |
− | ## Experiment II ( | + | |
+ | ## Experiment II (Subgraphs) | ||
## Stand-alone Subgraphs | ## Stand-alone Subgraphs | ||
# Vascular Reactivity Dataset | # Vascular Reactivity Dataset |
Revision as of 21:37, 21 February 2012
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
Now that techniques are available for predication extraction, automating Swanson's hypothesis generation using the MEDLINE corpus becomes particularly appealing. In particular, the extent of combinatorial explosion presents a major limitation in using the predications to recover the original paths suggested by Swanson. At the same time however, many alternative and potentially interesting connects may also exist, not limited to the length and types suggested by Swanson. In this project we intend to (semi)automatically discover interesting paths between concepts in closed-discovery.
RS-DFO Hypothesis
Various datasets and experimental results are available for download and reuse
Datasets and Experimental Results
- Dataset
- Experimental Results
- Experiment I (Subgraphs)
- Experiment II (Subgraphs)
- Stand-alone Subgraphs
- Vascular Reactivity Dataset
- SemRep Relations
- Manually added Predications
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%)