Revision as of 00:42, 23 April 2014

Obvio (spanish for obvious) is the name of the project on the use of structured knowledge representation for Literature-Based Discovery (LBD) using Biomedical Literature. The goal of Obvio is to uncover hidden connections between concepts in biomedical texts, to facilitate hypothesis generation from publicly available scientific literature.

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). The fundamental notion is that LBD could be greatly facilitated by the Semantic Integration of assertions extracted from scientific literature and well curated background knowledge from heterogeneous data sources.

People

Graduate Students: Delroy Cameron, Swapnil Soni, Nishita Jaykumar, Vishnu Bompally
External Collaborators: Olivier Bodenreider, Thomas C. Rindflesch, Ramakanth Kavuluru
Faculty: Krishnaprasad Thirunarayan, Amit P. Sheth (Advisor)
Past Members: Pablo N. Mendes, Tu Danh, Sreeram Vallabhaneni, Hima Yalamanchili, Drashti Dave

Live Demo

http://knoesis-hpco.cs.wright.edu/obvio/

Applications

Reachability

We applied semantic predications to biomedical QA using data from the 2006 TREC Challenge using the notion of reachability to determine whether documents that answer complex biomedical questions could be connected through semantic predications, together with background knowledge<ref>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%)</ref>. The QA tasks put forth by the Text REtrieval Conferences (TREC) offer an opportunity to determine whether semantic predications and background knowledge can produce relevant information for complex biomedical questions and information needs. Below is our presentation from BIBM 2011 on our findings for the application of semantic predications and background knowledge to QA.

Rediscovery

A second application of semantic predications is to the field of Literature-based Discovery (LBD). LBD refers to uncovering conclusions that have never been made explicit before, but are implicit in publicly available literature. Semantic predications have been demonstrated to be important constructs in facilitating LBD by providing context among associated concepts.

RS-DFO Hypothesis

Much of the early research aimed at rediscovering Swanson's Hypotheses 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<ref>D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch, A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications, Journal of Biomedical Informatics 46(2) 238-251, (2013). ScienceDirect, PMID </ref> 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. Below is a presentation, and various datasets and experimental results for download and consumption based on our investigation.

Datasets and Experimental Results

1. Dataset
   1. Baseline (B1)
      1. Original PDFs of the 65 articles cited by Swanson's RS-DFO paper (30.5MB)
      2. ASCII text with end-of-line text wrapping fixed
      3. Text in Medline format for parsing by SemRep
      4. SemRep Relations Output
      5. SemRep Relations Output (vascular reactivity)
      6. SemRep Extracted Predications
      7. Manually Identified Predications (vascular reactivity)
   2. Baseline (B2)
      1. Titles and abstracts of the 65 articles cited by Swanson's RS-DFO paper in Medline format for parsing by SemRep
      2. SemRep Relations Output
      3. SemRep Relations Output (vascular reactivity)
      4. SemRep Extracted Predications
      5. Manually Identified Predications (vascular reactivity)
2. Experimental Results
   1. Association-Subgraph Comparisons (Experiment I)
   2. Association-Subgraph Comparisons (Experiment II)
   3. All Generated Subgraphs (Experiments 1 & 2)

Automatic Subgraph Creation

In an evaluation of the context-driven subgraph model for LBD, 8 out of 9 existing discoveries were rediscovered using this approach. A manuscript detailing the approach is under preparation <ref>D. Cameron, R. Kavuluru, T. C. Rindflesch, A. P. Sheth, K. Thirunarayan, O. Bodenreider Context-Driven Automatic Subgraph Creation for Literature-Based Discovery (under preparation)</ref> The results of each scenario is detailed in the following table.

Publications

<references/>

SWLBD Workshop

Kno.e.sis and the National Library of Medicine (NLM) organized The First International Workshop on the role of Semantic Web in Literature-Based Discovery (SWLBD2012) in conjunction with The IEEE Conference on Bioinformatics and Biomedicine (BIBM2012) in Philadelphia PA, USA.

• Due date for full workshop papers submission: Aug 6, 2012
• Notification of paper acceptance to authors: August 28, 2012
• Camera-ready version of accepted papers: September 4, 2012
• Workshop: October 4, 2012

Internal

Obvio Web App
Automatic Subgraph Creation
Recovery and Decomposition
Reachability

Contact: Delroy Cameron