Current Projects


Semantic MediaWiki

Creating semantic infrastructure including ability to create semantic metadata for a variety of data types utilizing domain models and knowledge bases nd to create a Collaborative data collection and data/knowledge management using semantic technologies in Material Science
My work: Create a collaborative knowledge management system by leveraging Semantic MediaWiki to make assertion in Material Science using W3C vocabularies, Virtuoso, SPARQL, PHP.

Project Page :http://matvocab.org/wiki-dev/index.php/Main_Page


OBVIO

Obvio is a graph-based framework for exploring biomedical literature to facilitate Literature-Based Discovery (LBD) based on rich knowledge representations. Its broader goal is to uncover hidden and complex associations between concepts in biomedical texts. Obvio has resulted in the rediscovery of 8 out of 9 existing discoveries from scientific literature.
My work: To compute MeSH Semantic Similarity between MeSH descriptors using various Semantic Similarity Measures and also to develop a web application using Adobe Flex for visualizing the automatically generated subgraphs, which capture complex associations between two concepts.

Project Page:http://wiki.knoesis.org/index.php/Obvio
Online Resources OBVIO : Wiki,


Information Extraction in Material Science and Biomaterial Texts:

A project in collaboration with Air Force Research Laboratories (AFRL/RX). For this project we are applying knowledge and technology in informatics to the material domains and develop a data exchange system that will allow researchers to index, search, and compare data in material science.
My work: Performing Text analytics on Biomaterial texts and identify specific patterns of interest and the relations between them. Also, assisted in developing a database interactive tool for allowing domain experts to annotate domain-specific literature.

Project Page :http://knoesis.org/research/semMat

Online Resources: Wiki, MatVocab Search - Search driven by the Materials Vocabulary


PREDOSE

PREDOSE is a social media analytics platform developed to facilitate prescription drug abuse epidemiology on the illicit use of pharmaceutical opioids. It is designed to capture the knowledge, attitudes and behaviors of prescription drug abusers through the automatic extraction of semantic information from social media. This includes extraction of entities, relationships, triples and other intelligible constructs such as sentiments, emotions, intervals, frequency and dosage from unstructured text.
My Work: To search for specific patterns in unstructured text and determine semantic relationships for the purpose of discovering interesting and unknown trends of drug abuse using IBM BigInsights and Text analyst (AQL) technology.

Project Page:http://wiki.knoesis.org/index.php/PREDOSE
Online Resources PREDOSE : Wiki, Live Tool, Video demo
Knowledge Aware Search: Wiki, Live Tool, Video Demo


Publications

D.Cameron, A.P. Sheth, N. Jaykumar , G. Anand, K. Thirunarayan, G. A. Smith, A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs (under review)


CourseWork


>Advanced Database Systems
>Advanced Programming Languages
>Information Retrieval
> Web 3.0 - Semantic Web
> Algorithm Design and Analysis
> Cloud computing
> Advanced Semantic Web
> Web Information Systems