Current Projects
Insta-Search: Towards Effective Exploration of Knowledge Graphs
This project involves developing a web-based interface for facilitating exploration over Knowledge Graphs. The users would be given autocomplete options, reformulation suggestions and instant feedback in the form of the expected number of answers and expected top answers.
Past Projects
Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs [Jul 2015 - Oct 2018]
This project involved optimization of SPARQL queries with relaxations. We proposed a speculative approach to predict the requirement of relaxations and hence, prune them to reduce the runtimes and memory consumptions. This work got published in EDBT 2019 and is available here.
KlusTree: Clustering Answer Trees from Keyword Search on Graphs [Mar 2013 - Aug 2017]
Keyword search on graphs is supported by many algorithms. They return answer trees which are interconnections between the matching keyword nodes. This project focused on improving user experience by reducing redundant information. In order to achieve this, we proposed a new distance metric based on Language Models (LMs) to cluster answer trees conveying similar information together. This work got published in CoDS-COMAD 2018 and is available here.
Polarizer -- University Hack Day Project [Aug 2013]
Polarizer is a sentiment analysis engine developed as a part of HackU, Yahoo! annual University Hack Day. We had implemented peer-to-peer insult filtering and pro-con classification of user comments on online public debates. We also added the feature of ranking the comments and generating location based demographic sentiment heatmap using heatmap.js. Polarizer won first prize competing with 40 other teams.
Ontology Extraction by focused retrieval using the web as an Oracle -- M.Tech Thesis [Jan 2012 - May 2012]
This project involved building up of a domain ontology using Latent Semantic Indexing (LSI) and theme extraction techniques. The LSI discovers the concept and the term nodes. The theme extraction and clustering techniques have been used to extract relations. The final ontology is in the form of a bipartite graph.
Focused Web information retrieval for building a contextual knowledge base using statistical learning techniques -- Research Project [Jan 2011 - Dec 2011]
In this project, statistical learning techniques like incremental clustering (for document and word) and classification (for document and word) were used to retrieve web information for building a contextual knowledge base. The learning ensures that the web pages retrieved are relevant to the current focus which is defined by the seed words and seed sites.