Publications, Reports and Thesis
Angelos-Christos Anadiotis, Ioana Manolescu, Madhulika Mohanty; Integrating connection search in graph queries. BDA 2022 (Accepted)
S. Amer-Yahia, Y. Amsterdamer, S. Bhowmick, A. Bonifati, P. Bonnet, R. Borovica-Gajic, B. Catania, T. Cerquitelli, S. Chiusano, P. Chrysanthis, C. Curino, J. Darmont, A. El Abbadi, A. Floratou, J. Freire, A. Jindal, V. Kalogeraki, G. Koutrika, A. Kumar, S. Maiyya, A. Meliou, M. Mohanty, F. Naumann, N. S. Noack, F. Özcan, L. Peterfreund, W. Rahayu, W. C. Tan, Y. Tian, P. Tözün, G. Vargas-Solar, N. Yadwadkar, M. Zhang; Diversity and Inclusion Activities in Database Conferences: A 2021 Report. SIGMOD Record 2022
Techniques for Effective Search and Retrieval over Knowledge Graphs. Madhulika Mohanty, Ph.D. Thesis 2020.
Madhulika Mohanty, Maya Ramanath; Insta-Search: Towards Effective Exploration of Knowledge Graphs.(Demo) CIKM 2019
Madhulika Mohanty, Maya Ramanath, Mohamed Yahya, and Gerhard Weikum; Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs. EDBT 2019
Madhulika Mohanty, Maya Ramanath; KlusTree: Clustering Answer Trees from Keyword Search on Graphs. CoDS-COMAD '18 Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2018
Madhulika Mohanty, Deepanjana Deb, Aravind Konidala, Ashish Mishra, and Solomon Raju Kota; Framework for design with LEON3 system and testing with SHA-1 algorithm. International Conference on Communication and Industrial Application (ICCIA), 2011
Aligning Knowledge Graphs for Question Answering applications [Feb 2021 - present]
This project involves aligning different Knowledge Graphs (KGs) for various applications involving Question Answering (QA). The core of this work involves aligning entities, predicates, and classes across various KGs using novel algorithmic strategies, NLP techniques, and user feedback.
Structured + Unstructured query support on graphs [Aug 2021 - present]
This work involves developing the semantics for a novel graph query language that enables both structured and unstructured fragments. Additionally, it involves the development of an execution engine for executing such queries.
Insta-Search: Towards Effective Exploration of Knowledge Graphs [Aug 2018 - present]
This is a web-based interface for facilitating exploration over Knowledge Graphs. The users are given autocomplete options, reformulation suggestions and instant feedback in the form of the expected number of answers and expected top answers. A prototype of this work has been published in CIKM 2019 and is available here.
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.