Contributions
1. Deciphering psycho-social effects of Eating Disorder: Analysis of Reddit Posts using LLMs and Topic Modeling (EMNLP 2024)
Authors: Medini Chopra, Anindita Chatterjee, Lipika Dey, Partha Pratim Das
Venue: Proceedings of the Workshop on Natural Language Processing for Digital Humanities, to be held at Miami, Florida in November 2024, during Empirical Methods in Natural Language Processing (EMNLP 2024)
TBD
2. Enhancing FKG.in: automating Indian food composition analysis (MADiMa 2024)
Authors: Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, Geeta Trilok-Kumar, Ramesh Jain
Venue: Proceedings of the 9th International Workshop on Multimedia Assisted Dietary Management (MADiMa) in conjunction with the 27th International Conference on Pattern Recognition (ICPR 2024), Kolkata, India, December 01, 2024
This paper presents a novel approach to compute food composition data for Indian recipes using a knowledge graph for Indian food (FKG.in) and LLMs. The main focus is to give a broad overview of an automated food composition analysis workflow and describe its core functionalities viz. nutrition data aggregation, food Composition analysis, and LLM-augmented information resolution. This workflow aims to complement FKG.in and supplement the food composition data from verified knowledge bases iteratively. This paper also highlights the challenges in both representing Indian food and accessing food composition data digitally and delves into reviewing 3 sources of food composition data viz. Indian Food Composition Tables, Indian Nutrient Databank, and Nutritionix API. Additionally, it briefly describes how users can interact with the workflow to receive diet-based health recommendations and food composition information for innumerable recipes. We then discuss the complex challenges in analyzing Indian recipe information along the axes of structure, multilingualism, and uncertainty and present our ongoing work to discuss some LLM-based resolutions that could be used to address these challenges.
3. Building FKG.in: a Knowledge Graph for Indian Food (IFOW 2024)
Authors: Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, Ramesh Jain
Venue: Proceedings of the Joint Ontology Workshops (JOWO) - Episode X: The Tukker Zomer of Ontology, and satellite events co-located with the 14th International Conference on Formal Ontology in Information Systems (FOIS 2024), July 15-19, 2024, Enschede, The Netherlands
This paper presents an ontology design along with knowledge engineering and multilingual semantic reasoning techniques to build an automated system for assimilating culinary information for Indian food in the form of FKG.in - a knowledge graph. The focus is on designing intelligent methods to derive ontology designs and capturing all-encompassing knowledge about food, recipes, ingredients, cooking characteristics, and most importantly, nutrition, at scale. We also describe the relevant challenges in curating knowledge of Indian food, propose our high-level ontology design, and present a novel workflow that uses AI, LLM, and language technology to curate information from recipe blog sites in the public domain to build knowledge graphs for Indian food. The design is application-agnostic and can be used for AI-driven smart analysis, building recommendation systems for Personalized Digital Health, and complementing the knowledge graph for Indian food with contextual information such as user information, food biochemistry, geographic information, agricultural information, etc.
4. Building a knowledge graph for Indian food (BDBio 2024 - Poster)
Authors: Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, Ramesh Jain
Venue: IISc, Bangalore - Big Data Algorithms for Biology (BDBio) Symposium (June ‘24)
TBD