Chronological Analysis of Rigvedic Mandalas using Social Networks

Establishing the chronology of the Vedas has interested scholars for the last two centuries. The oldest among them is Rig-Veda which has ten Mandalas, each composed separately. In this paper, we look at deciphering plausible pointers to the internal chronology of the Mandalas, by focusing on Gods and Goddesses worshiped in different Mandalas. We apply text analysis to the Mandalas using Clustering Techniques based on Cosine Similarity. Then we represent the association of deities with Mandalas using a grid-based Social Network that is amenable to chronological analysis and demonstrates the benefits of using Social Network Analysis for the problem at hand. Further, we analyze references to rivers to arrive at additional correlations. The approach used can be deployed generically to analyze other kinds of references and mentions and arrive at more substantive inferences.

Linguistic Analysis using Paninian System of Sounds and Finite State Machines

The study of spoken languages comprises phonology, morphology, and grammar. Analysis of a language can be based on its syntax, semantics, and pragmatics. The languages can be classified as root languages, inflectional languages, and stem languages. All these factors lead to the formation of vocabulary which has commonality/similarity as well as distinct and subtle differences across languages. In this paper, we make use of Paninian system of sounds to construct a phonetic map and then words are represented as state transitions on the phonetic map. Each group of related words that cut across languages is represented by a m-language (morphological language). Morphological Finite Automata (MFA) are defined that accept the words belonging to a given m-language. This exercise can enable us to better understand the inter-relationships between words in spoken languages in both language-agnostic and language-cognizant manner.

Transforming India's Agricultural Sector using Ontology-based Tantra Framework

Food production is a critical activity in which every nation would like to be self-sufficient. India is one of the largest producers of food grains in the world. In India, nearly 70 percent of rural households still depend on agriculture for their livelihood. Keeping farmers happy is particularly important in India as farmers form a large vote bank which politicians dare not disappoint. At the same time, Governments need to balance the interest of farmers with consumers, intermediaries and society at large. The whole agriculture sector is highly information-intensive. Even with enormous collection of data and statistics from different arms of Government, there continue to be information gaps. In this paper we look at how Tantra Social Information Management Framework can help analyze the agricultural sector and transform the same using a holistic approach. Advantage of Tantra Framework approach is that it looks at societal information as a whole without limiting it to only the sector at hand. Tantra Framework makes use of concepts from Zachman Framework to manage aspects of social information through different perspectives and concepts from Unified Foundational Ontology (UFO) to represent interrelationships between aspects. Further, Tantra Framework interoperates with models such as Balanced Scorecard, Theory of Change and Theory of Separations. Finally, we model Indian Agricultural Sector as a business ecosystem and look at approaches to steer transformation from within.