Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs
This work addresses the challenge of knowledge extraction for Sanskrit literature, which has limited NLP tools, though it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of extracting knowledge from Sanskrit texts by building a framework for automated construction of knowledge graphs and a question-answering system, achieving about 50% accuracy in answering factoid questions from specific relationships in texts like Mahabharata and Ayurvedic sources.
Sanskrit (sa\d{m}sk\d{r}ta) enjoys one of the largest and most varied literature in the whole world. Extracting the knowledge from it, however, is a challenging task due to multiple reasons including complexity of the language and paucity of standard natural language processing tools. In this paper, we target the problem of building knowledge graphs for particular types of relationships from sa\d{m}sk\d{r}ta texts. We build a natural language question-answering system in sa\d{m}sk\d{r}ta that uses the knowledge graph to answer factoid questions. We design a framework for the overall system and implement two separate instances of the system on human relationships from mahābhārata and rāmāya\d{n}a, and one instance on synonymous relationships from bhāvaprakāśa nigha\d{n}\d{t}u, a technical text from āyurveda. We show that about 50% of the factoid questions can be answered correctly by the system. More importantly, we analyse the shortcomings of the system in detail for each step, and discuss the possible ways forward.