  Knowledge bases (KB) are an important resource in a number of natural
language processing (NLP) and information retrieval (IR) tasks, such as
semantic search, automated question-answering etc. They are also useful for
researchers trying to gain information from a text. Unfortunately, however, the
state-of-the-art in Sanskrit NLP does not yet allow automated construction of
knowledge bases due to unavailability or lack of sufficient accuracy of tools
and methods. Thus, in this work, we describe our efforts on manual annotation
of Sanskrit text for the purpose of knowledge graph (KG) creation. We choose
the chapter Dhanyavarga from Bhavaprakashanighantu of the Ayurvedic text
Bhavaprakasha for annotation. The constructed knowledge graph contains 410
entities and 764 relationships. Since Bhavaprakashanighantu is a technical
glossary text that describes various properties of different substances, we
develop an elaborate ontology to capture the semantics of the entity and
relationship types present in the text. To query the knowledge graph, we design
31 query templates that cover most of the common question patterns. For both
manual annotation and querying, we customize the Sangrahaka framework
previously developed by us. The entire system including the dataset is
available from https://sanskrit.iitk.ac.in/ayurveda/ . We hope that the
knowledge graph that we have created through manual annotation and subsequent
curation will help in development and testing of NLP tools in future as well as
studying of the Bhavaprakasanighantu text.
