Knowledge engineering refers to modeling, storing, processing, mining, correlation and inference of knowledge typically embedded in natural languages in various media. This field has been in existence for decades in the form of expert systems, though at small scale for niche applications. However, it is re-emerging as a growth area in computing after Big Data analytics. It is especially relevant to India now due to the government’s thrust on language-enabled services and exploiting indigenous knowledge for sustainable growth.
India has one of the richest repositories of human knowledge preserved over millennia in languages such as Sanskrit that are naturally more amenable to machine-processing. The bulk of them are in oral traditions as well as in 4+ million written manuscripts in dozens of Indic languages and scripts. They are yet to be deciphered, and span a wide variety of subjects including ecology, biology, psychology, agriculture, metallurgy, architecture and astronomy. Mining this knowledge base requires applying the latest computing algorithms and technologies in innovative ways.
Moreover, India’s traditional sciences called pada-vākya-pramāṇaśāstra-s extensively deal with language interpretation and precise and concise knowledge representation. Many śāstra texts adhere to their rules, making them more amenable to machine-processing than modern language texts.