Knowledge representation using semantic nets and introduction to Sanskrit

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September 13, 2016 - "Knowledge Representation in Sanskrit and Artificial Intelligence" by Rick Briggs is an interesting paper which discusses how Sanskrit could be a best natural language for computer processing/Artificial Intelligence (AI). For decades, the scientific community has been trying to identify and design systems which can represent and process natural language. English is widely spoken language and we intend machines to learn English and process the data.

However, one cannot program systems using natural English language; we have to reframe or rephrase the language in a systematic way so that systems can understand. In Rick Briggs', the author explains how Sanskrit is significantly more advanced than English, and how it can be made use of in the field of AI.

The paper comprises three parts. In the first part, a knowledge representation scheme is discussed using semantic nets. In second, the author outlines methods used by ancient Indian grammarians to analyze sentences unambiguously. In the third, equivalence is established between the Sanskrit language analysis and techniques used in applications of AI.

When attempts at machine translation failed to teach a computer to understand natural language, AI turned to knowledge representation. Teaching natural language to a machine should not necessarily involve word-to-word mapping. One has to overcome the ambiguity of words in natural language and the interference of syntax. To overcome the ambiguity of words, there should be a representation of meaning independent of individual words used. The author cites three sentences as examples to demonstrate a prototypical semantic net system.

“John gave the book to Mary"
The grammatical information can be transformed into an arc and a node. The above sentence can be stored as triples:

  • give, agent, John
  • give, object, book
  • give, recipient, mary
  • give, time, past

This can be schematically represented as below:

Figure 1: Schematic representation of sentence “John gave the book to Mary." (Rick Briggs, 1985)

"John told Mary that the train moved out of the station at 3 o' clock."
As the below figure shows there was a change in state in which the train moved to an unspecified location from the station. It changed its location at 3'o clock. We can now covert this to triples like the previous example. Here the verb is given significance and is considered the focus and distinguishing aspect of the sentence.

Figure 2: Schematic representation of sentence “John told Mary that the train moved out of the station at 3 o' clock." (Rick Briggs, 1985).

Rick Briggs mentions other sentences in the paper, which when drawn as above nets will represent only a state of a thing or an event.

"John, a programmer living at Maple St., gives a book to Mary, who is a lawyer."
The above statement, if read as semantic net, would give an awkward and cumbersome representation. The degree to which a semantic net is cumbersome and odd-sounding in a natural language is the degree to which that language is “natural" and deviates from the precise or “artificial." See the figure below.

Figure 3: Schematic representation of sentence “John, a programmer living at Maple St., gives a book to Mary, who is a lawyer." (Rick Briggs, 1985).

Rick Briggs gives brief history of Sanskrit grammarians such as Panini, Kaundabhatta, Bhattoji Dikshita and Nagesha. Panini who lived during 4th century BCE gave a strong foundation to the Sanskrit grammar. Panini's successors like Bhartrhari gave algebraic formulation for grammar and tried to improve upon them. During the 16th century Kaundabhatta and Bhattoji Dikshita gave new touch to the existing grammar with their publication of Bhattoji Dikshita's Vaiyakarana-bhusanasara. Similarly during 17th century Nagesha contributed to the language with his major work on Vaiyakaranasiddhantamanjusa, or Treasury of definitive statements of grammarians. Author sites these grammarians and makes a strong point that the Sanskrit is not only a simple spoken language but has a scientific and mathematical backbone to it.

This is the first blog about Sanskrit and Artificial Intelligence from Jayanth Babu MN.

Jayanth Babu MN is part of the Genpact Chief Science Office protégé program. Jayanth Babu MN was supervised by Pradyumna S. Upadrashta, Chief Science Officer, Analytics & Research, for this blog.

Author: Jayanth Babu MN - Business Analyst


  1. Rick Briggs (1985) Knowledge Representation In Sanskrit And Artificial Intelligence.
  2. Bhatta, Nagesha (1963) Vaiyakarana-Siddhanta-Laghu-Manjusa, Benares (Chowkhamba Sanskrit Series Office).
  3. Nilsson, Nils J. Principles of Artificial Intelligence. Palo Alto: Tioga Publishing Co
  4. Bhatta, Nagesha (1974) Parama-La&u-Manjusa