Methodology: AI
Framework for
Computational Analysis of
Sanskrit Texts
1 Methodology
This research introduces a novel, two-stage artificial intelligence framework
specifically designed for the computational analysis of Sanskrit texts with
the goal of reviving embedded scientific knowledge. Due to the highly
inflected and semantically dense nature of Sanskrit, traditional NLP
pipelines are insufficient. Therefore, we propose an architecture composed
of two purpose-built algorithms: SanskritDeepNet-Lexical Graph Constructor
(SDN-LGC) and VedaJnana-Conscious Concept Synthesizer (VJCCS). The
entire pipeline begins with curated data collection and linguistic
normalization, proceeds to semantic graph generation, and concludes with
scientific concept extraction via ontology mapping.
1.1 Data Collection and Preprocessing
The foundation of our system is built upon the Digital Corpus of Sanskrit
(DCS) - a freely available, linguistically annotated repository of classical
Sanskrit texts. We selected approximately 20,000 sentences across
wellknown scientific treatises such as Susruta Samhita, Charaka Samhita,
Aryabhatiya, and the Vaisesika Sutras. These texts are not only linguistically
rich but also represent diverse scientific domains, including medicine,
astronomy, mathematics, and metaphysics.
Preprocessing of these texts begins with Unicode normalization to ensure
consistency across character representations. This is followed by sandhi
splitting, where compound euphonic transformations are separated into
their constituent words using a hybrid rule-based and probabilistic engine.
After this, we apply lemmatization to reduce words to their root forms using
the DCS lexicon, and extract morphological features such as tense, number,
voice, and grammatical case. Compound words (samāsa) are decomposed,
and syntactic roles are tagged for each token. The preprocessed and
annotated sentences are then transformed into token vectors for
downstream semantic parsing.
1.2 SanskritDeepNet-Lexical Graph
Constructor (SDNLGC)
The first core algorithm, SDN-LGC, is responsible for transforming a
sentence into a structured semantic dependency graph that captures
grammatical and conceptual relationships between words. To begin, the
input sentence is encoded into a contextual vector T , using a weighted sum
of token embeddings:
n
T =∑ w i x i+ b
i=1
(1)
Here, x i denotes the vector representation of the i th token, w i is a learned
attention weight, and b is a bias term. To introduce non-linearity and
normalize the aggregated vector, a sigmoid function is applied:
1
σ (T )=
1+e −T
(2)
The semantic relationships between each token pair are computed using
scaled dot-product attention:
(Q K T )i j / dk
e
Ai j = n
∑ e( Q K ) /d
T
ik k
k=1
(3)
These attention scores are passed through a multi-layer perceptron to
classify syntactic roles such as subject, object, modifier, or compound head:
Y =softmax ( W 2 ⋅ ReLU ( W 1 ⋅ X +b 1 ) +b2 )
(4)
The output is then formalized as a directed graph:
G=(V , E)
(5)
Where V represents nodes (words) and E represents syntactic or semantic
edges (relationships). The model is trained by minimizing the mean squared
error between predicted and gold-standard dependencies:
n
1
Loss = ∑ ( ý i − y i ) 2
n i=1
(6)
This graph encodes valuable structural insights such as which term is the
main predicate, which are its arguments, and how scientific terms are
constructed through compound formations or hierarchical dependencies.
1.3 VedaJnana-Conscious Concept Synthesizer
(VJCCS)
Once the sentence is parsed into a dependency graph, the second core
algorithm, VJ-CCS, performs domain-aware concept extraction by mapping
the graph structure onto a curated ontology of ancient Indian scientific
knowledge. This is achieved through hybrid symbolic-neural reasoning.
The algorithm begins by computing the cosine similarity between each
graph node vector and ontology term embeddings:
a ⋅b
CosSim(a ,b)=
‖ a ‖‖b ‖
(7)
To capture dependencies across longer sequences or linked ideas in
compound sentences, a contextual recurrent neural network is used:
ht =f ( W xt +U ht − 1+ b )
(8)
The system's performance is evaluated using standard metrics. Precision
measures the correctness of identified concepts:
TP
Precision =
T P+ F P
(9)
Recall quantifies the coverage of true concepts:
TP
Recall =
T P+ F N
(10)
F1 score balances both metrics:
2 ⋅ Precision ⋅ Recall
F1 Score =
Precision + Recall
(11)
To balance the graph-based and semantic objectives, the total loss function
is defined as:
Ltotal =α ⋅ Lgraph + β ⋅ Lsemantic
(12)
To identify conceptually meaningful co-occurrences, we use Pointwise
Mutual Information (PMI):
Ei j=log
( P ( wi , w j )
P ( wi ) ⋅ P ( w j ) )
(13)
Graph nodes are updated by aggregating contextual information from their
neighbors:
C k= ∑ Ak j h j
j∈ N (k)
(14)
Finally, to project this structured representation into the conceptual space
of the scientific domain, a hyperbolic activation is applied:
Z=tanh ( W z H +b z )
(15)
The output vector Z represents the recognized scientific concept mapped
from the original Sanskrit sentence. For instance, a graph containing the
terms "vāta," "pitta," and "kapha" would be identified as related to Ayurveda,
while "graha," "nakṣatra," and "tithi" would map to astronomy. This enables
intelligent indexing, annotation, and interpretation of ancient texts in a
modern digital framework.
