SYSTEM FOR GRAPH-BASED CLUSTERING OF DOCUMENTS

1 . A system for graph-based clustering of documents, the system comprises one or more processors configured to:

receive a digital copy of a document to convert the document into a graph object;

identify and label entities in the document, wherein each of the entities is represented as a node of the graph object;

create the graph object for the received digital copy of the document;

generate a graph embedding vector using a graph embedding neural network trained to receive the graph object as input and generate the graph embedding vector for the graph object as output; and

cluster the graph embedding vector to a cluster comprising similar looking templates of the document.

2 . The system as claimed in claim 1 , wherein the one or more processor is configured to create the graph object by:

connecting each of the nodes representing an entity with its neighbouring nodes along four directions; and

forming edges between each of the nodes and its neighbouring nodes along four directions.

3 . The system as claimed in claim 2 , wherein the edges are formed between the nodes based on the relative position of each of the nodes with its neighbouring nodes.

4 . The system as claimed in claim 1 , wherein the graph embedding neural network is a Siamese network comprising:

a first neural network comprising:

a first encoder;

a first graph neural network; and

a first pooling layer; and

a second neural network comprising:

a second encoder;

a second graph neural network; and

a second pooling layer.

5 . The system as claimed in claim 4 , wherein the one or more processors is configured to:

train the graph embedding neural network using a training dataset comprising training documents, wherein graph embedding neural network is trained by:

identifying and labelling entities, using the processor, in each of the training documents, wherein each of the entities is represented as a node of a graph object;

creating graph objects, using the processor, for each of the training documents;

computing graph edit distance (GED) matrix, using the processor, for a first batch of documents from the training dataset;

inputting a pair of graph objects and the computed graph edit distance matrix to the graph embedding neural network, wherein one of the graph objects is input to the first neural network and the other graph object is input to the second neural network;

generating a graph embedding vector, by the graph embedding neural network, for each of the input pair of graph objects; and

calculating a similarity score between the graph embedding vectors generated by the first neural network and the second neural network;

6 . The system as claimed in claim 5 , wherein:

the first encoder and the second encoder are configured to receive the graph object as input and generate an entity type embedding vector of a predefined size;

the first graph neural network and the second graph neural network are configured to generate node representations encoding the structural information of the documents of the graph objects; and

the first pooling layer and the second pooling layer are configured to aggregate the node representations and generate the graph embedding vector for the input graph object.

7 . The system as claimed in claim 5 , wherein the one or more processor is configured to normalize the computed graph edit distance of the graph edit distance matrix to be between the range of 0 to 1.

8 . The system as claimed in claim 5 , wherein the graph embedding vector is vector of the size 1×128.

9 . The system as claimed in claim 5 , wherein upon training the graph embedding neural network, only one neural network from the Siamese network is configured to generate the graph embedding vector for the input graph object.

10 . The system as claimed in claim 5 , wherein:

the similarity score is calculated using a cosine similarity function; and

the similarity score is represented as (1-GED).

11 . The system as claimed in claim 1 , wherein the one or more processors are configured to cluster the graph embedding vectors using clustering techniques such as partitional clustering such as K-means clustering, hierarchical clustering such as agglomerative clustering, or spectral clustering.

12 . The system as claimed in claim 1 , the system comprises a machine learning model configured to classify the documents, wherein the clustered documents are fed as input to the machine learning model.