activity
20182024
most citedSimilar Cases Recommendation using Legal Knowledge Graphs

8 citations · 35 across the 13 of their papers we have counts for

collaborators

13 papers

cs.AI2024

xLP: Explainable Link Prediction for Master Data Management

Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala +6

Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the m…

cs.AI2022

xEM: Explainable Entity Matching in Customer 360

Sukriti Jaitly, Deepa Mariam George, Balaji Ganesan +2

Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity. Entities are typically people, organizations, locations, and ev…

cs.AI2021★ 8 cited

Similar Cases Recommendation using Legal Knowledge Graphs

Jaspreet Singh Dhani, Ruchika Bhatt, Balaji Ganesan +2

A legal knowledge graph constructed from court cases, judgments, laws and other legal documents can enable a number of applications like question answering, document similarity, an…

cs.LG2021

Reimagining GNN Explanations with ideas from Tabular Data

Anjali Singh, Shamanth R Nayak K, Balaji Ganesan

Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained o…

cs.AI2021★ 1 cited

Towards Automated Evaluation of Explanations in Graph Neural Networks

Vanya BK, Balaji Ganesan, Aniket Saxena +2

Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem. In particular, we do not have well develope…

cs.AI2021★ 1 cited

Document Structure aware Relational Graph Convolutional Networks for Ontology Population

Abhay M Shalghar, Ayush Kumar, Balaji Ganesan +3

Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems. While there have been efforts towards populating domain specific…

cs.CR2020★ 3 cited

Explainable Link Prediction for Privacy-Preserving Contact Tracing

Balaji Ganesan, Hima Patel, Sameep Mehta

Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have be…

cs.SI2020★ 6 cited

Link Prediction using Graph Neural Networks for Master Data Management

Balaji Ganesan, Srinivas Parkala, Neeraj R Singh +5

Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, and customer due diligence. Contact tra…

cs.IR2020★ 2 cited

Data Augmentation for Personal Knowledge Base Population

Lingraj S Vannur, Balaji Ganesan, Lokesh Nagalapatti +2

Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant impro…

cs.AI2020★ 4 cited

A Neural Architecture for Person Ontology population

Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh +2

A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for b…

cs.CL2018

Document Structure Measure for Hypernym discovery

Aswin Kannan, Shanmukha C Guttula, Balaji Ganesan +2

Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypern…

cs.CL2018★ 6 cited

Fine Grained Classification of Personal Data Entities

Riddhiman Dasgupta, Balaji Ganesan, Aswin Kannan +2

Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents. While neural networks have recently improved the classification…

cs.CL2018★ 4 cited

Collective Learning From Diverse Datasets for Entity Typing in the Wild

Abhishek Abhishek, Amar Prakash Azad, Balaji Ganesan +2

Entity typing (ET) is the problem of assigning labels to given entity mentions in a sentence. Existing works for ET require knowledge about the domain and target label set for a gi…