Background and Motivation

Knowledge Graph (KG) is an encapsulation of structured knowledge in a graphical representation & used for a variety of information processing and management tasks such as

Data & knowledge integration from diverse sources
Improve automation
Enabling new generation of applications
Empowering machine learning (ML) & NLP techniques with domain knowledge

and applications such as question answering, summarization, text simplification, and Named Entity Recognition (NER).

Most existing KG platforms & tools are limited in

Provenance
Dynamicity (ie: static schema vs schema generation)
Temporal
Domain specificity
Modularity

The AIISC Knowledge Graph (KG) EMPWR effort involves the development of a comprehensive tool and platform for KG development with the following aims

1. Develop a KG development platform capable of instantiating KGs in any domains from structured, semi-structured, and unstructured data:

- Biomedical & pharmaceutical domain with Percuro

2. Improve & address the limitations of existing KG platforms

3. Constructs a Knowledge Graph (based on a combination of)

- Enrich an existing Knowledge Graph (Top-down declarative)
- Construct a Knowledge Graph out of given entities (Bottom-up data driven)

Goals & Use-Cases

The goals of KGs are to provide

Contextualization
- Context-Enriched Learning Models for Aligning Biomedical Vocabularies in the UMLS Metathesaurus
Personalization
Abstraction
Explainability

Collaborations

Percuro is a collaborative research project involving WIPRO, The AI Institute at University of South Carolina (AIISC), and IIT-Patna (IIT-P). It involves development of semantic (i.e., knowledge graph enhanced) approach to natural language processing (NLP), natural language generation (NLG) and natural language understanding (NLU) targeted at the pharmaceutical domain. It will involve techniques for NLP/NLG/NLU on biomedical and clinical documents relevant to pharmaceutical markets.

Percuro aims to solve tasks such as (a) text simplification, (b) summarization and (c) question answering. These are tasks that are not straightforward and require more information that what the text provides.

Project: Multimodal Knowledge Graphs (MMKGs) Construction and Analysis to Accelerate Pharmaceutical Drug Discovery with EMPWR

One of the main challenges in the pharmaceutical industry is navigating and integrating vast, heterogeneous datasets, from scientific literature and clinical trial results to genomic and molecular data. Knowledge Graphs (KGs) have emerged as a powerful and promising technology to structure this information, representing real - world entities like drugs, diseases, and genes as nodes, and their relationships as edges. However, building and maintaining these KGs is a complex, and continuous process. The EMPWR platform is a comprehensive solution designed to manage the entire KG lifecycle. It was developed to bridge the gap between traditional symbolic AI systems, which are explainable but difficult to scale, and modern data - driven systems like deep learning and transformer - based models, which are powerful but often function as "black boxes".