Knowledge Graph Platform with Percuro
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
- 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) 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:
- 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)
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.
Overview
GitHub
Demo
People
- Artificial Intelligence Institute, University of South Carolina
