1st open source release of SADL
There exist few tools for expert and non-expert users to rapidly explore, query and manage semantic data in knowledge graphs. We have developed a suite of toolkits that provide a strong foundation for modeling domain knowledge and building knowledge-driven applications, and have open-sourced two of these tools. The Semantic Application Design Language (SADL) allows users to author and view domain models in a structured English-like language understandable by domain experts without requiring expertise in semantics. The Semantics Toolkit (SemTK) facilitates the development of knowledge-driven applications by enabling visual model exploration, automatic pathfinding and query building, and providing a suite of API’s to interact with digitized domain knowledge. Together, these tools enable us to rapidly prototype, develop and deploy knowledge-driven solutions for a variety of industrial and biomedical applications.
Through these tools we have made significant contributions to applications such as:
(i) Analysis of Semantic Specifications and Efficient generation of Requirements-based Tests (ASSERT) for GE Aviation Systems to perform verification and validation of software and system requirements to ensure they are complete and consistent before development begins, significantly reducing software development cost and time
(ii) Cyber Assured Systems Engineering (CASE), a DARPA program to drive cyber resiliency
(iii) TEDS, the Turbine Engineering Data System for GE Power, to enable knowledge-driven integration and access to gas turbine test datasets, reducing data access times from days to minutes.
The ASSERT tool… greatly improves the accuracy of requirements and removes ambiguity, issues which have long plagued traditional implementation and verification activities. C. McMillan, GE Aviation
Accelerating Knowledge-Drive Solutions
1st open source release of SemTKJan 2016
Production launch of ASSERT tool suite
Production launch of TEDS using SemTKJan 2018
Capabilities utilized for Accelerating Knowledge-Driven Solutions project
Knowledge Management & Big Data
Applying semantic modeling, text mining and Big Data to capture and digitize industrial domain knowledge for human and machine useRead more
Developing and scaling machine learning solutions for industrial applications to facilitate continuous learning, adaptation and improvement in dynamic operating environmentsRead more
- Crapo, A., Moitra, A., McMillan, C. and Russell, D., 2017, September. Requirements Capture and Analysis in ASSERT (TM). In Requirements Engineering Conference (RE), 2017 IEEE 25th International (pp. 283-291). IEEE.
- McHugh, J., Cuddihy, P.E., Williams, J.W., Aggour, K.S., Kumar, V.S. and Mulwad, V., 2017, December. Integrated Access to Big Data Polystores through a Knowledge-driven Framework. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 1494-1503). IEEE.
- Williams, J.W., Cuddihy, P., McHugh, J., Aggour, K.S., Menon, A., Gustafson, S.M. and Healy, T., 2015, October. Semantics for Big Data access & integration: Improving industrial equipment design through increased data usability. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 1103-1112). IEEE.
- Cuddihy, P., McHugh, J., Williams, J.W., Mulwad, V. and Aggour, K., SemTK: A Semantics Toolkit for User-friendly SPARQL Generation and Semantic Data Management.