Tractor is a system for understanding English messages within the context of hard and soft information fusion for situation assessment. Tractor processes a message through text processors using standard natural language processing techniques, and represents the result in a formal knowledge representation language. The result is a hybrid syntactic-semantic knowledge base that is mostly syntactic. Tractor then adds relevant ontological and geographic information. Finally, it applies handcrafted syntax-semantics mapping rules to convert the syntactic information into semantic information, although the final result is still a hybrid syntactic-semantic knowledge base. The complete Tractor architecture is shown below.
Our evaluation of Tractor found that it worked quite well, with recall of .83 and precision of .83. Similar results were obtained on both development and test datasets. See the below articles for more details. Tractor is designed to be just one component in a larger hard-soft information fusion system. Articles about this larger system are also listed below.
Tractor is a joint development of myself, Dr. Stuart C. Shapiro, Michael Prentice, and Michael Kandefer.
Read More about Tractor
- Shapiro, S.C., Schlegel, D.R., and Prentice, M., Tractor Manual. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, 2015.
- Shapiro, S.C. and Schlegel, D.R. Use of Background Knowledge in Natural Language Understanding for Information Fusion. Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), IFIP, July 2015, 901–907.
- Shapiro, S.C. and Schlegel, D.R., Natural Language Understanding for Information Fusion. In P. Scott and G. Rogova, Eds., Fusion Methodologies in Crisis Management: Higher Level Fusion and Decision Making, Springer S. A., (in press).
Read More about the MURI Hard-Soft Information Fusion System
- Gross, G.A., Date, K., Schlegel, D.R., Corso, J.J., Llinas, J., Nagi, R., and Shapiro, S.C. Systemic Test and Evaluation of a Hard+Soft Information Fusion Framework : Challenges and Current Approaches. Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), IFIP, July 2014, 8 pages.
- Gross, G.A., Nagi, R., Sambhoos, K., Schlegel, D.R., Shapiro, S.C., and Tauer, G. Towards Hard+Soft Data Fusion: Processing Architecture and Implementation for the Joint Fusion and Analysis of Hard and Soft Intelligence Data. Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), 2012, pp. 955–962.