Knowledge representations for sensemaking

I discuss the lessons learned during the design and implementation of three knowledge representations systems for sensemaking. The focus is on the tension that exists between a knowledge representation's role as a surrogate for the world and its role as a facilitator of computational reasoning. Each system accepts natural language inputs and implements a bidirectional model of sensemaking. One system emphasizes inference while the other two systems emphasize their role as a representation for the world. I discuss the differences between these systems and what gives rise to these differences.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Nguyen, Hong-Linh Q

Advisor dc:contributor.advisor

• Boris Katz.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/85395

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85395