Enhanced Object-Based Production Conference
Event Date and Venue
May 13 – 14, 2020
SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207
Organizer and Contact
For information or to register contact:
David G. Limbaugh email@example.com
Schedule of Topics and Speakers
Wednesday May 13, 2020
Day One: Referent Tracking and Object Based Production Part I
9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology
Barry Smith, Director, National Center for Ontological Research (NCOR)
9:45am – 10:45am (60 minutes) Using the Common Core Ontologies
Ron Rudnicki, Senior Ontologist, CUBRC
10:45am – 11:00am (15 minutes) BREAK
11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence
12:00pm – 1:00pm LUNCH
1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking
Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo
William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida
3:10pm – 3:25pm (15 minutes) BREAK
3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production
David Limbaugh, Intelligence Community Postdoc, University at Buffalo
Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo
Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory
Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory
Thursday, May 14, 2020
Day Two: Intelligence Community Ontology Foundry 9am – 12:00pm (180 minutes)
Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.
Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.
The Problem: Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.
The Research: Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years . E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.
The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.
E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted . It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.