Find-to-Forecast Process An Automated Methodology for Situation Assessment

Kent Bimson**/*, Ahmad Slim*, Gregory Heileman*

University of New Mexico* and ISS**

•  Research Goal •  Develop an information processing methodology for

automated situation assessment

•  Research Objectives •  Identify key technical challenges in developing an

automated situation assessment system

•  Propose a methodology that meets those challenges

•  Develop prototypes of the components that can automate methodology steps

Project Research Goals and Objectives

•  Present overview of the F2F methodology

•  Provide the situation assessment context for the paper

•  Present initial prototype details on Step 5, the Focus Algorithm

Paper Focus

The goal of the Focus Algorithm is to explicitly define and compute the value that a new piece of information can subjectively have on an operator’s current world view

Find-to-Forecast (F2F) Process

1 Find

2 Filter

3 Format

4 Fuse

5 Focus

6 Forecast

Relevant Intelligence

Normalized Intelligence

Fused Intelligence

Focused Intelligence

Predictive Intelligence

Integrated Mission Picture

Available Intelligence

Step 1–Find. F2F uses configuration files (not discussed in this paper) to find relevant information. In a US Navy engagement scenario, this might include radar, sonar, textual reports and tactical communications about US assets, enemy threats, geo locations, events, and time references.

Step 2–Filter. F2F uses the annotated tata as a basis for narrowing down the information of interest to those essential elements required to enhance situational understanding, filtering out information that is irrelevant to the task at hand based on the semantics of the mission model.

Step 3–Format. F2F uses RDF transformation capabilities to format relevant information into a normalized RDF knowledge representation. For text, this is performed using “bridge rules” to translate semantically tagged intelligence into RDF triples, structured in accordance with mission ontology constructs.

F2F Steps

Step 4–Fuse. F2F fuses related RDF triples to support enhanced reasoning over a semantically integrated knowledge base. Fusion, of course, is a very complex technical challenge. At a summary level, fusion involves connecting semantic graphs represented in the RDF triple store.

Step 5–Focus. F2F helps stakeholders to focus their attention on changing information of most value to their areas of responsibility. Using the mission ontology, our algorithm presents stakeholders with significant events, assets, locations, threats, and time lines based on operator-defined, mission-specific priorities, promoting enhanced situation understanding.

Step 6–Forecast. Various hybrid reasoning capabilities can be used on normalized, mission-relevant information to forecast possible behaviors, identify information gaps, assess trustworthiness of the sources, and project responsive courses of action. This represents future work.

F2F Steps

Situation Context: Piracy Interdiction

(from a related paper)

Mission Context Ontology

Warrior 1 Warrior 2

Bogie 1 Bogie 2

Freighter XYZ Freighter ABC

USS Ronald Reagan

threatens threatens

threatens

protects protects

commands commands

reports reports

observes observes

observes observes

Transcript Excerpt: Input Data

RDF Extracted from Messages

Knowledge extracted from message: T+16:00 Reagan, this is warrior 1. Bogie 2 just rammed bogie 1 from the side. Appears bogie 1 is dead in the water

Focus Algorithm Focusing Operator Attention on Situational Changes Most

Relevant to an Area of Responsibility

•  Purpose of this Step •  Focus user attention on relevant situational changes

•  Approach •  Build a graph of an operator’s current situational

understanding, called the context graph •  Build graph of new knowledge, called the target graph

•  Compute the semantic difference between the two

•  Focus operator attention on new events and assets most closely related to current situational knowledge

Step 5: Focus

•  Caveats •  This is initial research that will be expanded over the

next two years

•  The prototype algorithm does not handle some complexities that it will need to handle

•  The idea, however, is promising as a technique

Step 5: Focus

•  Situational Awareness Impact Subsystem (SAIS) implements the Focus Step of the F2F process •  SAIS computes the potential semantic impact of new

information on a recipient’s situational knowledge

•  Our approach was to start with a very simple graph analysis method that can be extended over time

•  Two graphs are constructed by SAIS •  Context graph Gc: operator’s situational knowledge

•  Target graph Gt: new situational knowledge

How the Focus Step Works

•  SAIS computes the semantic difference in two steps 1.  SAIS fuses the “target” graph with “context” graph, or

new knowledge with current knowledge 2.  SAIS computes the semantic value of the new (Gt)

information based on operator’s current knowledge (Gc), based on the degree of connectivity between Gc and Gt

•  Example: data about “basketball scores” is of less value to a person interested in “sculpture” than data about “museum exhibits” and we expect a “baseball graph” to be less connected to a “museum graph” than a “sculpture graph” will be

How the Focus Step Works

•  Input •  Gt is a target graph representing the new information

piece to be evaluated

•  Gc is a context graph representing the world view

•  Gc’ = Gt ∪ Gc

•  Output •  Impact (Gt, Gc) ∈ [0, 1], the impact of the new

information on the world-view

Formal Definitions

SAIS is applied to compute value of new situational knowledge.

Situational knowledge is normalized as RDF graphs.

Situational knowledge (known data) and new data arriving from various structured and unstructured sources

SAIS Processing Context

Soft DataKnown Data

RDF Graph RDF Graph

RDFizer Module

RDF Graph

“Situational Awareness Impact System”“Situational Awareness Impact System”

RDFizer Module RDFizer Module

Hard Data

Process Flow: Steps 1 - 3

•  When a new RDF triple (target assertion) overlaps with a concept known to the operator (in the concept graph), a couple of things happen: •  The new assertion get added to Gc •  Other assertions may also be added to Gc by rule •  The sum of these new assertions constitute the “new

knowledge” added to make up Gc’

•  The more the “new knowledge” is connected to “existing knowledge,” the more valuable it is to the operator.

The Concept

•  SAIS assigns a value to each new piece of information in Gt •  Value assigned is proportional to the degree of

connectedness between a new target assertion and Gc

•  Impact of the new knowledge is directly correlated to degree of connectivity of new knowledge to context graph

•  Attention should be focused on new knowledge that is highly relevant to the situational context

Value of New Information

•  A database contains records on four people •  John Smith, Rebecca Smith •  Sarah Jones, Matt Jones

•  We represent their names (given, family and full names) as individual RDF statements, as in:

[John-Smith family-name Smith] [John-Smith given-name John]

•  We have a total of 12 statements, in four graphs, representing the Context Graph of “name” knowledge

Simple Example Scenario

Context Graphs

•  New data is acquired from other sources

•  Some data may be duplicative, some data may not be related, and some data may represent new information about known entities, such as people

•  Target graphs are constructed from the data (in the first four steps of the F2F process)

•  The result is a set of target graphs Gt to compare to the graphs in Gc

New Data

Target Graphs from New Data

Rebecca Smith

•  New information has an impact on the world view, or context graph, that can be assigned a computed value based on overlapping statements

•  SAIS computes impact as follows 1.  Identify overlapping entities (vertices) in new data

•  If there is new information, impact value equals the total number of statements in the overlapping nodes (six in this example) divided by the total number of statements in all nodes (12) and the impact is .5

•  If there is no new related information, the impact is zero

2.  Revise the knowledge graph Gc to Gc’

SAIS Updates Context Graph

Revised Context Graphs

Rebecca

Rebecca Smith