Ontology-based Software for Generating Scenarios for Characterizing Searches for Nuclear Materials* Richard C. Ward, Alexandre Sorokine, Bob Schlicher Michael Wright, Kara Kruse Oak Ridge National Laboratory Oak Ridge, TN 37831 wardrc1@ornl.gov Abstract—A software environment was created in which searcher, mobile objects, sources and other entities in the ontologies are used to significantly expand the number and scene, the scenarios can be rendered using three- variety of scenarios for special nuclear materials (SNM) dimensional rendering software such as Blender [6]. detection based on a set of simple generalized initial descriptions. A framework was built that combined advanced reasoning from ontologies with geographical and other data II. GENERAL ASSUMPTIONS sources to generate a much larger list of specific detailed descriptions from a simple initial set of user-input variables. The present version of the ODSG software is intended This presentation shows how basing the scenario generation on to simulate an urban environment that is traversed by a a process of inferencing from multiple ontologies, including a single searcher on foot carrying a gamma-ray detector in a new SNM Detection Ontology (DO) combined with data backpack. Each scenario is generated for an urban setting extraction from geodatabases, provided the desired significant defined as an area in a city and described by a user-selected variability of scenarios for testing search algorithms, including set of general descriptors. These general descriptors may unique combinations of variables not previously expected. The include: location type (e.g., “city on the East coast”), the various components of the software environment and the resulting scenarios generated will be discussed. weather (temperature, humidity, etc.), information on the background radiation environment (e.g., possible Keywords-component; ontology, software environment, presence of individuals treated with radioisotopes, presence scenario of man-made objects, industry), hypothesized illicit locations of SNM source, and the general direction and I. INTRODUCTION walking time of the searcher carrying the detector. Further, searching is assumed to be conducted only in Recently there has been considerable interest in the outdoor environment of the city with the searcher constructing computational systems that utilize ontologies in walking in non-adaptive patterns based on the shortest path a multitude of ways [1, 2]. Examples are a semantic-based to cross the search area; in this version the presence of a biosimulation modeling approach [3] that is being built on source does not alter the searcher’s path. The software ontologies of anatomy and the physics of biology and the design is flexible enough so that future versions could Gene Ontology (GO) [4] for bioinformatics. Here we account for teams of searchers and adaptive searching with present an ontology-based software framework for more complex search protocols. generating scenarios for a single searcher looking for the presence of special nuclear materials (SNM). Our software, the ontology-driven scenario generator (ODSG), will III. DEVELOPMENT OF SUPPORTING ONTOLOLGIES provide a capability to reason detailed scenario descriptions ODSG uses multiple ontologies to infer from a general from limited user-input variables and create a multiplicity of description (a list of user-input variables) to a much more scenarios with greater complexity than the initial input. The complex detailed description and generates scenarios that value to proliferation research is that this approach can be are used later to test algorithms of SNM detection. We used to generate a wide variety of scenarios, incorporating developed the SNM DO based on a multitude of sources complexities that were unobtainable from the intuitive including interviews of subject matter experts (SMEs), field heuristics, for testing detection algorithms. manuals, textbooks, and other sources. SNM DO depicts an The software system operates by first configuring an SNM detection environment the way it is perceived by the end-user application from the SNM Detection Ontology SMEs and outlines elements of the detection environment (SNM DO) and other data. Then the user selects scenario that may affect sensor readings in the opinion of the SMEs. variables and ranges as desired. Once the variables are Fig. 1 shows the general structure of the SNM DO. specified, a reverse process constructs the “data” for a series In addition to the SNM DO, several other ontologies of scenarios using ontologies of data products and aimed at depicting the latent background knowledge, were simulation models. developed. Overall ontology development methodology was Each of the resulting scenarios can be viewed on the based on Basic Formal Ontology (BFO) [7]. Several screen or encoded into XML or other formats, including ontologies were developed that describe geographic data KML [5], for further processing, and optionally converted sources, such as TIGER [8], DHS Homeland Security into a human-readable narrative description. With the Infrastructure Program (HSIP) [9], and others. Also we addition of building heights, elevations of floor levels, developed ontologies for simulation models, such as models for simulating paths of moving objects and pavement and variable that can be controlled by the end-user through the sidewalk configurations. These simulation models were GUI. Also each SNM DO entity is matched to a used during scenario generation to substitute for missing or corresponding entity in the supporting ontologies that unavailable data. SNM DO was matched with data source describes geographic datasets and simulation models and model ontologies using an intermediate ontology based available for scenario generation. The GUI generator uses on the entries commonly found in the dataset ontologies and these matches to deduce properties of an input variable such other geographic ontologies such as SWEET [10]. type (numeric, enumerated, geographic, etc.) and domain The ontologies were developed using the Simple and appropriately formats the GUI elements of that variable. Ontology Format (SOFT) [11] that provides such The end user selects the variables of interest and capabilities as visualization of ontologies in GraphViz and provides ranges of values for those variables. For example, reasoning over a hierarchy of entities and relations [12]. An the user’s selection might include geographic region, example SOFT diagram of portions of the SNM DO is population, terrain type, presence of major buildings, roads, shown in Fig. 2. bridges, etc. associated with the location, and the presence of mobile objects such as people, cars, trucks, etc. Given the user’s input, the program selects a real urban location satisfying those criteria (e.g., East coast city with hospital and university near the scenario center). Following our assumption that the scene is restricted to the plan of the urban landscape provided by maps discussed above, a few of the variables governing scenario generation are: a) the path taken by the individual with the detector (allowed areas of walkable map); b) the types of shielding associated with the buildings or structures - these could be inferred using the ontology from the building type or use (government, school, store, etc.); c) characteristics such as types of soil, types of building materials commonly used, the vegetation present and weather (humidity or rain); d) the presence of or Figure 1. A portion of the special nuclear materials detection inference of known medical sources in individuals who have ontology (SNM DO). This portion focuses on geographic features. been treated or diagnosed using medical radioisotopes; and e) the presence of mobile objects such as cars, pedestrians, etc. Variation in the range of these variables comes partly from inferencing via the ontology and partly from random sampling over assumed typical ranges. Figure 2. A portion of the SNM DO relating hospitals, procedures, We also use ontologies to reason additional data from services, medical procedures and isotopes. existing sources. For example, the possibility of finding anthropogenic radiation sources used in medical treatments can be inferred from the presence of the hospitals of certain IV. SOFTWARE ARCHITECTURE types with the search area and thus the presence of treated ODSG system architecture is built around utilizing individuals. Such radiation sources can be detected by the ontologies in various parts of its data processing cycle. The searcher. Fig. 2 illustrates one of these cases - if a hospital SNM DO and supporting ontologies are used to configure in the search area provides an oncology service that the interactive scenario generator. At the configuration relies_on ventilation/perfusion (V/Q) procedures pulmonary stage entities from the ontologies are used to link the perfusion (that uses Tc-99m) and pulmonary ventilation geodata sources and simulation models and generate the (that uses Xe-133), patients exiting this hospital might carry graphical user interface (GUI) of the end-user application. these specific medical isotopes. A SNM detection algorithm At the scenario generation stage user input is received must be able to recognize these anthropogenic background through the GUI and used to construct the data by either sources. retrieving it from the matching location in the geodatabase or running simulation models if such data are not available. VI. MOBILE OBJECTS In the scenario generation we had to deal with mobile V. USER INTERFACE objects, entities such as the searcher, pedestrians, vehicles, A web interface was developed to capture the user’s etc. that move through the scene or otherwise change as a general input descriptors for the scenario. ODSG is a web- function of time. The searcher path is accomplished by based application whose GUI is generated semi- weighting each point in a grid on the urban landscape, automatically from the SNM DO and supporting ontologies. removing any points that have weights above a defined Each entity in the SNM DO corresponds to a scenario value (for example buildings, water features, etc.) that the searcher could not traverse, and creating an undirected Table I. A Portion of the User Input Variables for Example graph. Using the A*search algorithm [13] a path is computed through this landscape of weighted values that is Number of searchers 1 the minimum path between arbitrarily selected endpoints on roads at the edge of the scene. In addition, we used Number of pedestrians 0 MASON [14], open source agent-based modeling (ABM) Number of vehicles 3 tools, to update and track the objects as they moved through General US Region New England the scene. The ODSG software provides random paths for up to ten pedestrians and ten vehicles. Type of Detector Handheld Material LaBr3 Type of Search Event-driven By protocol Near search area Railway Port VII. GEOGRAPHICAL INFORMATION SYSTEMS ODSG is a web application that uses a PostgreSQL [15] IX. CONCLUSIONS database with the PostGIS extension [16] for most of its Utilizing both domain-specific ontologies and those storage and data processing needs and Minnesota containing latent-background terminology, we have created a MapServer [17] for geographic display of the resulting software environment that generates an expanded number of scenarios. Using GIS the track of any mobile object scenarios from a general set of user input variables for purposes (searcher, pedestrian, vehicles, etc.) is easily visualized and of testing algorithms for detection of SNM. The specific correlated to the text narrative. The approach of combining ontology developed, the SNM DO, was built using subject- inferencing from ontologies within a GIS framework to matter expert knowledge of the detection process for searchers generate scenarios enhances the capability to generate and on foot in an urban setting. The detailed dependence of the visualize scenarios for evaluation of SNM detection software construction and operation on the ontologies is algorithms. The scenarios were passed to a narrative described and a specific example of the user input variables used generator where they are converted into English sentences. to create sixteen scenarios is elaborated. By using ontologies In addition, they can be delivered in XML format which both to configure the software architecture and to drive could be passed to a 3D-georenderer (Blender) for three- inferencing based on ontological reasoning, we greatly expanded dimensional display of the scene [6], or KML format to be the number and variety of scenarios generated from a single set viewed in Google Earth. of user input. Such applications show the importance of incorporating ontologies into software frameworks for VIII. RESULTS generation of scenarios for activities such as searching for nuclear materials. During the system demonstration, ODSG was used to generate about a hundred scenarios using several sets of input variables. In many cases multiple scenarios were generated from the same input data set by using iteration over the permitted ranges of variable values. All scenarios had a single searcher in the scene and many had pedestrians and/or vehicles in the scene, demonstrating the capability of adding mobile objects to the scenario generation. An example of the capability to generate multiple scenarios from a single input is the sixteen scenarios created from the user input shown in Table I. The user input for “General US Region” is New England. The user has also selected presence in or near the scene of a railway and a port. The GIS map for one of the sixteen scenarios generated (sc0126_005) is shown in Fig. 3. Each scenario displays the searcher path (dark circles) as well the track of three vehicles (squares) passing through the scene. The railway is seen in the bottom portion of Fig. 3. The combination of location and presence of various infrastructures (such as railways and ports) generates multiple output scenarios. This example demonstrates the ease with which a large set of detailed scenarios can be constructed from a much simpler set of generalized user input variables. Figure 3. Scenario sc0126_005 generated from input in Table 1. The searcher path is shown with dark circles and the vehicle tracks with squares. ACKNOWLEDGEMENTS [17] MapServer, http://mapserver.org/, 2011. * Research sponsored by U.S. Department of Energy/National Nuclear Security Administration NA-22 Simulation, Algorithms and Modeling Program under contract: OR10-Ontology Demo-PD06. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. 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