Fusion of observational data acquired by human observers and couched in linguistic form is a modern-day challenge for the fusion community. This paper describes a basic research effort examining various strategies for associating and exploiting such data for intelligence analysis purposes. An overall approach is described that involves Latent Semantic Analysis, Inexact Graph Matching, formal ontology development, and Social Network Analyses. Not all the methods have yet been employed but the exploitation of the developed ontology and graphical techniques have been implemented in a working prototype and preliminary results have shown promise. Planned future research will complete the implementation of the methods described herein and add yet further enhancements.

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Graph matching applications in high level information fusion

Graphical methods for real-time fusion and estimation with soft message data

Social media data are amenable to representation by directed graphs. A node represents an entity in the social network such as a person, organization, location, or event. A link between two nodes represents a relationship such as communication, participation, or financial support. When stored in a database, these graphs can be searched and analyzed for occurrences of various subgraph patterns of nodes and links. This paper describes an interactive visual interface for constructing subgraph patterns called the Graph Matching Toolkit (GMT). GMT searches for subgraph patterns using the Truncated Search Tree (TruST) graph matching algorithm. GMT enables an analyst to draw a subgraph pattern and assign labels to nodes and links using a mouse and drop-down menus. GMT then executes the TruST algorithm to find subgraph pattern occurrences within the directed graph. Preliminary results using GMT to analyze a simulated collection of text communications containing a terrorist plot are reported.

Discovering patterns in social networks with graph matching algorithms

Ontologies are being used increasingly in fusion applications, particularly for higher-level fusion, where data must often be understood relationally. This research presents a methodology for utilizing ontologies to enhance the process of graph matching in fusion applications, particularly those associated with soft data (e.g., linguistic data existing in things such as intelligence messages). This paper presents some of the considerations and challenges associated with merging the technologies of ontologies and graph matching, as well as some preliminary research findings that show the effectiveness of using ontologies to enhance the matching capabilities of target graphs (as relational items of interest) against larger data graphs.

Enhancing graph matching techniques with ontologies

In a counterinsurgency environment, patrol or spot reports and human observations stream into a soft information fusion node in arbitrary temporal order. This paper discusses the use of discrete temporal features and interval logic for temporal alignment in soft information. This aligned sequence can be presented to a graph matching method for situational understanding. Temporal transformations will provide a “present” tense time for incoming messages, which will be represented in a graphical form. The use of the OWL-Time ontology, along with a rule based inference engine JENA, enables messages to be placed in linear order. Given such information, it is feasible to match given scenarios of interest which contain temporal specific requirements. This is accomplished using a temporal version of graph matching. Extensions on this research will include considerations of uncertainty in time through fuzzy representations.

Additional excerpts

Temporal alignment in soft information processing

For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching.

methods

/pdf/expert-system-for-crop-disease-based-on-graph-pattern-3jfsix1c59.pdf

Graph matching applications in high level information fusion

Citations

Graphical methods for real-time fusion and estimation with soft message data

Discovering patterns in social networks with graph matching algorithms

Enhancing graph matching techniques with ontologies

Temporal alignment in soft information processing

Expert System for Crop Disease based on Graph Pattern Matching: A proposal

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