Refining Relation Identification by Combining Soft and Sensor Data

In recent years homeland security is becoming increasingly sensitive to threats posed by the tactics of subversive groups or individuals with malicious intent. Networked groups and organizations leverage various means of communication, ranging from simple phone calls to more sophisticated forms of collaborations. Such data provide a rich collection of evidence from which to infer relationships of individuals or even the structure of networks and organizations. This paper describes an approach developed to combine information from disparate data sources in order to identify relevant relationships in heterogeneous environments. The objective is to discover entities and the relationships they share by a joint combination of sensor and soft data. By analyzing patterns of sensor-based communications and content of reports, the solution highlights entity associations which are indicative of social relationship. First, the approach identifies a set of entity relations thanks to sensor data. Then, association rules extract relations from texts by using a combination of part of speech features provided by natural language processing tools and the entity type, as labeled by a domain ontology. The overall approach is designed for domains such as intelligence analysis, where the analyst attempts to build a picture of relations holding between individuals by combining sensor streams that can refer to real-world ongoing events and more complex reports provided by human sources. Results are evaluated over a heterogeneous data set and experiments show that the combination of soft and hard data outperforms individual approaches in terms of recall.