Evaluating spreading activation for soft information fusion

A soft-information fusion process produces refined estimates of soft-information, such as natural language messages. Information resulting from a soft-information process can be used to retrieve related, relevant information from background (a-priori) knowledge sources using contextual “cues” contained in those messages, a process we call “Context-Based Information Retrieval (CBIR)”. These retrieval results can be used to aid further understanding, and other fusion operations (e.g., data association). CBIR process performance is dependent on the choice of algorithms and parameters for those algorithms, and it is crucial that these are chosen appropriately for the problem domain the CBIR algorithm is used to aid. In this paper an f-measure evaluation of two spreading activation algorithms and their parameters is given using a soft information fusion process in a counterinsurgency domain. This evaluation takes place in two phases. The first phase executes the algorithms over a range of values in order to determine how those parameters affect the performance of the algorithms, and to set these parameters for future use. The second phase compares the results of these algorithms using the parameter settings learned from the first phase.