An F-measure for context-based information retrieval

Computationally expensive processes, such as deductive reasoners, can suffer performance issues when they operate over large-scale data sets. The optimal procedure would allow reasoners to only operate on that information that is relevant. Procedures that approach such an ideal are necessary to accomplish the goal of commonsense reasoning, which is to endow an agent with enough background knowledge to behave intelligently. Despite the presence of some procedures for accomplishing this task one question remains unanswered: How does one measure the performance of procedures that bring relevant information to bear in KR systems This paper answers this question by introducing two methods for measuring the performance of context-based information retrieval processes in the domain of KR systems. Both methods produce an f-measure as a result. These methods are evaluated with examples and discussion in order to determine which is more effective. Uses of these measures are also discussed.