Using Ant Colony Optimization for Results Merging in Federated Search

Abstract Federated search routes the user's search query to multiple component collections and presents a merged results list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results in ranked order. To address this issue, most existing approaches merged the returned results by changing the document ranks to their ranking scores or downloading the documents and computing their relevance score at query time. However, these approaches are less efficient as the former suffer from limited efficiency of results merging due to negligible overlapping documents among the component collections, and the latter is resource intensive. This research addresses this problem by proposing a new method that extracts features of both the documents and component collections from the available information provided by the collections at querying time. Next, each document and its collection features are exploited together to establish the document relevance score. The ant colony optimization is then used for information foraging to create a merged results list. The empirical results on a real-world dataset demonstrate significant improvements by the proposed approach over baseline approaches.