  As the content on the Internet continues to grow, many new dynamically
changing and heterogeneous sources of data constantly emerge. A conventional
search engine cannot crawl and index at the same pace as the expansion of the
Internet. Moreover, a large portion of the data on the Internet is not
accessible to traditional search engines. Distributed Information Retrieval
(DIR) is a viable solution to this as it integrates multiple shards (resources)
and provides a unified access to them. Resource selection is a key component of
DIR systems. There is a rich body of literature on resource selection
approaches for DIR. A key limitation of the existing approaches is that they
primarily use term-based statistical features and do not generally model
resource-query and resource-resource relationships. In this paper, we propose a
graph neural network (GNN) based approach to learning-to-rank that is capable
of modeling resource-query and resource-resource relationships. Specifically,
we utilize a pre-trained language model (PTLM) to obtain semantic information
from queries and resources. Then, we explicitly build a heterogeneous graph to
preserve structural information of query-resource relationships and employ GNN
to extract structural information. In addition, the heterogeneous graph is
enriched with resource-resource type of edges to further enhance the ranking
accuracy. Extensive experiments on benchmark datasets show that our proposed
approach is highly effective in resource selection. Our method outperforms the
state-of-the-art by 6.4% to 42% on various performance metrics.
