Learning To Rank Resources with GNN

What is it about?

As the amount of content on the Internet keeps growing, it's becoming harder for traditional search engines to keep up with all the new and diverse sources of data. A lot of the information online can't even be reached by these search engines. One way to deal with this issue is by using a system called Distributed Information Retrieval (DIR). This system connects multiple sources of data and allows users to access them all together. A crucial part of DIR systems is choosing the right data sources to look for information, and there are many methods to do this. However, these methods have some limitations, mainly because they don't consider the relationships between the data sources and the search queries, or between the data sources themselves. In this research, the authors propose a new method that uses a graph neural network (GNN) to learn how to rank the data sources. This method can understand the relationships between data sources and search queries, as well as between different data sources. A language model is used to help understand the meaning of the queries and the information in the data sources. Then, a graph is built to represent these relationships and GNN is leveraged to extract useful information from the graph.

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Photo by Philip Strong on Unsplash

Photo by Philip Strong on Unsplash