Spectral clustering and query expansion using embeddings on the graph-based extension of the set-based information retrieval model

This paper presents a straightforward yet novel approach to enhance graph-based information retrieval models, by calibrating the relationships between node terms, leading to better evaluation metrics at the retrieval phase, and by reducing the total size of the graph. This is achieved by integrating spectral clustering, embedding-based graph pruning and term re-weighting. Spectral clustering assigns each term to a specific cluster, allowing us to propose two pruning methods: out-cluster and in-cluster pruning based on node similarities. In-cluster pruning refers to pruning edges between terms within the same cluster, while out-cluster pruning refers to edges that connect different clusters. Both methods utilize spectral embeddings to assess node similarities, resulting in more manageable clusters termed concepts. These concepts are likely to contain semantically similar terms, with each term's concept defined as the centroid of its cluster. We show that this graph pruning strategy significantly enhances the performance and effectiveness of the overall model, reducing, at the same time, its graph sparsity. Moreover, during the retrieval phase, the conceptually calibrated centroids are used to re-weight terms generated by user queries, and the precomputed embeddings enable efficient query expansion through a k-Nearest Neighbors (K-NN) approach, offering substantial enhancement with minimal additional time cost. To the best of our knowledge, this is the first application of spectral clustering and embedding-based conceptualization to prune graph-based IR models. Our approach enhances both retrieval efficiency and performance while enabling effective query expansion with minimal additional computational overhead. Our proposed technique is applied across various graph-based information retrieval models, improving evaluation metrics and producing sparser graphs.