Efficient Semantic Similarity Search over Spatio-textual Data
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https://hdl.handle.net/11250/3121219Utgivelsesdato
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Advances in Database Technology - Volume 26 Proceedings 26th International Conference on Extending Database Technology ( EDBT 2023 )Sammendrag
In this paper, we address the problem of semantic similarity
search over spatio-textual data. In contrast with most existing
works on spatial-keyword search that rely on exact matching
of query keywords to textual descriptions, we focus on seman-
tic textual similarity using word embeddings, which have been
shown to capture semantic similarity exceptionally well in prac-
tice. To support efficient 𝑘-nearest neighbor (𝑘-NN) search over
a weighted combination of spatial and semantic dimensions, we
propose a novel indexing approach (called CSSI) that ensures
correctness of results, alongside its approximate variant (called
CSSIA) that introduces a small amount of error in exchange for im-
proved performance. Both variants are based on a hybrid cluster-
ing scheme that jointly indexes the spatial and textual/semantic
information, achieving high pruning percentages and improved
performance and scalability