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Conference ProceedingsOPEN ACCESS

Accelerating Substructure Similarity Search for Formula Retrieval

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020) 12035 LNCS 714-727
DOI: 10.1007/978-3-030-45439-5_47
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Abstract

Formula retrieval systems using substructure matching are effective, but suffer from slow retrieval times caused by the complexity of structure matching. We present a specialized inverted index and rank-safe dynamic pruning algorithm for faster substructure retrieval. Formulas are indexed from their Operator Tree (OPT) representations. Our model is evaluated using the NTCIR-12 Wikipedia Formula Browsing Task and a new formula corpus produced from Math StackExchange posts. Our approach preserves the effectiveness of structure matching while allowing queries to be executed in real-time.

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Zhong, W., Rohatgi, S., Wu, J., Giles, C. L., & Zanibbi, R. (2020). Accelerating Substructure Similarity Search for Formula Retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12035 LNCS, pp. 714–727). Springer. https://doi.org/10.1007/978-3-030-45439-5_47

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