Top-k String Auto-Completion with Synonyms
This addresses the limitation of prefix-only auto-completion in real applications where entities have synonyms, offering an incremental improvement for information systems.
The paper tackles the problem of auto-completion by incorporating synonyms and abbreviations, proposing trie-based algorithms that achieve retrieval times of about a microsecond per completion with small space overhead (160-200 bytes per string) on large-scale datasets.
Auto-completion is one of the most prominent features of modern information systems. The existing solutions of auto-completion provide the suggestions based on the beginning of the currently input character sequence (i.e. prefix). However, in many real applications, one entity often has synonyms or abbreviations. For example, "DBMS" is an abbreviation of "Database Management Systems". In this paper, we study a novel type of auto-completion by using synonyms and abbreviations. We propose three trie-based algorithms to solve the top-k auto-completion with synonyms; each one with different space and time complexity trade-offs. Experiments on large-scale datasets show that it is possible to support effective and efficient synonym-based retrieval of completions of a million strings with thousands of synonyms rules at about a microsecond per-completion, while taking small space overhead (i.e. 160-200 bytes per string). The source code of our experiments can be download at: http://udbms.cs.helsinki.fi/?projects/autocompletion/download .