Efficient edit distance based string similarity search using deletion neighborhoods
Proceedings of the Joint EDBT/ICDT 2013 Workshops
A partition-based method for string similarity joins with edit-distance constraints
ACM Transactions on Database Systems (TODS)
A human-machine method for web table understanding
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Efficient error-tolerant query autocompletion
Proceedings of the VLDB Endowment
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Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies.