| Hahn-Ming Lee|
(Alternative names for this author)
|Co-authors||Cheng-Yu Lu, Das Sarma A., Fei Wu, Fu-Yuan Hsu, Gupta N., Halevy A., Jan-Ming Ho, Jen-Ming Chung, Lujun Fang, Shou-Wei Ho, Xin R., Yu C.|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Hahn-Ming Lee is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Finding related tables||Data integration
|Proceedings of the ACM SIGMOD International Conference on Management of Data||English||2012||We consider the problem of finding related tables in a large corpus of heterogenous tables. Detecting related tables provides users a powerful tool for enhancing their tables with additional data and enables effective reuse of available public data. Our first contribution is a framework that captures several types of relatedness, including tables that are candidates for joins and tables that are candidates for union. Our second contribution is a set of algorithms for detecting related tables that can be either unioned or joined. We describe a set of experiments that demonstrate that our algorithms produce highly related tables. We also show that we can often improve the results of table search by pulling up tables that are ranked much lower based on their relatedness to top-ranked tables. Finally, we describe how to scale up our algorithms and show the results of running it on a corpus of over a million tables extracted from Wikipedia.||0||0|
|Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network||Expert-finding