Attribute-Value-Level Matching
Zhe Lim, Benjamin I. P. Rubinstein · Minerva Access (University of Melbourne) · 2014
We explore normalisation of attribute values across multiple data sources, where attributes could be categorical, numerical, otherwise and could multi-valued. For example the genre(s) of a movie or the cuisine(s) of a restaurant. To benchmark our statistical approach (based on Canonical Correlation Analysis) against baselines, we crawled and prepared two datasets of four sources each on movie genres (7852 records across IMDB, Rotten Tomatoes, The Movie DB, Yahoo! Movies) and restaurant cuisines (3120 records across Factual, Foursquare, Google Places, Yelp). After performing a simple entity resolution (record linkage) to align matched records across sources, we extracted the attribute to be matched (genres or cuisines respectively). The data here represents keys to the original records, plus attribute values, all in entity-aligned order. Finally, we also used Amazon Mechanical Turk to produce human judgments to evaluate the attribute-value matchings. More details can be found in the download READMEs and the paper.