Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Sriram Srinivasan, Sourangshu Bhattachaya

Domain Match is an Internet monetization product offered by web companies like Yahoo! The product offers display of ads and search results, when a user requests a webpage from a domain which is non-existent or does not have any content. This product earns significant amount of advertising revenue for major internet companies like Yahoo! Hence it is an important product receiving millions of queries per day. Domain Match (DM) works by tokenizing the input domains and sub-folders into keywords and then displaying ads and search results queried on the keywords. In this poster, we describe a machine learning based solution, which automatically learns to tokenize new domains, given a training dataset containing a set of domains and their tokenizations. We use positional frequency and parts of speech as features for scoring tokens. Tokens are scored combined using various scoring models. We compare two ways of training the models: a simple gain function based training and a large margin training. Experimental results are encouraging.

Learning to Tokenize Web Domains was presented at this event.

Keywords: World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/poster/learning-to-tokenize-web-domains

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