Crowdsourcing-Assisted Query Structure Interpretation / 2092
Jun Han, Ju Fan, Lizhu Zhou

Structured Web search incorporating data from structured sources into search engine results has attracted much attention from both academic and industrial communities. To understand user's intent, query structure interpretation is proposed to analyze the structure of queries in a query log and map query terms to the semantically relevant attributes of data sources in a target domain. Existing methods assume all queries should be classified to the target domain, and thus they are limited when interpreting queries from different domains in real query logs. To address the problem, we introduce a human-machine hybrid method by utilizing crowdsourcing platforms. Our method selects a small number of query terms and asks the crowdsourcing workers to interpret them, and then infers the interpretations based on the crowdsourcing results. To improve the performance, we propose an iterative probabilistic inference method based on a similarity graph of query terms, and select the most useful query terms for crowdsourcing by considering their domain-relevance and gained benefit. We evaluate our method on a real query log, and the experimental results show that our method outperforms the state-of-the-art method.