Mining query log graphs towards a query folksonomy
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- TL;DR
- Web searching generates implicit knowledge as people interact with and click on retrieved documents.
- Problem
- A key challenge is extracting semantic relations between queries and their terms from large-scale query logs.
- Method
- The paper presents an approach for query contextualization by associating tags with queries to build query folksonomies, even when tags do not appear directly in the query text.
- Results
- The method relies on analyzing large query log-induced graphs, specifically click-induced graphs.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- Experiments with real-world data demonstrate that the inferred query folksonomies provide valuable insights into semantic relations among queries and web user intent.
- Applications
- Not specified in the abstract.
- Topics
- Query logs, folksonomies, click-induced graphs, semantic relations, web user intent
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
SUMMARY The human interaction through the web generates both implicit and explicit knowledge. An example of an implicit contribution is searching, as people contribute with their knowledge by clicking on retrieved documents. When this information is available, an important and interesting challenge is to extract relations from query logs, and, in particular, semantic relations between queries and their terms. In this paper, we present and discuss results on query contextualization through the association of tags to queries, that is, query folksonomies. Note that tags may not even occur within the query. Our results rely on the analysis of large query log induced graphs, namely click induced graphs. Results obtained with real data show that the inferred query folksonomy provide interesting insights both on semantic relations among queries and on web users intent.Copyright © 2011 John Wiley & Sons, Ltd.