Items where author is affiliated with Microsoft Research
Number of items: 4.
and Chakrabarti, Kaushik
and Chaudhuri, Surajit
and Ganti, Venkatesh
and Christian König, Arnd
and Xin, Dong Exploiting Web Search Engines to Search Structured Databases.
Web search engines often federate many user queries to relevant structured databases. For example, a product related query might be federated to a product database containing their descriptions and speciﬁcations. The relevant structured data items are then returned to the user along with web search results. However, each structured database is searched in isolation. Hence, the search often produces empty or incomplete results as the database may not contain the required information to answer the query. In this paper, we propose a novel integrated search architecture. We establish and exploit the relationships between web search results and the items in structured databases to identify the relevant structured data items for a much wider range of queries. Our architecture leverages existing search engine components to implement this functionality at very low overhead. We demonstrate the quality and efficiency of our techniques through an extensive experimental study.
and Ganti, Venkatesh
and Xin, Dong Exploiting Web Search to Generate Synonyms for Entities.
Tasks recognizing named entities such as products, people names, or locations from documents have recently received signiﬁcant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table. Prior approaches have relied on string-based similarity which only compare a candidate string and an entity it matches with. In this paper, we exploit web search engines in order to deﬁne new similarity functions. We then develop efficient techniques to facilitate approximate matching in the context of our proposed similarity functions. In an extensive experimental evaluation, we demonstrate the accuracy and efficiency of our techniques.
Bennett, Paul N.
and Maxwell Chickering, David
and Mityagin, Anton Learning Consensus Opinion: Mining Data from a Labeling Game.
We consider the problem of identifying the consensus rank- ing for the results of a query, given preferences among those results from a set of individual users. Once consensus rank- ings are identified for a set of queries, these rankings can serve for both evaluation and training of retrieval and learn- ing systems. We present a novel approach to collecting the individual user preferences over image-search results: we use a collaborative game in which players are rewarded for agree- ing on which image result is best for a query. Our approach is distinct from other labeling games because we are able to elicit directly the preferences of interest with respect to image queries extracted from query logs. As a source of rel- evance judgments, this data provides a useful complement to click data. Furthermore, the data is free of positional biases and is collected by the game without the risk of frus- trating users with non-relevant results; this risk is prevalent in standard mechanisms for debiasing clicks. We describe data collected over 34 days from a deployed version of this game that amounts to about 18 million expressed prefer- ences between pairs. Finally, we present several approaches to modeling this data in order to extract the consensus rank- ings from the preferences and better sort the search results for targeted queries.
and Kenthapadi, Krishnaram
and Mishra, Nina
and Ntoulas, Alexandros Releasing Search Queries and Clicks Privately.
The question of how to publish an anonymized search log was brought to the forefront by a well-intentioned, but privacy-unaware AOL search log release. Since then a series of ad-hoc techniques have been proposed in the literature, though none are known to be provably private. In this paper, we take a major step towards a solution: we show how queries, clicks and their associated perturbed counts can be published in a manner that rigorously preserves privacy. Our algorithm is decidedly simple to state, but non-trivial to analyze. On the opposite side of privacy is the question of whether the data we can safely publish is of any use. Our ﬁndings offer a glimmer of hope: we demonstrate that a non-negligible fraction of queries and clicks can indeed be safely published via a collection of experiments on a real search log. In addition, we select an application, keyword generation, and show that the keyword suggestions generated from the perturbed data resemble those generated from the original data.
About this site
This website has been set up for WWW2009 by Christopher Gutteridge of the University of Southampton, using our EPrints software.
Add your Slides, Posters, Supporting data, whatnots...
If you are presenting a paper or poster and have slides or supporting material you would like to have permentently made public at this website, please email
email@example.com - Include the file(s), a note to say if they are presentations, supporting material or whatnot, and the URL of the paper/poster from this site. eg. http://www2009.eprints.org/128/
It's impractical to add all the workshops at WWW2009 by hand, but if you can provide me with the metadata in a machine readable way, I'll have a go at importing it. If you are good at slinging XML, my ideal import format is visible at http://www2009.eprints.org/import_example.xml
We (Southampton EPrints Project) intend to preserve the files and HTML pages of this site for many years, however we will turn it into flat files for long term preservation. This means that at some point in the months after the conference the search, metadata-export, JSON interface, OAI etc. will be disabled as we "fossilize" the site. Please plan accordingly. Feel free to ask nicely for us to keep the dynamic site online longer if there's a rally good (or cool) use for it...
- WWW2009 EPrints supports OAI 2.0 with a base URL of http://www2009.eprints.org/cgi/oai2
- The JSON URL is http://www2009.eprints.org/cgi/json?callback=function&eprintid=number
To prevent google killing the server by hammering these tools, the /cgi/ URL's are denied to robots.txt - ask Chris if you want an exception made.
Feel free to contact me (Christopher Gutteridge) with any other queries or suggestions. ...Or if you do something cool with the data which we should link to!
These are not directly related to the EPrints set up, but may be of use to delegates.
- Social tool links
- I've put links in the page header to the WWW2009 stuff on flickr, facebook and to a page which will let you watch the #www2009 tag on Twitter. Not really the right place, but not yet made it onto the main conference homepage. Send me any suggestions for new links.