Towards Adaptive Information Retrieval - Step 1: Collecting Real Data
This event took place on Friday 25 May 2007 at 11:30
Udo Kruschwitz University of Essex, Department of Computer Science, Wivenhoe Park, Colchester
One of the most exciting areas of research in search engine technology and information retrieval is the move towards "adaptive" search systems. A particularly promising aspect of this wide field is to move log analysis right in the centre of attention. The challenge is to exploit the user interaction (as recorded in the log files) to make the search system adapt to the users' search behaviour. Instead of looking at the Web in general we are interested in smaller document collections with a more limited range of topics.
We are focusing on a search paradigm where automatically extracted domain knowledge is incorporated in a simple dialogue system in order to assist users in the search process. The challenge is to mine the log files in order to automatically improve the suggestions made by the system, in other words to "adapt" to the users' search behaviour. We are interested in a specific aspect of this search behaviour, namely the selection of query modification terms which provides us with "implicit feedback" from the users and should be sufficient to come up with a model to automatically adjust the domain knowledge without having to rely on other forms of explicit or implicit user feedback.
This whole process requires real data. We have made a start by running a prototype of our own search system that combines a standard search engine with automatically extracted domain knowledge. The system has been running on the University of Essex intranet for nearly a year now and we have collected more than 35,000 queries. The log files we keep collecting are an extremely valuable resource because they are a reflection of real user interests (different to TREC like scenarios which are always somewhat artificial). The data collected so far are a justification for a system that guides a user in the search process: more than 10% of user queries are query modification steps, i.e. the user either replaces the initial query or adds terms to the query to make it more specific. Adding a term happens more often than replacing the query with a completely new one. We also observe that a user is more likely to select one of the suggestions made by our search engine than modifying the query manually.
The talk will focus on our ongoing research and present some analysis of
the log files collected so far.
This event took place on Friday 25 May 2007 at 11:30
One of the most exciting areas of research in search engine technology and information retrieval is the move towards "adaptive" search systems. A particularly promising aspect of this wide field is to move log analysis right in the centre of attention. The challenge is to exploit the user interaction (as recorded in the log files) to make the search system adapt to the users' search behaviour. Instead of looking at the Web in general we are interested in smaller document collections with a more limited range of topics.
We are focusing on a search paradigm where automatically extracted domain knowledge is incorporated in a simple dialogue system in order to assist users in the search process. The challenge is to mine the log files in order to automatically improve the suggestions made by the system, in other words to "adapt" to the users' search behaviour. We are interested in a specific aspect of this search behaviour, namely the selection of query modification terms which provides us with "implicit feedback" from the users and should be sufficient to come up with a model to automatically adjust the domain knowledge without having to rely on other forms of explicit or implicit user feedback.
This whole process requires real data. We have made a start by running a prototype of our own search system that combines a standard search engine with automatically extracted domain knowledge. The system has been running on the University of Essex intranet for nearly a year now and we have collected more than 35,000 queries. The log files we keep collecting are an extremely valuable resource because they are a reflection of real user interests (different to TREC like scenarios which are always somewhat artificial). The data collected so far are a justification for a system that guides a user in the search process: more than 10% of user queries are query modification steps, i.e. the user either replaces the initial query or adds terms to the query to make it more specific. Adding a term happens more often than replacing the query with a completely new one. We also observe that a user is more likely to select one of the suggestions made by our search engine than modifying the query manually.
The talk will focus on our ongoing research and present some analysis of
the log files collected so far.
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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
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