Automatic Adaptation of Knowledge Structures for Assisted Information Seeking (AutoAdapt)

Lead Research Organisation: University of Essex
Department Name: Computer Sci and Electronic Engineering

Abstract

A massive number of electronic document collections exist within companies, universities and other institutions. Two common forms of information seeking are searching and exploring (browsing) the collections. However, finding relevant information within such collections can be difficult. This is true for searching with poorly formulated and less specific queries as well as for browsing where the user may not have a specific target to search. The user's information seeking could be assisted by well-structured knowledge about the search domain, which we refer to as domain model. A domain model is effectively a structure that people impose on data to support them in information seeking. We can now derive query modification or browsing suggestions directly from the domain model. To illustrate the point using a realistic example, assume a user of the University of Essex intranet started by searching for union . This query would trigger the search system to offer query refinement terms such as students union and european union . Indeed, all local Web sites, intranets and similar collections do contain a huge amount of valuable domain knowledge that is encoded implicitly. The challenge is to automatically acquire a domain model and then make it usable by assisting users in information seeking tasks such as searching or browsing. An even bigger challenge is to evolve this domain model automatically. The novelty of this proposal lies in evolving automatically acquired domain knowledge by observing users' usage of it and altering it accordingly. We hypothesize that the submitted user queries and the dialogues between users and search system can be monitored and used to improve the domain model over time. A user's selection of a query modification suggestion is taken as an indication of relevance. This can then be used to update the domain knowledge and thus help the next user with a similar query by presenting updated query modification suggestions.This project aims to develop and evaluate methods for adapting automatically constructed domain models to the population of users' search or browsing behaviour. Application and large-scale evaluation of the developed methods in two information seeking scenarios - namely, interactive search and browsing - will be performed on a number of domains including the intranets of the Essex University, the Open University and our industrial partners.

Publications

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Adeyanju I (2011) RGU-ISTI-essex at TREC 2011 session track in NIST Special Publication

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Albakour M (2011) University of essex at the TREC 2011 session track in NIST Special Publication

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Albakour M (2011) University of essex at log CLEF 2011: Studying query refinement in CEUR Workshop Proceedings

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Albakour M (2011) An adaptive search framework for intranets in LWA 2011 - Technical Report of the Symposium "Lernen, Wissen, Adaptivitat - Learning, Knowledge, and Adaptivity 2011" of the GI Special Interest Groups KDML, IR and WM

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Albakour M (2010) University of essex at the TREC 2010 session track in NIST Special Publication

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Clark M (2014) You have e-mail, what happens next? Tracking the eyes for genre in Information Processing & Management

 
Description The research conducted within this project led to a set of methodologies to automatically extract knowledge from document collections and user navigation/search log data resulting in structures that resemble profiles or domain models for the collections at hand. These models have been extensively tested, evaluated and compared against state-of-the-art baseline approaches. The findings have been reported in the top research outlets of the discipline.
Exploitation Route The methods should generally be applicable to anyone facing the problem of searching or navigating a document collection. All methods have been made available so that they can easily be replicated by anyone interested.
Sectors Digital/Communication/Information Technologies (including Software)