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

公司、大学和其他机构中存在大量的电子文档集合。信息搜索的两种常见形式是搜索和探索(浏览)集合。然而，在这样的集合中找到相关信息可能很困难。这适用于具有表达不佳和不太具体的查询的搜索，以及对于用户可能没有特定搜索目标的浏览。用户的信息搜索可以由关于搜索域的结构良好的知识来辅助，我们称之为域模型。领域模型实际上是一种结构，人们将其强加于数据以支持他们寻找信息。现在，我们可以直接从域模型派生查询修改或浏览建议。为了用一个现实的例子来说明这一点，假设埃塞克斯大学内部网的一个用户从搜索Union开始。该查询将触发搜索系统提供诸如学生会和欧洲联盟之类的查询精细化术语。事实上，所有本地网站、内部网和类似的集合都包含大量有价值的领域知识，这些知识都是隐式编码的。挑战是自动获取领域模型，然后通过帮助用户执行搜索或浏览等信息搜索任务来使其可用。一个更大的挑战是自动发展这个领域模型。这一建议的新颖之处在于通过观察用户对领域知识的使用并相应地对其进行更改来进化自动获取的领域知识。我们假设提交的用户查询和用户与搜索系统之间的对话可以被监控并用于随着时间的推移改进领域模型。用户对查询修改建议的选择被视为相关性的指示。这个项目旨在开发和评估自动构建的领域模型以适应用户搜索或浏览行为的总体情况。将在若干领域，包括埃塞克斯大学、开放大学和我们的工业伙伴的内联网上，对开发的方法在两种信息搜索方案--即互动搜索和浏览--中进行应用和大规模评估。

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.