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

用于辅助信息搜索的知识结构自动适应（AutoAdapt）

• 批准号: EP/F035357/1
• 负责人: Udo Kruschwitz
• 金额: $ 35.46万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF035357%2F1
• 关键词: Automatic; Adaptation; Knowledge; Structures; Assisted

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.

大量的电子文档收藏存在于公司、大学和其他机构中。信息查找的两种常见形式是搜索和浏览馆藏。然而，在这样的集合中找到相关信息可能很困难。这适用于格式不佳和不太具体的查询的搜索，以及用户可能没有特定搜索目标的浏览。搜索领域的知识结构良好，可以帮助用户进行信息搜索，我们称之为领域模型。领域模型实际上是人们强加给数据的一种结构，以支持他们进行信息查找。我们现在可以直接从域模型中获得查询修改或浏览建议。为了使用一个实际的示例说明这一点，假设埃塞克斯大学内部网的一个用户首先搜索union。该查询将触发搜索系统提供查询细化术语，如students union和european union。实际上，所有本地Web站点、内部网和类似的集合都包含大量隐式编码的有价值的领域知识。挑战是自动获取领域模型，然后通过帮助用户进行信息查找任务（如搜索或浏览）使其可用。一个更大的挑战是自动地发展这个领域模型。该方案的新颖之处在于，通过观察用户对领域知识的使用情况，自动进化获取的领域知识，并对其进行相应的修改。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视，并用于随着时间的推移改进域模型。用户对查询修改建议的选择被视为相关性的指示。然后可以使用它来更新领域知识，从而通过提供更新的查询修改建议来帮助下一个用户进行类似的查询。该项目旨在开发和评估使自动构建的领域模型适应用户搜索或浏览行为的方法。将在埃塞克斯大学、开放大学和我们的工业合作伙伴的内部网等多个领域对开发的方法在两种信息寻找场景（即交互式搜索和浏览）中的应用和大规模评估。

项目成果

Adaptation of the concept hierarchy model with search logs for query recommendation on intranets

• DOI: 10.1145/2348283.2348288
• 发表时间: 2012-08
• 作者: I. Adeyanju;D. Song;M. Albakour;Udo Kruschwitz;A. Roeck;Maria Fasli

RGU-ISTI-essex at TREC 2011 session track

RGU-ISTI-essex 在 TREC 2011 会议轨道上

• 发表时间: 2011
• 期刊: NIST Special Publication
• 作者: Adeyanju I

University of essex at log CLEF 2011: Studying query refinement

埃塞克斯大学记录 CLEF 2011：研究查询细化

• 发表时间: 2011
• 期刊: CEUR Workshop Proceedings
• 作者: Albakour M

University of essex at the TREC 2011 session track

埃塞克斯大学 TREC 2011 会议轨道

• 发表时间: 2011
• 期刊: NIST Special Publication
• 作者: Albakour M

Learning from users' querying experience on intranets

借鉴用户内网查询体验

• DOI: 10.1145/2187980.2188197
• 发表时间: 2012
• 作者: Adeyanju I