Context-sensitive and task-oriented adaptive information retrieval

上下文敏感和面向任务的自适应信息检索

• 批准号: 298236-2007
• 负责人: Huang, Xiangji(Jimmy)
• 金额: $ 1.31万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=395990
• 关键词: Context; sensitive; task; oriented; adaptive

Using Google is easy; finding information using Google is another matter. Over the past decades, significant progress has been made in Information Retrieval (IR). However, many challenges remain. First, most Web search engines take a short text query as input and output a ranked list of documents. The retrieval decision is made primarily based on the current query and document collection. For example, a person may use "IRIX" to mean Information Retrieval in Context at one time, but IRIX operating systems at another time. It is impossible for the current search engines to distinguish these two cases because the user's search history and context are not considered. Second, IR is, in general, an interactive process. With the current document-centered retrieval paradigm, interactive retrieval is treated as a sequence of independent simple retrieval decision-making steps. However, it has been brought into attention that analysis of task-oriented user sessions provides useful insight into the query behavior of the users. Third, most of present IR systems including general search engines (e.g. Google) and scientific literature search engines (e.g. PubMed) use keywords to query and index documents, which provides little semantic context for the understanding of the user's information needs. Thus, the integration of semantic context according to the user's information need and the user's understanding of the documents in the collection into IR systems will definitely improve the IR performance. However, how to encode and use semantic context evidence for the benefit of retrieval is a difficult problem.

使用谷歌很容易;使用谷歌查找信息是另一回事。在过去的几十年里，信息检索（IR）取得了重大进展。然而，仍然存在许多挑战。首先，大多数Web搜索引擎将短文本查询作为输入，并输出文档的排名列表。检索决策主要基于当前查询和文档集合。例如，一个人可能在某个时候使用“IRIX”来表示上下文中的信息检索，但在另一个时候使用IRIX操作系统。目前的搜索引擎不可能区分这两种情况，因为没有考虑用户的搜索历史和上下文。第二，一般来说，IR是一个互动的过程。在当前以文档为中心的检索范式中，交互式检索被视为一系列独立的简单检索决策步骤。然而，它已经引起注意，面向任务的用户会话的分析提供了有用的洞察用户的查询行为。第三，大多数现有的IR系统，包括通用搜索引擎（如谷歌）和科学文献搜索引擎（如PubMed）使用关键字来查询和索引文档，这提供了很少的语义上下文的理解用户的信息需求。因此，根据用户的信息需求和用户对集合中文档的理解将语义上下文集成到IR系统中，必将提高IR性能。然而，如何编码和使用语义上下文证据以利于检索是一个难题。