Searching and Analyzing Big Data: Context-sensitive and Task-aware Approaches

搜索和分析大数据：上下文敏感和任务感知的方法

• 批准号: RGPIN-2020-07157
• 负责人: Huang, Jimmy
• 金额: $ 4.66万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718720
• 关键词: Searching; Analyzing; Big; Data; Context

Using Google is easy. Finding information using Google is another matter. Over the decades, significant progress has been made in Information Retrieval (IR). However, IR is far from a solved problem and 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. The results for a given query are usually identical, independent of the user or the context in which the user made the request. 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 Web search systems to distinguish these two cases because the user's search context is 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 Bing) and scientific literature search engines (e.g. PubMed and ACM Digital Library) use keywords to query and index documents. However, this traditional keyword based IR model provides little semantic context for the understanding of user information needs, which poses a critical challenge of mismatch between query and document in search. Ideally, one would like to see query and document match with each other, if they are topically relevant or semantically close. Thus, the integration of semantic context according to the user's understanding of the documents in the collection into IR systems (e.g. via utilizing natural language processing techniques) will improve the IR performance. Fourth, context-sensitive and task-oriented approaches are even more challenging to implement with huge amounts of data accumulated every day. With the availability of deep learning and data analyzing techniques, context-sensitive and task-oriented approaches become more feasible for semantic-based matching. The long-term objective of the proposed research is to overcome the limitations of the existing IR methods and formally develop a new retrieval paradigm called context-sensitive and task-aware information search for big data. In particular, (1) we will develop novel theoretical models to automatically analyze text data with very large quantity to efficiently extract knowledge and perform both semantic and exact matching accurately; (2) we will develop a new retrieval framework for capturing user information and search context in big data; (3) we will develop novel task-based retrieval methods for context-sensitive information retrieval to optimize the long-term retrieval utility over an entire retrieval session.

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（Information Retrieval， IR）取得了显著的进步。然而，国际关系问题远未解决，仍然存在许多挑战。首先，大多数Web搜索引擎将一个简短的文本查询作为输入，并输出一个文档排序列表。检索决策主要基于当前查询和文档集合。给定查询的结果通常是相同的，独立于用户或用户发出请求的上下文。例如，一个人可能在某个时间使用“IRIX”表示上下文中的信息检索，但在另一个时间使用IRIX操作系统。目前的Web搜索系统不可能区分这两种情况，因为没有考虑用户的搜索上下文。其次，IR通常是一个互动的过程。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，人们已经注意到，对面向任务的用户会话的分析提供了对用户查询行为的有用洞察。第三，目前大多数IR系统，包括通用搜索引擎（如b谷歌和Bing）和科学文献搜索引擎（如PubMed和ACM数字图书馆），都使用关键字来查询和索引文档。然而，这种传统的基于关键字的IR模型为理解用户信息需求提供了很少的语义上下文，这给搜索中的查询和文档不匹配带来了严峻的挑战。理想情况下，如果查询和文档在主题上相关或语义上接近，人们希望看到它们相互匹配。因此，根据用户对集合中文档的理解将语义上下文集成到IR系统中（例如，通过利用自然语言处理技术）将提高IR性能。第四，上下文敏感和面向任务的方法在每天积累大量数据的情况下实施起来更具挑战性。随着深度学习和数据分析技术的出现，上下文敏感和面向任务的方法对于基于语义的匹配变得更加可行。