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

使用Google很容易。使用Google搜索是另一回事。几十年来，信息检索取得了重大进展。然而，IR远未解决问题，仍存在许多挑战。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。给定查询的结果通常是相同的，与用户或用户发出请求的上下文无关。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。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Google和Bing）和科学文献搜索引擎（如PubMed和ACM数字图书馆）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模型提供了很少的语义上下文的理解用户的信息需求，这提出了一个关键的挑战，查询和文档之间的搜索不匹配。理想情况下，人们希望看到查询和文档彼此匹配，如果它们是主题相关的或语义接近的话。因此，根据用户对集合中的文档的理解将语义上下文集成到IR系统中（例如，经由利用自然语言处理技术）将改进IR性能。第四，情境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发展，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。 该研究的长期目标是克服现有IR方法的局限性，并正式开发一种新的检索范式，称为上下文敏感和任务感知的大数据信息搜索。特别是，（1）我们将开发新的理论模型来自动分析大量文本数据，以有效地提取知识，并准确地执行语义和精确匹配;（2）我们将开发一个新的检索框架，用于捕获大数据中的用户信息和搜索上下文;（3）我们将开发新的基于任务的上下文相关信息检索方法，以优化整个检索会话的长期检索效用。

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.