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

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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.