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Given a query, unlike traditional IR that finds relevant documents orentities, in this work, we focus on retrieving both entities and theirconnections for insightful knowledge summarization. For example, given a query"computer vision" on a CS literature corpus, rather than returning a list ofrelevant entities like "cnn", "imagenet" and "svm", we are interested in theconnections among them, and furthermore, the evolution patterns of suchconnections along particular ordinal dimensions such as time. Particularly, wehope to provide structural knowledge relevant to the query, such as "svm" isrelated to "imagenet" but not "cnn". Moreover, we aim to model the changingtrends of the connections, such as "cnn" becomes highly related to "imagenet"after 2010, which enables the tracking of knowledge evolutions. In this work,to facilitate such a novel insightful search system, we propose\textsc{SetEvolve}, which is a unified framework based on nonparanomalgraphical models for evolutionary network construction from large text corpora.Systematic experiments on synthetic data and insightful case studies onreal-world corpora demonstrate the utility of \textsc{SetEvolve}.

Query-Specific Knowledge Summarization with Entity Evolutionary Networks