DeepAI AI Chat
Log In Sign Up

Query-Specific Knowledge Summarization with Entity Evolutionary Networks

by   Carl Yang, et al.
University of Illinois at Urbana-Champaign

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query "computer vision" on a CS literature corpus, rather than returning a list of relevant entities like "cnn", "imagenet" and "svm", we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as "svm" is related to "imagenet" but not "cnn". Moreover, we aim to model the changing trends 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 SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of SetEvolve.


page 1

page 2

page 3

page 4


Semantic Search using Spreading Activation based on Ontology

Currently, the text document retrieval systems have many challenges in e...

Time-Aware and Corpus-Specific Entity Relatedness

Entity relatedness has emerged as an important feature in a plethora of ...

Notable Characteristics Search through Knowledge Graphs

Query answering routinely employs knowledge graphs to assist the user in...

Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

Temporal knowledge prediction is a crucial task for the event early warn...

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed

Today's machine learning models for computer vision are typically traine...