Guidance for Multi-Type Entity Graphs from Text Collections
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The visual exploration of graphs encoding relationships between entities of multiple types (e.g., persons, locations,...) supports journalists in finding newsworthy information in large text collections. Journalists may have interest in certain entity types or their relations such as locations or person-person relations. This interest may change during the exploration process. The exploration of such large graphs is often supported by guidance using a degree-of-interest (DOI) function. Although many DOIs exist, they do not differentiate entity types, rely on additional data, or require complex settings overburding the journalists. We present a novel DOI for graphs with multiple types of entities. We show the interesting subgraph around the focal node and offer information about possible further steps. The user can interactively set her interest in entity types and entity relations. We apply our approach to a graph extracted from WikiLeaks PlusD Cablegate documents and report on journalists' feedback.
Description
@inproceedings{10.2312:eurova.20171111,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Michael Sedlmair and Christian Tominski},
title = {{Guidance for Multi-Type Entity Graphs from Text Collections}},
author = {Müller, Martin and Ballweg, Kathrin and Landesberger, Tatiana von and Yimam, Seid and Fahrer, Uli and Biemann, Chris and Rosenbach, Marcel and Regneri, Michaela and Ulrich, H.},
year = {2017},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-042-0},
DOI = {10.2312/eurova.20171111}
}