TY - JOUR
T1 - FacetAtlas
T2 - Multifaceted visualization for rich text corpora
AU - Cao, Nan
AU - Sun, Jimeng
AU - Lin, Yu Ru
AU - Gotz, David
AU - Liu, Shixia
AU - Qu, Huamin
N1 - Funding Information:
This work was supported in part by grant HK RGC GRF 619309 and an IBM Faculty Award. The authors would like to thank all the user study participants and doctors for their contributions to the system evaluation, and the anonymous reviewers for their valuable comments.
PY - 2010/11/12
Y1 - 2010/11/12
N2 - Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.
AB - Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.
KW - Multi-relational Graph
KW - Multifaceted visualization
KW - Search UI
KW - Text visualization
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U2 - 10.1109/TVCG.2010.154
DO - 10.1109/TVCG.2010.154
M3 - Article
C2 - 20975156
AN - SCOPUS:78149261860
SN - 1077-2626
VL - 16
SP - 1172
EP - 1181
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 6
M1 - 5613456
ER -