SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis


Mining the distribution of features and sorting items by combined attributes are 2 common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these 2 tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above 2 tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via 2 practical cases.

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    Reduced space refers to the 2D plane created by DR algorithms.


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The authors wish to thank all anonymous reviewers. This work was supported by National NSF of China (No. 61702359).

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Correspondence to Zeyu Li.

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Li, Z., Zhang, C., Zhang, Y. et al. SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis. J Vis (2021).

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  • Multivariable data
  • Multi-attribute rankings
  • Dimension reduction
  • Semantic modeling