3.8 Proceedings Paper

Exploiting Knowledge Graph to Improve Text-based Prediction

Journal

Publisher

IEEE

Keywords

Knowledge graph; text representation; text-based prediction

Funding

  1. National Science Foundation [IIS-1633370, SMA-1620319, CNS-1513939, CNS-1801652]

Ask authors/readers for more resources

As a special kind of big data, text data can be regarded as data reported by human sensors. Since humans are far more intelligent than physical sensors, text data contains useful information and knowledge about the real world, making it possible to make predictions about real-world phenomena based on text. As all application domains involve humans, text-based prediction has widespread applications, especially for optimization of decision making. While the problem of text-based prediction resembles text classification when formulated as a supervised learning problem, it is more challenging because the variable to be predicted may not be directly derivable from the text and thus there is a semantic gap between the target variable and the surface features that are often used for representing text data in conventional approaches. In this paper, we propose to bridge this gap by using knowledge graph to construct more effective features for text representation. We propose a two-step filtering algorithm to enhance such a knowledge-aware text representation for a family of entity-centric text regression tasks where the response variable can be treated as an attribute of a group of central entities. We evaluate the proposed algorithm by using two revenue prediction tasks based on reviews. The results show that the proposed algorithm can effectively leverage knowledge graphs to construct interpretable features, leading to significant improvement of the prediction accuracy over traditional features.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available