Structure Selection from Streaming Relational Data

TL;DR

This paper introduces a streaming relational feature selection method that efficiently evaluates features on streaming data, leading to faster and more accurate models in social media applications.

Contribution

It presents a lightweight approach for evaluating relational features on streaming data, reducing manual trial-and-error in model development.

Findings

More accurate models achieved

Faster learning process demonstrated

Effective on social media tasks

Abstract

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster.