Generating Preview Tables for Entity Graphs
This work addresses the challenge for users in quickly previewing heterogeneous entity graphs before committing resources, though it appears incremental as it builds on existing optimization and algorithmic approaches.
The paper tackles the problem of selecting entity graphs from abundant datasets by proposing methods to generate preview tables that compactly present important entity types and relationships, with experiments showing accuracy and efficiency in scoring measures and algorithms.
Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.