Name
Title | ||
|---|---|---|
Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution. |
Abstract | ||
|---|---|---|
Entity resolution (ER), the process of identifying entities that refer to the same real-world object, has long been studied in the knowledge graph (KG) community, among many others. Humans, as a valuable source of background knowledge, are increasingly getting involved in this loop by crowdsourcing and active learning, where presenting condensed and easily-compared information is vital to help human intervene in an ER task. However, current methods for single entity or pairwise summarization cannot well support humans to observe and compare multiple entities simultaneously, which impairs the efficiency and accuracy of human intervention. In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table. We formulate several optimization problems for generating an optimal comparative table according to intuitive goodness measures and various constraints. Our experiments on real-world datasets, comparison with related work and user study demonstrate the superior efficiency, precision and user satisfaction of our approach in multi-entity resolution (MER).
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3209978.3210021 | SIGIR |
Keywords | Field | DocType |
Entity resolution,knowledge graph,comparative table,multi-entity summarization,holistic property matching | Automatic summarization,Pairwise comparison,Knowledge graph,Active learning,Name resolution,Information retrieval,Computer science,Crowdsourcing,Optimization problem | Conference |
ISBN | Citations | PageRank |
978-1-4503-5657-2 | 0 | 0.34 |
References | Authors | |
23 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| JiaCheng Huang | 1 | 2 | 1.37 |
| Yuzhong Qu | 2 | 726 | 62.49 |
| Haoxuan Li | 3 | 2 | 3.08 |
| Yuzhong Qu | 4 | 927 | 78.03 |