Work

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

Year: 2020

Type: conference-paper

Abstract: Knowledge graphs (KGs) store highly heterogeneous information about the world in the structure of a graph, and are useful for tasks such as question answering and reasoning. However, they often contain errors and are missing information. Vibrant research in KG refinement has worked to resolve these issues, tailoring techniques to either detect specific types of errors or complete a KG.

Source: International World Wide Web Conference (WWW)

DOI: 10.1145/3366423.3380189

Authors: Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra

Institutions: University of Michigan, Helmholtz Center for Information Security

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CAREER: Timely Insights: Interpretable, Multi-scale Summarization of Networks over Time