Qi Song

Orcid: 0000-0002-1726-7858

Affiliations:
  • University of Science and Technology of China, Hefei, Anhui, China
  • Washington State University, Pullman, WA, USA (former)


According to our database1, Qi Song authored at least 13 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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2021
2022
2023
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Legend:

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In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2023
CoTel: Ontology-Neural Co-Enhanced Text Labeling.
Proceedings of the ACM Web Conference 2023, 2023

Efficient Deep Ensemble Inference via Query Difficulty-dependent Task Scheduling.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

HeteroPush: Communication-Efficient Video Analytics by Scheduling Heterogeneous Filters.
Proceedings of the 9th International Conference on Big Data Computing and Communications, 2023

2022
Answering Why-Questions for Subgraph Queries.
IEEE Trans. Knowl. Data Eng., 2022

2021
GRIP: Constraint-based Explanation of Missing Answers for Graph Queries.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Explaining Missing Data in Graphs: A Constraint-based Approach.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

2020
Repairing Entities using Star Constraints in Multirelational Graphs.
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

2019
Discovering Patterns for Fact Checking in Knowledge Graphs.
ACM J. Data Inf. Qual., 2019

2018
Mining Summaries for Knowledge Graph Search.
IEEE Trans. Knowl. Data Eng., 2018

Fact Checking in Knowledge Graphs with Ontological Subgraph Patterns.
Data Sci. Eng., 2018

Discovering Graph Patterns for Fact Checking in Knowledge Graphs.
Proceedings of the Database Systems for Advanced Applications, 2018

2017
Discovering Graph Temporal Association Rules.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016
Mining Summaries for Knowledge Graph Search.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016


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