Cited By
View all- Wu TYao KLi WQi GYu YZhao NZhang RDuan P(2024)Triple confidence measurement in knowledge graph with multiple heterogeneous evidencesWorld Wide Web10.1007/s11280-024-01307-x27:6Online publication date: 30-Sep-2024
unKR: A Python Library for Uncertain Knowledge Graph Reasoning by Representation Learning
Authors: 
Jingting Wang, Tianxing Wu, Shilin Chen, Yunchang Liu, Shutong Zhu, Wei Li, Jingyi Xu, Guilin QiAuthors Info & Claims
Pages 2822 - 2826
Abstract
Recently, uncertain knowledge graphs (UKGs), where each relation between entities is associated with a confidence score, have gained much attention. Compared with traditional knowledge graphs, UKGs possess the capability of uncertainty knowledge expression, which facilitates more reliable and precise knowledge graph reasoning by not only completing missing triples but also predicting triple confidences. In this paper, we release unKR, the first open-source python library for uncertain Knowledge graph (UKG) Reasoning by representation learning. We design a unified framework to implement two types of representation learning models for UKG reasoning, i.e., normal and few-shot ones. Besides, we standardize the evaluation tasks and metrics for UKG reasoning to ensure fair comparisons, and report the detailed results of each model under the consistent test setting. With unKR, it is effortless for users to reproduce existing models, as well as efficiently customize their own models. The library, documentation, demo, and re-implementing results are all publicly released at https://github.com/seucoin/unKR.
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Index Terms
- unKR: A Python Library for Uncertain Knowledge Graph Reasoning by Representation Learning
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Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 ACM.
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Publication History
Published: 11 July 2024
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- Short-paper
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- the Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- ZhiShan Young Scholar Program of Southeast University
- the Big Data Computing Center of Southeast University
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SIGIR 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
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View all- Wu TYao KLi WQi GYu YZhao NZhang RDuan P(2024)Triple confidence measurement in knowledge graph with multiple heterogeneous evidencesWorld Wide Web10.1007/s11280-024-01307-x27:6Online publication date: 30-Sep-2024
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