Alert button
Picture for Zhichun Wang

Zhichun Wang

Alert button

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Mar 09, 2020
Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches.

* 21 pages, in Chinese, 9 figures and 17 tables, CCKS 2019 held an evaluation track about knowledge graph with 6 tasks and attracted more than 1,600 teams 
Viaarxiv icon

RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

Dec 24, 2015
Zhichun Wang, Juanzi Li

Figure 1 for RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Figure 2 for RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Figure 3 for RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Figure 4 for RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

Recently, several large-scale RDF knowledge bases have been built and applied in many knowledge-based applications. To further increase the number of facts in RDF knowledge bases, logic rules can be used to predict new facts based on the existing ones. Therefore, how to automatically learn reliable rules from large-scale knowledge bases becomes increasingly important. In this paper, we propose a novel rule learning approach named RDF2Rules for RDF knowledge bases. RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting frequent patterns in knowledge bases, and then generates rules from the mined FPCs. Because each FPC can produce multiple rules, and effective pruning strategy is used in the process of mining FPCs, RDF2Rules works very efficiently. Another advantage of RDF2Rules is that it uses the entity type information when generates and evaluates rules, which makes the learned rules more accurate. Experiments show that our approach outperforms the compared approach in terms of both efficiency and accuracy.

Viaarxiv icon