## 6 Conclusions  
This article delves into the impact of predicate settings in KGs, aiming to enhance reasoning performance through predicate decomposition. We present PDEC, a technique that improves the performance of reasoning methods on KGs. This method is unique in that it implements predicate-inductive reasoning without introducing external information. When the original predicate set changes, PDEC can still perform KGC tasks effectively. Furthermore, this approach enhances the quality of KGs.  
To validate the efficacy of PDEC, we conducted rigorous experiments. We analyzed the correlation between the granularity of predicate decomposition and the improvement in PDEC performance, as well as the impact of synonymous predicate merging. Our findings demonstrate the effectiveness of PDEC in enhancing reasoning performance on KGs.  
**Limitations**. The limitations of this method mainly lie in the following: First, the automatic optimization of clustering threshold hyperparameters in Equation (9) has not been achieved yet. In fact, according to the discussion in Section 3.6, this hyperparameter can be estimated based on the low dimensional manifold distribution of the dataset. The current algorithm version's clustering threshold parameters are based on empirical values obtained from a large number of experiments, which affects the efficiency and practicality of the algorithm. We have conducted relevant research and exploration, but have not yet reached a clear conclusion. Second, PDEC's clustering-based predicate decomposition cannot fully correspond to the hypernyms and hyponyms of predicates in the ontology, which reduces the interpretability of the new predicate set after predicate decomposition. This is also the cost that PDEC attempts to avoid introducing external information. Third, it has not yet been possible to synchronize and optimize the decomposition of polysemous predicates with the merging of synonymous predicates.  
Moving forward, our focus will be on automating the optimization of clustering threshold parameters for PDEC and combining ontology information to obtain new predicates that are more interpretable. We will also explore more effective optimization methods that balance polysemous predicate decomposition and synonymous predicate merging. These future directions aim to further improve the performance and quality of KGs.  
Conceptualization, X.T. and Y.M.; methodology, X.T. and Y.M.; software, X.T.; validation, X.T.; formal analysis, X.T.; investigation, X.T. and Y.M.; resources, X.T. and Y.M.; data curation, X.T.; writing--original draft preparation, X.T.; writing--review and editing, X.T.; visualization, X.T.; supervision, Y.M.; project administration, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The FB15K-237 Knowledge Base Completion dataset is available at [[https://www.microsoft.com/en-us/download/details.aspx?id=52312](https://www.microsoft.com/en-us/download/details.aspx?id=52312)]([https://www.microsoft.com/en-us/download/details.aspx?id=52312](https://www.microsoft.com/en-us/download/details.aspx?id=52312)) (accessed on 15 February 2024). The YAGO3-10 dataset can be downloaded at [[https://web.informatik.uni-mannheim.de/pi1/kge-datasets/yago3-10.tar.qz](https://web.informatik.uni-mannheim.de/pi1/kge-datasets/yago3-10.tar.qz)]([https://web.informatik.uni-mannheim.de/pi1/kge-datasets/yago3-10.tar.qz](https://web.informatik.uni-mannheim.de/pi1/kge-datasets/yago3-10.tar.qz)) (accessed on 15 February 2024). The NELL-995 dataset can be downloaded at [[https://github.com/wenhuchen/KB-Reasoning-Data](https://github.com/wenhuchen/KB-Reasoning-Data)]([https://github.com/wenhuchen/KB-Reasoning-Data](https://github.com/wenhuchen/KB-Reasoning-Data)) (accessed on 15 February 2024).  
The work and writing of this thesis have received strong support and assistance from [PERSON] from the Department of Computer Science at Tsinghua University.  
The authors declare no conflicts of interest.