## 1 Introduction  
Predicates play a pivotal role in knowledge management and interpretable reasoning. Formulating a reasonable and precise set of predicates is a fundamental step in building a knowledge graph (KG). Despite the widespread use of large language models (LLMs), a well-configured predicate set remains crucial for achieving interpretable reasoning. Traditional KG-based reasoning research typically operates in a transductive setting, where the triples to predict involve only entities and predicates that have occurred in the embedding training triples [1; 2; 3; 4]. This is known as transductive knowledge graph completion (_transductive KGC_). Conversely, _inductive KGC_ aims to complete triples involving unseen entities without retraining the KG embeddings from scratch [5; 6]. However, most inductive KGC studies focus on introducing new entities in the test set, rather than new predicates [7; 8]. This assumes that the predicate set configuration is reasonable, which is often not the case. Predicates in a KG may possess multiple semantics, reflected in the local structure associated with the predicate in the graph [9]. When a predicate exhibits multiple patterns in its local structure, it may have multiple semantics. For example, in Freebase [10], the predicate Person-Language represents the relationship between persons and the language they speak. This relationship can be further subcategorized into predicate relationships between persons and their mother tongue (Person-NativeLanguage) and between persons and the foreign language they master (Person-ForeignLanguage). The former exhibits local structural features closely associated with predicates like Person-Nationality. Predicates with multiple semantics can be termed as _polysemous predicates_. Additionally, there may be issues of _synonymous predicates_, where multiple distinct predicates share identical semantic features, leading to confusion in distinguishing between them. Conversely, there may be issues of _predicates missing_ in KGs, where a particular local structural pattern between entities is not captured by a single predicate (e.g., a KG has the predicate husbandOf but lacks the predicate coupleOf; the latter could assist KGC tasks related to marital relationships on the KG). These issues of predicate set configuration can significantly impact the ability of KGs to represent knowledge and the performance of reasoning models. Therefore, it is crucial to address these issues to enhance the quality of KGs and improve the performance of reasoning models. Recently, some research has been dedicated to implementing _predicate inductive KGC_, which involves introducing predicates that have never existed in the training set into the test set of KGC tasks [11]. However, these efforts either rely on additional ontology information and textual information for assistance in induction or focus solely on feasibility studies, often sacrificing the performance of reasoning models [12]. Furthermore, they have primarily considered the issue of missing predicates and have not delved into issues of polysemy and synonymy of predicates. In contrast, our work focuses on addressing the problem of polysemous predicates and proposes a simple yet effective method, PDEC (predicates decomposition), to enhance reasoning model performance without introducing any external information. PDEC optimizes the quality of KGs by decomposing polysemous predicates into more specific ones, thereby improving the accuracy and interpretability of reasoning. Our approach is based on representation learning, where we obtain entity and predicate embeddings by calculating _edge vectors_ for each triplet in the training set. These vectors are then clustered to identify polysemy in predicates and determine an optimal split for the polysemous predicates. This refined KG, achieved through _predicate decomposition_, can be seamlessly integrated into any reasoning model. Moreover, the original predicates can be reconstructed from the split predicates, enabling the model to operate on the original KG.