# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 1 Introduction  
Recently, knowledge bases (KBs) have become a key component in a variety of applications, including recommendation systems [1; 2], engineering technologies [3; 4], and intelligent conversation robots [5; 6]. To help produce new information for these knowledge-driven applications, researchers have generated a variety of KBs [7; 8; 9] in the last several decades. KBs are intelligible domain engines that save the structural knowledge of human understanding. Furthermore, they easily integrate various data sources and give a structured semantic representation for computer analysis and knowledge inference. KBs are multi-relational graphs that depict a large amount of concrete information. Different entities are represented by nodes, and the relations between entities are connected by edges, as seen in Figure 1. All components are grouped in the form of triplets, such as (_head entity_, _relation_, _tail entity_). For example, we know that Mercedes Benz is located in Germany, which can be represented as (_Mercedes Benz_, _Located_in_, _Germany_). Although many KBs now comprise billions of relations, entities, and triplets, they also have to deal with incompleteness and newly acquired real-world information. To address the above concerns, studies have focused on link prediction, which attempts to forecast missing facts in knowledge bases. Knowledge graph embedding (KGE) [10] is a term used to describe existing link prediction methods. KGE is used to figure out how to represent relations and entities in their embedding representations. Previously, the embedding representation of a relation was mainly seen as a translational distance or semantic matching between entities. They are simple and feasible, and they have been demonstrated to be successful in the field of prediction tasks. However, compared to deep and multi-layer architectures, they learn fewer expressive features, which theoretically restricts their performance. A typical means of increasing the features and expressiveness of existing methods is that of increasing the embedding size. Thus, the total embedding parameters are proportional to the number of relations and entities. This approach does not scale to large KBs. To increase the method's expressiveness independently of the embedding size, recent studies have proposed several multi-layer methods [11; 12; 13] and reported commendable results on established datasets. Having adopted fully connected feature layers, these methods are prone to overfitting. One way to address the scaling and overfitting problems is to use parameter-efficient and fast operators that can be integrated into deep networks. Convolutional neural networks (CNNs) have grown into a powerful technique among the many available technologies due to their significant performance in speech recognition, computer vision, and natural language processing. Based on the CNN algorithm, artificial intelligence has made significant breakthroughs in many fields [14; 15]. The convolution operator is an effective parameter and can achieve rapid computing with highly optimized GPU implementations. Moreover, with the extensive usage of multiple robust methodologies, over-fitting control is efficient in the training of convolutional networks. To provide a highly articulate link prediction system that effectively converges the advantages of CNNs, this paper proposes a convolutional adaptive network (CANet) method for KGE. First, all entities and relations are embedded into real-valued low-dimensional vectors, and an interaction operation is suggested to convert the initialized embeddings of the head entity and relation into multiple triplet-specific embeddings. Then, the specific embeddings of the head entity and relation are concatenated into 2D input matrices. In the convolution layer, CANet learns features that are different between the input matrices and allows the network to perform feature recalibrations.