Text-based knowledge graph completion methods take advantage of pre-trained language models (PLM) to enhance intrinsic semantic connections of raw triplets with detailed text descriptions. Typical methods in this branch map an input query (textual descriptions associated with an entity and a relation) and its candidate entities into feature vectors, respectively, and then maximize the probability of valid triples. These methods are gaining promising performance and increasing attention for the rapid development of large language models. According to the property of the language models, the more related and specific context information the input query provides, the more discriminative the resultant embedding will be. In this paper, through observation and validation, we find a neglected fact that the relation-aware neighbors of the head entities in queries could act as effective contexts for more precise link prediction. Driven by this finding, we propose a relation-aware anchor enhanced knowledge graph completion method (RAA-KGC). Specifically, in our method, to provide a reference of what might the target entity be like, we first generate anchor entities within the relation-aware neighborhood of the head entity. Then, by pulling the query embedding towards the neighborhoods of the anchors, it is tuned to be more discriminative for target entity matching. The results of our extensive experiments not only validate the efficacy of RAA-KGC but also reveal that by integrating our relation-aware anchor enhancement strategy, the performance of current leading methods can be notably enhanced without substantial modifications.

Knowledge-aware recommendation systems have shown superior performance by connecting user item interaction graph (UIG) with knowledge graph (KG) and enriching semantic connections collected by the corresponding networks. Among the existing methods, self-supervised learning has attracted the most attention for its significant effects in extracting node self-discrimination auxiliary supervision, which can largely improve the recommending rationality. However, existing methods usually employ a single (either node or edge) perspective for representation learning, over-emphasizing the pair-wise topology structure in the graph, thus overlooking the important semantic information among neighborhood-wise connection, limiting the recommendation performance. To solve the problem, we propose Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec). The hierarchical property of the method is shown in two perspectives. First, to better reveal the knowledge graph semantic relations, we design a Triple-Graph Masked Autoencoder (T-GMAE) to force the network to estimate the masked node features, node connections, and node degrees. Second, to better align the user-item recommendation knowledge with the common knowledge, we conduct contrastive learning in a hybrid way, i.e., both neighborhood-level and edge-level dropout are adopted in a parallel way to allow more comprehensive information distillation. We conduct an in-depth experimental evaluation on three real-world datasets, comparing our proposed HKRec with state-of-the-art baseline models to demonstrate its effectiveness and superiority. Respectively, Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset.