Retrieval-augmented generation (RAG) provides an efficient solution for expanding the knowledge boundaries of large language models (LLMs), where the indexing serves as a compass to guide LLMs in locating query-relevant external knowledge. Nevertheless, current indexing methods commonly encounter a critical challenge: native indexing is convenient to construct, but it usually disrupts contextual associations and constrains the expressive capacity of rich knowledge. Conversely, knowledge indexing can structure contextual knowledge, but it is often based on preset schemas that limit its generalizability. To address it, we propose a universal and flexible knowledge indexing called pseudo-graph (PG) indexing. During the indexing construction phase, we use the advanced LLMs to transform the knowledge of each raw text into a concise and structured mind map, organizing intra-document knowledge. Subsequently, independent mind maps are linked by associating highly relevant topics or consistent facts across documents, thereby establishing inter-document knowledge connections. Eventually, using the resulting knowledge network PG as the knowledge indexing can circumvent the challenges associated with schema design reliant on preset knowledge and relationship types. During the knowledge retrieval phase, we develop a PG knowledge retriever to mimic human note-reviewing, adaptively navigating and recalling query-relevant knowledge from PG. Experimental results demonstrate that retrieving relevant pseudo-subgraphs from the PG via PG indexing and retriever significantly improves performance in fact-based Q&A, hallucination correction, and two multi-document Q&A tasks, achieving $F1_{QE}$ improvements of 15.85%, 8.12%, 3.34%, and 5.73%, respectively, and outperforming the state-of-the-art baseline KGP-LLaMA. Our code is available at: https://github.com/IAAR-Shanghai/PGRAG.

Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named DuCape , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.

As the scope of receptive field and the depth of Graph Neural Networks (GNNs) are two completely orthogonal aspects for graph learning, existing GNNs often have shallow layers with truncated-receptive field and far from achieving satisfactory performance. In this article, we follow the idea of decoupling graph convolution into propagation and transformation processes, which generates representations over a sequence of increasingly larger neighborhoods. Though this manner can enlarge the receptive field, it has two critical problems unsolved: how to find the suitable receptive field to avoid under-smoothing or over-smoothing? and how to balance different diffusion operators for better capturing the local and global dependencies? We tackle these challenges and propose a S calable, A daptive G raph C onvolutional N etworks ( SAGCN ) with Transformer architecture. Concretely, we propose a novel non-heuristic metric method that quickly finds the suitable number of diffusing iterations and produces smoothed local embeddings that enable the truncated receptive field to become scalable and independent of prior experience. Furthermore, we devise smooth2seq and diffusion-based position schemes introduced into Transformer architecture for better capturing local and global information among embeddings. Experimental results show that SAGCN enjoys high accuracy, scalability and efficiency on various open benchmarks and is competitive with other state-of-the-art competitors.

Temporal Knowledge Graph Embedding (TKGE) is a crucial technique for performing Temporal Knowledge Graph Completion (TKGC). The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns. However, as we know, most existing TKGE models usually embed KGs into a single geometric space such as Euclidean, hyperbolic or hyperspherical space to maintain their specific geometric structures (e.g., chain, hierarchy, and ring structures). None of the existing methods can simultaneously model relation patterns of chain, hierarchy, ring structures, and relation mapping properties. This paper constructs a hybrid interaction TKGE model HyIE, which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. HyIE performs two Householder transformations of head and tail entities parameterized by relations in a high-dimensional mixed vector space. The curvature of hyperbolic and hyperspherical spaces depends on the product of both relation and temporal. The core of HyIE lies in implementing transformations and interactions of vectors in Euclidean, hyperbolic and hyperspherical spaces, and Household transformation of head and tail entities. Theoretically, HyIE can model crucial relation patterns and mapping properties simultaneously. Experimental results on five temporal knowledge graph benchmarks show that our HyIE achieves state-of-the-art performance.