Tensor-based Knowledge Graph Reasoning

The accumulation and inheritance of knowledge has created the brilliance of human civilization,and will also promote a new highland for the innovation and development of machine intelligence.By establishing the mapping of data to entities,concepts,etc.,and using the associations to deeply understand things,the knowledge graph bridges the semantic gap between human intelligence and artificial intelligence,and empowers the agents with logical reasoning,accurate decision-making,and other capabilities.Knowledge graph is an essential driver for machines to transition from perceptual intelligence to cognitive intelligence.Knowledge reasoning is the core technology for building large-scale knowledge graph and knowledge-enabled upper-layer applications.Existing knowledge reasoning methods face challenges in capturing neighborhood structural information,learning complex relational semantics,and modeling temporal sequential patterns.Starting from the characteristics of complex association,multi-source conflict,dynamic evolution,etc.,and relying on the advantages of tensors in fully knowledge expression,latent interaction features capture,and semantic interpretability,etc.,research on theories,methods and technologies are carried out in static,temporal,and incremental knowledge reasoning.The main innovations and research contents are as follows:Knowledge graph has high relational heterogeneity,while the traditional graph neural networks have the defects of capturing relational semantics and local structural information.In terms of single knowledge graph reasoning task,this thesis studies tensor graph neural network for knowledge graph completion.The tensor graph neural network for knowledge graph is proposed,which adopts tensor operations to model the interaction between entities,relations,and triples to break through the limitations brought by matrix operations.Besides,the Tucker operator and its properties are adopted to compress parameters and reduce computations for the network.In terms of cross-knowledge graph reasoning task,this thesis studies multi-relational graph attention networks for joint entity and relation alignment.Based on the proposed tensor graph attention network,a multi-relational graph attention mechanism is further proposed to assign different weights to associated entities in the process of relation learning.In addition,an effective joint entity and relation alignment network and global evaluation criteria are proposed.The experimental results show that compared with the existing advanced static inference methods,the proposed model can improve the reasoning accuracy by up to 7.6%.The distribution of entities in the temporal knowledge graph is sparse and variable,and the static models lack the ability to capture factual relevance in the temporal context.In terms of temporal interpolation,this thesis studies temporal interaction embedding based on tensors.The interaction among entities,relations and time in temporal knowledge graph is explicitly discussed.Cross-convolution is proposed to capture the potential interaction mode in different quadruple contexts.Tensor neural network is adopted to preserve the interaction information structure,and extract effective features from different perspectives to improve temporal prediction.In terms of temporal extrapolation,this thesis studies finegrained tensor graph attention network for evolutional temporal reasoning.For concurrent facts,fine-grained tensor graph attention model is proposed to learn entity,relation-level attention coefficients and self-attention coefficients,which can fully use the sparse temporal subgraph information.For temporally adjacent facts,Gated Recurrent Unit(GRU)is employed to recursively model the sequential patterns and make the embeddings cover historical information.Enhanced representations are learned under the dual action of historical and structural factors to improve the knowledge evolution learning.The experimental results show that compared with the existing advanced temporal inference methods,the proposed model can improve the accuracy of temporal reasoning by up to 5.9%.In the context of the continuous generation of big data and the improvement of knowledge extraction technology,the facts in the knowledge graph increase and evolve over time.Existing reasoning methods suffer from inefficiency and lack of interpretability.This thesis studies incremental knowledge reasoning based on Boolean tensor factorization.The incremental Boolean tensor factorization method is developed,and the factor update and the Boolean features merging algorithm of the knowledge graph representation tensor are proposed,which avoids repeated calculations and improves the decomposition efficiency.By using Boolean tensor factorization to capture the latent semantic information of knowledge graph,an incremental knowledge inference method based on factor reconstruction tensor is proposed.According to the interpretability of the Boolean tensor decomposition,the inference results are reasonably explained.Experimental results show that compared with the non-incremental reasoning method,the proposed model can improve the reasoning efficiency by more than 10 times.The tensor-based knowledge reasoning approaches proposed in this thesis can empower the knowledge graph to break through its own limitations,and accumulate momentum for artificial intelligence to move towards a higher level of cognitive intelligence.