Rule extraction and reasoning for fusing relation and structure encoding
The domain knowledge graph exhibits characteristics of incompleteness and semantic complexity,which lead to shortcomings in the extraction and selection of rules,thereby affecting its inferential capabilities.A rule extraction model that integrates relationship and structural encoding is proposed to address this issue.A multidimensional embedding approach is achieved by extracting relational and structural information from the target subgraph and conducting feature encoding.A self-attention mechanism is designed to integrate relational and structural information,enabling the model to capture dependency relationships and local structural information in the input sequence better.This enhancement improves the understanding and expressive capabilities of context of the model,thus addressing the challenges of rule extraction and selection in the complex semantic situations.The experimental results for actual industrial datasets of automotive component failures and public datasets demonstrate improvements in the proposed model for link prediction and rule quality evaluation tasks.When the rule length is 3,an average increase of 7.1 percentage points in the mean reciprocal rank(MRR)and an average increase of 8.6 percentage points in Hits@10 are observed.For a rule length of 2,an average increase of 7.4 percentage points in MRR and an average increase of 3.9 percentage points in Hits@10 are observed.This confirms the effectiveness of relational and structural information in rule extraction and inference.
万方数据
维普
