Research On Ideological And Political Knowledge Mining Algorithm Based On Domain Knowledge Graph

Due to the outstanding advantages of domain knowledge graph in knowledge presentation,knowledge association discovery and knowledge structure representation,it has become an innovative technology in curriculum construction as well as curriculum ideological and political construction.However,when mining curriculum ideological and political elements,it is difficult to extract complex relationships and fuse to integrate multi-source heterogeneous knowledge.This paper studies the course ideological and political knowledge mining algorithm based on domain knowledge graph,and focuses on solving the algorithm problems of entity relation joint extraction and entity alignment.The main work is as follows:(1)A joint entity relation extraction algorithm based on hybrid neural network(JHNN)is improved.Firstly,based on the decomposition mechanism and shared coding,the entity relation extraction task is divided into two sub-tasks: the recognition of head entity and joint extraction of tail entity and relation.Then,the semantic dependency features in the semantic dependency adjacency matrix and the word feature vectors extracted from the sentences are utilized to complete the recognition of the head entity.Finally,the joint extraction of entity relations is conducteded based on the context semantic features and the structural features of the relationship weighted graph.Experiment results show that the F1 value of this algorithm is 1.4% higher than that of the optimal comparison model,which verifies the effectiveness.(2)This paper propose a hyperbolic dual attention mechanism(Hyper DA)for entity alignment.At first,the features of relation and attribute triples are fused into the entity vector representation through the graph attention network.Then,the entity vectors are mapped into the hyperbolic space by taking advantage of feature propagation in the hyperbolic space.Finally,combined with the weighted attribute information,based on the pre-aligned entity pairs,the hyperbolic distance is used to represent the similarity between the entity vectors to achieve entity alignment.Through comparative experiment analysis,the proposed algorithm achieves better performance in all indicators.(3)A course instance is investigated by combining the JHNN and Hyper DA algorithms.Aiming at the issue of efficient extraction of ideological and political elements,this paper takes two courses of C language programming and data structure as examples to construct the curriculum ideological and political field knowledge graph.Firstly,the entities and relations in the knowledge extraction phase are defined according to different data sources.Then,based on the JHNN and Hyper DA algorithms,the triplet extraction and entity alignment are completed,thus establishing the knowledge graph.The practical application shows,the algorithm in this paper is effective and strong connectivity when mining ideological and political elements and establishing the association between them and knowledge elements.In summary,this paper first studies the sentence-level entity relationship joint extraction algorithm JHNN to extract relation triples,and then presents the Hyper DA algorithm to realize knowledge fusion.Finally,the knowledge graph in the field of ideological and political courses is constructed to realize the efficient mining and application of ideological and political elements,which can better serve the ideological and political construction of courses.