Research On Technologies Of Knowledge Graph Construction In Cultural Relics

Museums are carriers for the protection and inheritance of cultural relics,and carry the ancient and civilized history.Driven by the booming internet of things,artificial intelligence,wisdom museum has attracted much attention in the museum field.However,due to many kinds and large quantities of cultural relics,and multi-source heterogeneity of cultural relic data on the Internet,there are two problems in the management and utilization of cultural relic information resources: there are lacks of effective organization in cultural heritage resources and effective correlation between the cultural relics.The cultural relic knowledge graph extracts the knowledge of cultural relics,forms a triple by using the potential connections among cultural relics,and constructs the knowledge base of cultural relics to realize the effective organization of cultural relics and provide the foundation for the integration and sharing of cultural relics resources.At the same time,the cultural relic knowledge graph is of great significance for extending cultural relic knowledge,enriching cultural relic display methods,promoting the development of intelligent question-and-answer,semantic search and wisdom tour projects,and improving museum intelligent services.The research of the cultural relic knowledge graph has attracted the attention of a large number of researchers.Although many studies have been done,the following challenges remain in building a high-quality cultural relic knowledge graph.(1)Cultural relic entity extraction,the supervised method requires a large amount of labeled data,but constructing large-scale labeled cultural relic entities is very laborious and time consuming.Besides,the word formation of Chinese cultural relic entity has particularity.(2)Cultural relic relation extraction,there are overlapped relations in cultural relic data,and keywords are sparse.(3)Cultural relic entity alignment,the cultural relic data in encyclopedia websites are multi-source and heterogeneity,and the precision of existing entity alignment methods which only obtain entity similarity from character or word level is relatively low.(4)Knowledge graph completion for cultural relics,there are implicit relations among cultural relic entities,and the labeled triples with implicit relation are scarce.To address the above challenges,this dissertation studies cultural relic entity extraction,relation extraction,entity alignment,and knowledge graph completion to provide support for CRKG construction.The achievements of this dissertation aresummarized as follows:(1)An entity extraction approach for cultural relics based on self-training semi-supervised is proposed.First,ELMo is used to extract the entity context features to solve the problem of word-formation particularity.Then,Bi LSTM and CRF models are constructed for feature extraction and tag prediction respectively to achieve the global optimal tag sequence prediction.Finally,a sample selection strategy of double-labeled for self-training is designed to promote the confidence of sample selection in semi-supervised pretraining,which selectes samples with high confidence by secondary labeling.Experimental results demonstrate that our approach achieves better performance with 50% labeled data in the entity extraction task for cultural relics.(2)A relation extraction approach for cultural relics based on capsule networks with wordattention synamic routing is proposed.First,character and word embedding incorporate part of speech and position to obtain semantic and word order features.Then,a dynamic routing algorithm based on word attention mechanism is designed,the information words are given higher weight and the connection strength is iteratively corrected to solve the problem of keyword sparsity.Finally,the instantiation parameters of advanced capsules are predicted by transformation matrix to realize overlapped relation extraction.Experimental results demonstrate that our approach effectively realizes the overlapped relation extraction.(3)An entity alignment approach for cultural relics based on multi-feature similarity is proposed.First,the entity attribute,entity abstract and entity context features are extracted respectively,and their similarities are calculated to obtain entity features from character,word and sentence levels.Then,the entity alignment model is constructed by integrating the entity attribute,entity abstract,and entity context feature similarities.Finally,the threshold is used to determine whether the two entities are aligned.Experimental results demonstrate that the precision of our approach is 2.11%,4.98% and 4.18% higher than that of comparison models in the entity alignment task,respectively.(4)A knowledge graph completion approach for cultural relics based on the BERT with entity-type information is proposed.First,the entity type as external knowledge is integrated to enhance the representation of text semantics to obtain the rich semantic information of entity effectively and eliminate counterexamples.Then,the multi-head attention mechanism is used to dynamically obtain text features to identify implicit relations effectively,and solve the sparsity of relations.Finally,a small number of labeled triples are used to fine-tune the model to solve the lack of labeled triples.Experimental results demonstrate that our approach utilizes35% labeled data to achieve good results in triple classification,link prediction and relation prediction task for cultural relics.