针对传统知识图谱实体抽取方法需要大量人工特征和专家知识的问题,提出一种基于BILSTM_CRF模型的神经网络结构实体抽取方法。它既能使用双向长短时记忆网络BILSTM(Bidirectional Long Short-Term Memory)提取文本信息的特征,又可利用条...针对传统知识图谱实体抽取方法需要大量人工特征和专家知识的问题,提出一种基于BILSTM_CRF模型的神经网络结构实体抽取方法。它既能使用双向长短时记忆网络BILSTM(Bidirectional Long Short-Term Memory)提取文本信息的特征,又可利用条件随机场CRF(Conditional Random Fields)衡量序列标注的联系。该方法对输入的文本进行建模,把句子中的每个词转换为词向量;利用BILSTM处理分布式向量得到句子特征;使用CRF标注并抽取实体,得到最终结果。实验结果表明,该方法的准确率和召回率更高,F1值提升约8%,具有更强的适用性。展开更多
Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only rec...Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.展开更多
文摘针对传统知识图谱实体抽取方法需要大量人工特征和专家知识的问题,提出一种基于BILSTM_CRF模型的神经网络结构实体抽取方法。它既能使用双向长短时记忆网络BILSTM(Bidirectional Long Short-Term Memory)提取文本信息的特征,又可利用条件随机场CRF(Conditional Random Fields)衡量序列标注的联系。该方法对输入的文本进行建模,把句子中的每个词转换为词向量;利用BILSTM处理分布式向量得到句子特征;使用CRF标注并抽取实体,得到最终结果。实验结果表明,该方法的准确率和召回率更高,F1值提升约8%,具有更强的适用性。
基金the National Key Research and Development Program of China(Nos.2020YFC2003502,2021YFF0704101)the National Natural Science Foundation of China(Grant No.62276038)+1 种基金the Natural Science Foundation of Chongqing(Nos.cstc2019jcyj-cxttX0002,cstc2021ycjh-bgzxm0013)the Key Cooperation Project of Chongqing Municipal Education Commission(HZ20210-08).
文摘Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.
