The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry ...The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry and wine culture entities with different lengths and high training cost of named entity recognition models at the present stage,this study proposes a lite BERT+bi-directional long short-term memory+attentional mechanisms+conditional random field(ALBERT+BILSTM+Att+CRF).The method first obtains the characterlevel semantic information by ALBERT module,then extracts its high-dimensional features by BILSTM module,weights the original word vector and the learned text vector by attention layer,and finally predicts the true label in CRF module(including five types:poem title,author,time,genre,and category).Through experiments on data sets related to Chinese poetry and wine culture,the results show that the method is more effective than existing mainstream models and can efficiently extract important entity information in Chinese poetry and wine culture,which is an effective method for the identification of named entities of varying lengths of poetry.展开更多
To unambiguously identify topological entities such as faces, edges and vertices is one of the key issues of feature_based modelling. This makes it possible to replay the modelling history when the solid model is re_e...To unambiguously identify topological entities such as faces, edges and vertices is one of the key issues of feature_based modelling. This makes it possible to replay the modelling history when the solid model is re_evaluated. This paper describes a naming mechanism in order to fully automate model generation in terms of the constructing history. The topological naming system(TNS),presented in this paper, assigns a persistent identification to every topological entity if necessary, thus automatically generates a design variant when the design object is re_evaluated.展开更多
An object on the Semantic Web is likely to be denoted with several URIs by different parties. Object core-ferencing is a process to identify ＂equivalent＂ URIs of objects for achieving a better Data Web. In this pape...An object on the Semantic Web is likely to be denoted with several URIs by different parties. Object core-ferencing is a process to identify ＂equivalent＂ URIs of objects for achieving a better Data Web. In this paper, we propose a bootstrapping approach for object coreferencing on the Semantic Web. For an object URI, we firstly establish a kernel that consists of semantically equivalent URIs from the same-as, （inverse） functional properties and （max-）cardinalities, and then extend the kernel with respect to the textual descriptions （e.g., labels and local names） of URIs. We also propose a trustworthiness-based method to rank the coreferent URIs in the kernel as well as a similarity-based method for ranking the URIs in the extension of the kernel. We implement the proposed approach, called ObjectCoref, on a large-scale dataset that contains 76 million URIs collected by the Falcons search engine until 2008. The evaluation on precision, relative recall and response time demonstrates the feasibility of our approach. Additionally, we apply the proposed approach to investigate the popularity of the URI alias phenomenon on the current Semantic Web.展开更多
基金the Sichuan Science and Technology Program of China(No.2021YFG0055)the Zigong Science and Technology Program of China(No.2019YYJC15)+1 种基金the Nature Science Foundation of Sichuan University of Science&Engineering(No.2020RC32)the 2022 Graduate Innovation Fund Project of Sichuan University of Science&Engineering(No.Y2022168)。
文摘The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry and wine culture entities with different lengths and high training cost of named entity recognition models at the present stage,this study proposes a lite BERT+bi-directional long short-term memory+attentional mechanisms+conditional random field(ALBERT+BILSTM+Att+CRF).The method first obtains the characterlevel semantic information by ALBERT module,then extracts its high-dimensional features by BILSTM module,weights the original word vector and the learned text vector by attention layer,and finally predicts the true label in CRF module(including five types:poem title,author,time,genre,and category).Through experiments on data sets related to Chinese poetry and wine culture,the results show that the method is more effective than existing mainstream models and can efficiently extract important entity information in Chinese poetry and wine culture,which is an effective method for the identification of named entities of varying lengths of poetry.
文摘To unambiguously identify topological entities such as faces, edges and vertices is one of the key issues of feature_based modelling. This makes it possible to replay the modelling history when the solid model is re_evaluated. This paper describes a naming mechanism in order to fully automate model generation in terms of the constructing history. The topological naming system(TNS),presented in this paper, assigns a persistent identification to every topological entity if necessary, thus automatically generates a design variant when the design object is re_evaluated.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61003018 and 60973024in part by the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20100091120041in part by the IBM CRL UR Joint Project
文摘An object on the Semantic Web is likely to be denoted with several URIs by different parties. Object core-ferencing is a process to identify ＂equivalent＂ URIs of objects for achieving a better Data Web. In this paper, we propose a bootstrapping approach for object coreferencing on the Semantic Web. For an object URI, we firstly establish a kernel that consists of semantically equivalent URIs from the same-as, （inverse） functional properties and （max-）cardinalities, and then extend the kernel with respect to the textual descriptions （e.g., labels and local names） of URIs. We also propose a trustworthiness-based method to rank the coreferent URIs in the kernel as well as a similarity-based method for ranking the URIs in the extension of the kernel. We implement the proposed approach, called ObjectCoref, on a large-scale dataset that contains 76 million URIs collected by the Falcons search engine until 2008. The evaluation on precision, relative recall and response time demonstrates the feasibility of our approach. Additionally, we apply the proposed approach to investigate the popularity of the URI alias phenomenon on the current Semantic Web.
