The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current pop...The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current popular deep learning algorithms.Therefore,obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm.Because of business requirements,this KG’s application field is determined and the experts’opinions also must be satisfied.Considering these factors we adopt the top-down method which refers to determining the data schema firstly,then filling the specific data according to the schema.The design of data schema is the top-level design of KG,and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization.This method is generally suitable for the construction of domain KG.This article proposes a fast and efficient method to extract the topdown type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage.At the same time,based on the obtained triples,a data labeling method is proposed to obtain sufficiently high-quality training data,using in various Natural Language Processing(NLP)information extraction algorithms’training.展开更多
Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people's interactions in social lives. Discovering typed communities (e.g., family communiti...Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people's interactions in social lives. Discovering typed communities (e.g., family communities or corporate communities) in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling: one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.展开更多
文摘The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current popular deep learning algorithms.Therefore,obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm.Because of business requirements,this KG’s application field is determined and the experts’opinions also must be satisfied.Considering these factors we adopt the top-down method which refers to determining the data schema firstly,then filling the specific data according to the schema.The design of data schema is the top-level design of KG,and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization.This method is generally suitable for the construction of domain KG.This article proposes a fast and efficient method to extract the topdown type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage.At the same time,based on the obtained triples,a data labeling method is proposed to obtain sufficiently high-quality training data,using in various Natural Language Processing(NLP)information extraction algorithms’training.
基金supported by the Fundamental Research Funds for the Central Universities of Chinathe National Natural Science Foundation of China under Grant No. 60905029the Beijing Natural Science Foundation under Grant No. 4112046
文摘Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people's interactions in social lives. Discovering typed communities (e.g., family communities or corporate communities) in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling: one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.