Semantic relation computing theory and its application

Sun, Yunchuan; Cheng, Liang; Bie, Rongfang; Zhang, Junsheng

doi:10.1016/j.jnca.2014.09.017

Cited by 23 publications

(15 citation statements)

References 26 publications

(26 reference statements)

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“…Specifically, when the training data in the target task are insufficient for a good data modeling, it transfers the useful knowledge from the related auxiliary data which are from another task to enrich the data features. In this case, more data characteristics are integrated into the data learning facilitating an improved learning results [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Automatic transfer learning for short text mining

Yang

Zhang

2017

J Wireless Com Network

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As a new emerging technique, transfer learning enjoys the advantage of integrating the well-learnt knowledge from another related work to facilitate an improved learning result of one task. Most of the existing transfer learning methods are designed for long texts and short texts. However, the latter one distinguishes from the former one in terms of its sparse nature, noise words, syntactical structure, and colloquial terminologies used. A transfer learning algorithm called automatic transfer learning (AutoTL) is proposed for short text mining. By transferring knowledge automatically learnt from the online information, the proposed method enables training data to be selected automatically. Furthermore, it does not make any a priori assumption about probability distribution. Our experimental results on 20Newsgroups, Simulated Real Auto Aviation, and Reuter-21578 validate the higher performance of the proposed AutoTL over several state-of-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Automatic transfer learning for short text mining

Yang

Zhang

2017

J Wireless Com Network

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“…With the rapid growth of emerging applications like social network analysis, semantic Web analysis, bioinformatics network analysis, and Internet of Things, it is urgent to require the processing capability on large scale graphs with billions of vertices [8][9][10][11][12][13][14]. In order to analyze the structure, behavior and evolution of the World Wide Web, researchers often model the Web as a directed graph by treating Web pages as nodes and treating the links between pages as directed edges.…”

Section: Introductionmentioning

confidence: 99%

Optimal Representation of Large-Scale Graph Data Based on K2-Tree

Chang

Zeng

Wang

et al. 2017

Wireless Pers Commun

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Graph is widely used to model data in various applications. With the rapid growth of many emerging applications such as Internet of Things, it is urgent to require the processing capability on large scale graphs with billions of vertices. Web graph is a typical case of graph data that is widely used for analyzing the structure, behavior and evolution of the World Wide Web. In this paper, we focus on optimal representation of large-scale Web graphs. Our work is motivated by the need of fit large-scale graphs into the main memory and carry out analyze on them. By analyzing the adjacency matrix of Web graphs, we find two characteristics on the distribution of 1s in the matrix. Firstly, only a very small proportion of elements in the matrix are 1s. Secondly, majority of 1s gather around the principal diagonal and form a few number of clusters in the matrix. Based on these characteristics, we first develop a clustering mechanism to locate the clusters of 1s in the adjacency matrix. Then, we combine this clustering mechanism with a structure named K 2 -tree and propose an approach for representing large-scale Web graphs compactly. Basic idea of the approach is trying to compress a large number of zeros as a single zero. Experimental results show that, our approach not only reduces the space for representing a Web graph, but also reduces the time consumption for operations such as retrieving neighbors of any nodes on the graph; compared with existing approaches, our approach achieves the best space/time tradeoff.

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“…Nowadays applications using the sensor network monitoring strategy are being more and more widely used, such as in forest fire monitoring systems, CitySee, real time CO 2 monitoring systems, real time shortest path for drivers [ 1 ], a complex embedded system-CPS [ 2 ], graph similarity issue [ 3 ], digital library services [ 4 ], data security [ 5 ], the development of the Internet of Things [ 6 , 7 ], the development of the Web of Things [ 8 ], Semantic Link Network (SLN) [ 9 , 10 ], different bird species’ behavior research, air quality inspection [ 11 ] and so on. Big data analytics for these applications has become more and more essential [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

Wang

Song

Wang

et al. 2016

Sensors

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Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors’ monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption.

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Semantic relation computing theory and its application

Cited by 23 publications

References 26 publications

Automatic transfer learning for short text mining

Automatic transfer learning for short text mining

Optimal Representation of Large-Scale Graph Data Based on K2-Tree

Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

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