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13th International Conference on Web Information Systems engineering, WISE 2012

作者： Zhang, Hongbo Zhao, Qun Liu, Hongyan Xiao, Ke He, Jun Du, Xiaoyong Chen, Hong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China School of Economics and Management Tsinghua University China

ISBN: (纸本)9783642350627

Retweeting ensures the information diffusion in micro-blog services. By this simple way, it is convenient for a user to share and spread interesting information in the whole network. In this paper, we consider many features to compute the probability that a user retweets a tweet. With the probability, we build a retweet model to predict the number of possible-views of a tweet. The model is based on the theory of random walks. Experiments conducted on real dataset show that the proposed method has a good performance than the traditional prediction methods. © 2012 Springer-Verlag.

关键词： Forecasting

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Probabilistic range queries for Uncertain Trajectories on road networks 11

Probabilistic range queries for Uncertain Trajectories on ro...

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14th International Conference on Extending database Technology: Advances in database Technology, EDBT 2011

作者： Zheng, Kai Trajcevski, Goce Zhou, Xiaofang Scheuermann, Peter School of ITEE University of Queensland Australia Department of EECS Northwestern University United States School of Information Renmin University of China Key Lab. of Data Engineering and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781450305280

Trajectories representing the motion of moving objects are typically obtained via location sampling, e.g. using GPS or road-side sensors, at discrete time-instants. In-between consecutive samples, nothing is known about the whereabouts of a given moving object. Various models have been proposed (e.g., sheared cylinders;spacetime prisms) to represent the uncertainty of the moving objects both in unconstrained Euclidian space, as well as road networks. In this paper, we focus on representing the uncertainty of the objects moving along road networks as time-dependent probability distribution functions, assuming availability of a maximal speed on each road segment. For these settings, we introduce a novel indexing mechanism - UTH (Uncertain Trajectories Hierarchy), based upon which efficient algorithms for processing spatio-temporal range queries are proposed. We also present experimental results that demonstrate the benefits of our proposed methodologies.

关键词： Trajectories

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Bootstrap-Based Layerwise Refining for Causal Structure Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2708-2722页

作者： Xiang, Guodu Wang, Hao Yu, Kui Guo, Xianjie Cao, Fuyuan Song, Yukun Hefei University of Technology Key Laboratory of Knowledge Engineering with the Big Data of Ministry of Education Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Shanxi University School of Computer and Information Technology Taiyuan030006 China

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this article, we propose a Bootstrap-based layerwise refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layerwise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-of-the-art methods on the benchmark Bayesian Network datasets. © 2020 IEEE.

关键词： Refining

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Efficient Algorithm for Computing Link-based Similarity in Real World Networks

Efficient Algorithm for Computing Link-based Similarity in R...

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9th IEEE International Conference on data Mining

作者： Cai, Yuanzhe Cong, Gao Jia, Xu Liu, Hongyan He, Jun Lu, Jiaheng Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China Department of Computer Science Aalborg University Denmark Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9781424452422

Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.

关键词： Similarity Calculation SimRank Graph Mining

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Learning Inter-Entity Interaction for Few-Shot knowledge Graph Completion

Learning Inter-Entity Interaction for Few-Shot Knowledge Gra...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Yuling Yu, Kui Huang, Xiaoling Zhang, Yuhong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Enginerring Hefei University of Technology China

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://***/cjlyl/FKGC-CIAN. © 2022 Association for Computational Linguistics.

关键词： knowledge graph

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for knowledge Base Question Generation

SGSH: Stimulate Large Language Models with Skeleton Heuristi...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

ISBN: (纸本)9798891761193

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH - a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github. © 2024 Association for Computational Linguistics.

关键词： knowledge based systems

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

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IEEE International Conference on Computer Science and Automation engineering

作者： Han, Hong-Qi An, Xiaomi Zhu, Donghua Wang, Xuefeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Management and Economics Beijing Institute of Technology Beijing 100872 China

ISBN: (纸本)9781424487257

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value;LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method. © 2011 IEEE.

关键词： Visualization

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Distilling Dynamic Spatial Relation Network for Human Pose Estimation 32

Distilling Dynamic Spatial Relation Network for Human Pose E...

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32nd British Machine Vision Conference, BMVC 2021

作者： Wu, Kewei Wang, Tao Xie, Zhao Guo, Dan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation against various unreliable joints is critical. To this end, we propose a Distilling Dynamic Spatial Relation network (DDSR), which builds pose-based graph representation by exploiting the feature of spatial relation from the location distribution of joints. We use a dynamic message propagation mechanism to update the spatial relation on edges. Specifically, to filter out the noisy predictions, we select the joints with high confidence;to enhance the spatial relation in a large receptive field, we propagate multi-stage messages among joints. Besides, to reduce the computation cost of the multi-stage message propagation, we design a cross-resolution distillation framework. We use a new spatial distillation loss to verify the spatial relation between the teacher model and the student model. Experimental results on COCO and MPII datasets show that our method is superior to the state-of-the-art methods. The visualization results further verify the interpretability of our spatial relation. © 2021. The copyright of this document resides with its authors.

关键词： Distillation

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Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System 2021

Electronic Medical Record Sharing System Based on Hyperledge...

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5th International Conference on Compute and data Analysis, ICCDA 2021

作者： Li, Lei Yue, Zhengxiang Wu, Gongqing Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781450389112

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data. © 2021 ACM.

关键词： Blockchain

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