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arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Predicting ICU Pressure Injuries with Historical data: A Multivariate Time Series Approach

Predicting ICU Pressure Injuries with Historical Data: A Mul...

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IEEE International Conference on Big knowledge (ICBK)

作者： Lintai Cui Liuqi Jin Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Hospital-acquired pressure injury (HAPI) is a se-rious healthcare problem for intensive care unit (ICU) patients, which significantly affects their quality of life and prognosis, and increases hospitalization time and medical expenses. The current prediction models cannot accurately predict the pressure injury (PI) in ICU wards, as these models usually only predict based on the patient's current physical condition and electronic health record data, resulting in poor recall and precision, which affects the prediction performance. To solve this problem, we applied the multivariate time series data of I CU patients from admission to discharge and established a PI prediction model for ICU patients by utilizing the bidirectional long short-term memory neural network (Bi-LSTM) model. Experiments on the MIMIC-III (Medical Information Mart for Intensive Care) dataset show that the Bi-LSTM model has an F1 score of 0.24 and an AUC (Area Under Curve) value of 0.81, which are better than other models. This validates the effectiveness of the Bi-LSTM on the multivariate time series data to predict the occurrence of PI in ICU wards and assist nursing staff to allocate nursing resources more effectively.

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Tao Liu Biao Fan He Hu Xiao-Yong Du Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Information Renmin University of China Beijing China

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation.

关键词： Organizations Crawlers Search engines data mining Web pages databases Reliability

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A Hybrid Model for Explaining Older Adults' Continuance Intention Toward SNSs

A Hybrid Model for Explaining Older Adults' Continuance Inte...

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Wei Liu Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance & Economics Nanchang P. R. China

Due to the rapid growth of social net services (SNSs), research into SNSs continuance usage has recently emerged as an important issue in information systems adaption. This study develops an integrated model based on the expectation-confirmation model (ECM), the technology acceptance model (TAM), and the theory of planned behavior (TPB), and apply them into the context of older adults' continuance intention toward SNSs. The hypothesized model is validated empirically using a sample collected from 250 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modeling. Analysis results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, attitude, subject norm and perceived behavioral control. The results of these findings for SNSs practitioners are discussed at the end of this work.

关键词： Electronic countermeasures Adaptation models Reliability Mathematical model Attitude control Loading Load modeling

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Parallel Aggregation Queries over Star Schema: A Hierarchical Encoding Scheme and Efficient Percentile Computing as a Case

Parallel Aggregation Queries over Star Schema: A Hierarchica...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiongpai Qin Huiju Wang Xiaoyong Du Shan Wang Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering RUC Beijing China School of Information Renmin University of China Beijing China

Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM, can be computed in parallel, because they satisfy distributive law of addition. Unfortunately, some of statistical functions are not naturally parallelizable. That means they do not satisfy distributive law of addition. In this paper, we focus on percentile computing problem. We proposed an iterative-style prediction-based parallel algorithm in a distributed system. Prediction is done through a sampling technique. Experiment results verify the efficiency of our algorithm.

关键词： Prediction algorithms Convergence Algorithm design and analysis Histograms Encoding Query processing Indexes

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Generating multi-page mirror site from ajax interfaces

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Journal of Information and Computational Science 2009年第2期6卷 985-991页

作者： Xia, Tian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

Ajax is an important approach for improving rich interactivity between web server and end users during Web 2.0 eras. At the same time, AJAX web pages can not be indexed by search engines due to its asynchronous loading. In this paper, we propose a technique for generating the traditional multi-page mirror site from the AJAX based web pages. Firstly, a page state structure to represent any dynamic state of Ajax page is defined, and a states repository is proposed to store the different states. Secondly, each state is fetched from repository and rendered by an embedded browser to get the dynamic DOM, and the click events of candidate clickable elements are fired, then new states are created and stored to repository. Finally, page states are linked and transformed into static HTML files. Experimental results show that mirroring Ajax sites is feasible, though there still have some limitations need to be improved later. Copyright ©2009 Binary Information Press.

关键词： Websites

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ru Wang Meng Wu Shengwei Ji Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG; 2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively.

关键词： knowledge engineering Adaptation models Computational modeling Semantics Diversity reception Feature extraction data models

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows

Task Offloading Optimization Based on Position Prediction in...

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International Conference on Computer and Communications (ICCC)

作者： Dawen Zheng Lusheng Wang Caihong Kai Min Peng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay.

关键词： Service robots Simulation Force Predictive models Prediction algorithms Delays Task analysis

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Shingles-based structural clustering of web documents

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Journal of Computational Information Systems 2008年第6期4卷 2777-2785页

Web document structural clustering is a useful task for many web intelligent applications, however, processing based on the structure of web documents have not yet received strong attention. In this paper, we propose a shingles-based approach to clustering web documents by structure. Firstly, semi-structured web documents are converted into structured tree which composed of a set of limited nodes, and structural features are extracted by shingles. Secondly, we define document distance and structural distance matrix, and then structural similarity is calculated according to this matrix. Finally, we cluster the document structure based on modified k-means algorithm. Different from existing methods, we construct shingles not only including real vertical paths, but also virtual horizontal paths. Weight factors are also considered to optimize the algorithm. Experimental results show the effectiveness of the new shingles-based similarity measurement and the structural clustering. The proposed document similarity, as well as the structural shingles analysis, could be applied to other web-based research issues. © 2008 Binary Information Press.

关键词： Information systems

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