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International Conference on Grid and Cooperative Computing Workshops (GCCW)

作者： Xiaoyong Du He Hu Man Li Wenjuan Wu Guo Qin Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

The paper describes an ongoing project which implements a subject-oriented semantic Web platform at Renmin Univ. of China. The economic semantic Web platform (ESWP) contains three components: collaborative ontology developing environment and repository system (CODERS); economic ontology annotation Web services (ConAnnotator); economic ontology and annotated resources. We describe each of these components in detail and illustrate some use cases of the ESWP

关键词： Semantic Web Ontologies Environmental economics Web services Collaboration Humans Software agents Application software Helium Laboratories

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NUITS: a novel user interface for efficient keyword search over databases 06

NUITS: a novel user interface for efficient keyword search o...

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Proceedings of the 32nd international conference on Very large data bases

作者： Shan Wang Zhaohui Peng Jun Zhang Lu Qin Sheng Wang Jeffrey Xu Yu Bolin Ding School of Information Renmin Univ. of China Key Laboratory of Data Engineering and Knowledge Engineering MOE of China Beijing China Department of Systems Engineering and Engineering Management The Chinese Univ. of Hong Kong Hong Kong China

The integration of database and information retrieval techniques provides users with a wide range of high quality services. We present a prototype system, called NUITS, for efficiently processing keyword queries on top of a relational database. Our NUITS allows users to issue simple keyword queries as well as advanced keyword queries with conditions. The efficiency of keyword query processing and the user-friendly result display will also be addressed in this paper.

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Survey on ontology learning research

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Ruan Jian Xue Bao/Journal of Software 2006年第9期17卷 1837-1847页

作者： Du, Xiao-Yong Li, Man Wang, Shan School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

Recently, ontology learning is emerging as a new hotspot of research in computer science. In this paper the issue of ontology learning is divided into nine sub-issues according to the structured degree (structured, semi-structured, non-structured) of source data and learning objects (concept, relation, axiom) of ontology. The characteristics, major approaches and the latest research progress of the nine sub-issues are summarized. Based on the analysis framework proposed in the paper, existing ontology learning tools are introduced and compared. The problems of current research are discussed, and finally the future directions are pointed out.

关键词： Computer science

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Probabilistic Graph Pattern Matching via Tumor knowledge Graph

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ACM Transactions on Probabilistic Machine Learning 1000年

作者： Lei Li Houdie Tu Zhenchao Tao Chenyang Bu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei 230601 China and School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China School of Data Science University of Science and Technology of China Hefei 230052 China and West District of the First Affiliated Hospital of University of Science and Technology of China Hefei 230031 China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei 230601 China

Graph Pattern Matching (GPM) entails the identification of subgraphs within a larger graph structure that either precisely mirror or closely parallel a predefined pattern graph. Despite the fact that research on GPM in large-scale graph data has been largely centered on social network analysis or enhancing the precision and efficiency of matching algorithms for expeditious subgraph retrieval, there is a noticeable absence of studies committed to probing GPM in medical domains. To rectify this shortcoming and probe the potential of GPM in clinical contexts, particularly in aiding patients with the selection of optimal tumor treatment plans, this paper introduces the concept of probabilistic graph pattern matching specifically modified for the Tumor knowledge Graph (TKG). We propose a multi-constraint graph pattern matching algorithm, hereinafter designated as TKG-McGPM, customized for the Tumor knowledge Graph. Through experimental verification, we establish that TKG-McGPM can facilitate more efficient and informed decision-making in tumor treatment planning.

关键词： Graph Pattern Matching Probabilistic Graph Pattern Matching Tumor knowledge Graph Monte Carlo Method

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Web Technologies Research and Development - APWeb 2005 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Yanchun Zhang Katsumi Tanaka Jeffrey Xu Yu Shan Wang Minglu Li

ISBN: (数字)9783540318491

ISBN: (纸本)9783540252078

This volume contains papers selected for presentation at the 7th Asia Pacific Conference on Web Technology (APWeb 2005), which was held in Shanghai, China during March 29–April 1, 2005. APWeb is an international conference series on WWW technologies and is the primary forum for researchers and practitioners from both academia and industry to exchange knowledge on WWW-related technologies and new advanced applications. APWeb 2005 received 420 submissions from 21 countries and regions worldwide, including China, Korea, Australia, Japan, Taiwan, France, UK, Canada, USA, India, Hong Kong, Brazil, Germany, Thailand, Singapore, Turkey, Spain, Greece, Belgium, New Zealand, and UAE. After a thorough review process for each submission by the Program Committee members and expert reviewers recommended by PC members, APWeb 2005 accepted 71 regular research papers (acceptance ratio 16.9%) and 22 short papers (acceptance ratio 5.2%). This volume also includes 6 keynote papers and 11 invited demo papers. The keynote lectures were given by six leading experts: Prof. Ah Chung Tsoi (Australia Research Council), Prof. Zhiyong Liu (National Nature Science Foundation of China), Prof. John Mylopoulos (University of Toronto), Prof. Ramamohanarao (Rao) Kotagiri (University of Melbourne), Prof. Calton Pu (Georgia Tech), and Prof. Zhiwei Xu (Chinese Academy of Sciences).

关键词： Information Storage and Retrieval Information Systems Applications (incl. Internet) Multimedia Information Systems User Interfaces and Human Computer Interaction Computer Communication Networks Computers and Society

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Conceptual Modeling for Advanced Application Domains 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Shan Wang Katsumi Tanaka Shuigeng Zhou Tok-Wang Ling Jihong Guan Dong-qing Yang Fabio Grandi Eleni E. Mangina Il-Yeol Song Heinrich C. Mayr

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Feature Disentanglement Based Heterogeneous Defect Prediction

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ACM Transactions on Software engineering and Methodology 1000年

作者： Xu Yu Jiaqi Yan Qinqin Gao Qinglong Peng Bin Yu Junwei Du Ying Xing Dunwei Gong Qingdao Institute of Software China University of Petroleum East China) Qingdao China College of Computer Science and Technology China University of Petroleum East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China University of Petroleum East China) Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China School of Data Science Qingdao University of Science and Technology Qingdao China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Data Science Qingdao University of Science and TechnologyQingdao China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Kunming China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

关键词： Heterogeneous Defect Prediction Feature Disentanglement Domain-related Features Domain-independent Features

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A Relation-Constraint Link Prediction Model for Dynamic knowledge Graphs with Entity Drift

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ACM Transactions on knowledge Discovery from data 1000年

作者： Xiulin Zheng Peipei Li Zan Zhang Jia Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China and School of Computer Science and Information Engineering Hefei University of Technology China School of Computing Faculty of Science and Engineering Macquarie University Australia

knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat

关键词： dynamic knowledge graph entity drift link prediction relation constraint

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MAINet: Modality-Aware Interaction Network for Medical Image Fusion

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Lisi Wei Libo Zhao Xiaoli Zhang College of Computer Science and Technology Jilin University China College of Artificial Intelligence and Big Data Hulunbuir University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

Due to the limitations of imaging sensors, obtaining a medical image that simultaneously captures both functional metabolic data and structural tissue details remains a significant challenge in clinical diagnosis. To address this, Multimodal Medical Image Fusion (MMIF) has emerged as an effective technique for integrating complementary information from multimodal source images, such as CT, PET, and SPECT, which is critical for providing a comprehensive understanding of both anatomical and functional aspects of the human body. One of the key challenges in MMIF is how to exchange and aggregate this multimodal information. This paper rethinks MMIF by addressing the harmony of modality gaps and proposes a novel Modality-Aware Interaction Network (MAINet), which leverages cross-modal feature interaction and progressively fuses multiple features in graph space. Specifically, we introduce two key modules: the Cascade Modality Interaction (CMI) module and the Dual-Graph Learning (DGL) module. The CMI module, integrated within a multi-scale encoder with triple branches, facilitates complementary multimodal feature learning and provides beneficial feedback to enhance discriminative feature learning across modalities. In the decoding process, the DGL module aggregates hierarchical features in two distinct graph spaces, enabling global feature interactions. Moreover, the DGL module incorporates a bottom-up guidance mechanism, where deeper semantic features guide the learning of shallower detail features, thus improving the fusion process by enhancing both scale diversity and modality awareness for visual fidelity results. Experimental results on medical image datasets demonstrate the superiority of the proposed method over existing fusion approaches in both subjective and objective evaluations. We also validated the performance of the proposed method in applications such as infrared-visible image fusion and medical image segmentation.

关键词： Multimodal fusion Medical image fusion Cascade modality interaction Modality-awareness Graph convolutional network

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