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Combining term semantics with content and structure semantics for XML element search results clustering

Journal of Convergence Information Technology 2012年第15期7卷 26-35页

作者： Minjuan, Zhong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

The biggest characteristic of the XML retrieval is able to return the element node results. This paper studies XML element search results clustering and proposes one similarity measurement method based on term semantics, in which the "core" concept between terms is got through latent semantic indexing technology(LSI) and the same time the XML element node content and semantic structure properties(CASS) are combined. In addition, two new performance evaluation methodologies, namely R_ClusterRatio and R_DocuRatio are introduced to evaluate clustering quality. It is motivated by the observations of relevant documents distribution and the fact that the experiment data collection, IEEE CS corpus, do not provide classification information. Experiment results show that proposed similarity method combining term semantics with content and structure semantics integration(LSI-CASS) is feasible, and it produces better clustering quality than LSI-CAS and CASS.

关键词： Semantics

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Dependency Structure -Enhanced Graph Attention Networks for Event Detection 38

Dependency Structure -Enhanced Graph Attention Networks for ...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Lu, Kun Li, Chenliang Liu, Xiping Liu, Dexi Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China SUNY Stony Brook Coll Business Stony Brook NY USA Univ Oklahoma Sch Lib & Informat Studies Norman OK 73019 USA Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China

ISBN: (纸本)1577358872

Existing models on event detection share three -fold limitations, including (1) insufficient consideration of the structures between dependency relations, (2) limited exploration of the directed -edge semantics, and (3) issues in strengthening the event core arguments. To tackle these problems, we propose a dependency structure-enhanced event detection framework. In addition to the traditional token dependency parsing tree, denoted as TDG, our model considers the dependency edges in it as new nodes and constructs a dependency relation graph (DRG). DRG allows the embedding representations of dependency relations to be updated as nodes rather than edges in a graph neural network. Moreover, the levels of core argument nodes in the two graphs are adjusted by dependency relation types in TDG to enhance their status. Subsequently, the two graphs are further encoded and jointly trained in graph attention networks (GAT). Importantly, we design an interaction strategy of node embedding for the two graphs and refine the attention coefficient computational method to encode the semantic meaning of directed edges. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Our model outperforms the best benchmark with the Fl score increased by 3.5 and 3.4 percentage points on ACE2005 English and Chinese corpus.

关键词： Semantics

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Configurable ranking for XML vague retrieval

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Journal of Computational Information Systems 2009年第2期5卷 683-692页

作者： Jiang, Tengjiao Wan, Changxuan Liu, Xiping School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

Ranking is one of the key factors for efficient and effective XML information retrieval. Compared with traditional IR, XML information retrieval has introduced many new challenges, one of which is that the traditional static document ranking method is no longer sufficient for ranking XML query results. As a result, we need a new ranking method. The paper studies the proper ranking model for XML vague retrieval. We first study the properties of vague retrieval over XML documents, and conclude the influencing factors of structure and content relaxation of XML vague retrieval. To improve recall and precision, we propose a new idea of confident structure query and vague structure query. Then, a configurable ranking model for twig pattern query results is proposed, in which users can configure query parameter. The model is designed to handle the new features of XML VCAS (vague content and structure). The architecture of corresponding XML search engine is also provided. © 2009 Binary Information Press March, 2009.

关键词： XML

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Clustering XML retrieval results based on hybrid similarity

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Journal of Computational Information Systems 2008年第3期4卷 1323-1330页

作者： Wan, Changxuan Yu, Hong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the unceasing growth of XML data in World Wide Web, XML document retrieval and clustering retrieval results are confronted with both challenges and opportunities. One of the challenges is how to improve the quality of XML retrieval results. Firstly, according to the features of XML documents, a method of modeling XML retrieval result documents is brought forward, which integrates both structural semantic features and content information of XML documents. Then, a measure method to compute similarity, including structural semantic similarity and keywords similarity, between retrieval result documents is suggested;and a strategy named Item Frequency in Cluster-Inverse Cluster Frequency to extract labels from result clusters is presented. Experiments indicate that the clustering quality for XML retrieval results based on hybrid similarity is obviously better than the one only based on content similarity.

关键词： XML

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Group event recommendation based on graph multi-head attention network combining explicit and implicit information

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INFORMATION PROCESSING & MANAGEMENT 2022年第2期59卷 102797-102797页

作者： Liao, Guoqiong Deng, Xiaobin Wan, Changxuan Liu, Xiping Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China Jiangxi Water Resources Inst Nanchang Jiangxi Peoples R China Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China

In event-based social networks (EBSN), group event recommendation has become an important task for groups to quickly find events that they are interested in. Existing methods on group event recommendation either consider just one type of information, explicit or implicit, or separately model the explicit and implicit information. However, these methods often generate a problem of data sparsity or of model vector redundancy. In this paper, we present a Graph Multi-head Attention Network (GMAN) model for group event recommendation that integrates the explicit and implicit information in EBSN. Specifically, we first construct a user-explicit graph based on the user's explicit information, such as gender, age, occupation and the interactions between users and events. Then we build a user-implicit graph based on the user's implicit information, such as friend relationships. The incorporated both explicit and implicit information can effectively describe the user's interests and alleviate the data sparsity problem. Considering that there may be a correlation between the user's explicit and implicit information in EBSN, we take the user's explicit vector representation as the input of the implicit information aggregation when modeling with graph neural networks. This unified user modeling can solve the aforementioned problem of user model vector redundancy and is also suitable for event modeling. Furthermore, we utilize a multi-head attention network to learn richer implicit information vectors of users and events from multiple perspectives. Finally, in order to get a higher level of group vector representation, we use a vanilla attention mechanism to fuse different user vectors in the group. Through experimenting on two real-world Meetup datasets, we demonstrate that GMAN model consistently outperforms state-of-the-art methods on group event recommendation.

关键词： Explicit information Implicit information Group recommendation Graph attention network

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K-nearest neighbors in uncertain graph

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第10期48卷 1850-1858页

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Du, Lingxia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Computer and Information Engineer Jiangxi Agriculture University Nanchang 330045 China

In many areas, a lot of data have been modeled by graphs which are subject to uncertainties, such as molecular compounds and protein interaction networks. While many real applications, for example, collaborative filtering, fraud detection, and link prediction in social networks etc, rely on efficiently answering k-nearest neighbor queries (kNN), which is the problem of computing the most "similar" k nodes to a given query node. To solve the problem, in this paper a novel method based on measurement of SimRank is proposed. However, because graphs evolve over time and are uncertainly, the computing cost can be very high in practice to solve the problem using the existing algorithms of SimRank. So the paper presents an optimization algorithm. Introducing path threshold, which is suitable in both determined graph and uncertain graph, the algorithm merely considers the local neighborhood of a given query node instead of whole graph to prune the search space. To further improving efficiency, the algorithm adopts sample technology in uncertain graph. At the same time, theory and experiments interpret and verify that the optimization algorithm is efficient and effective.

关键词： Collaborative filtering

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Top-k entities query processing on uncertainly fused multi-sensory data

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PERSONAL AND UBIQUITOUS COMPUTING 2013年第5期17卷 951-963页

作者： Liu, Dexi Wan, Changxuan Xiong, Naixue Park, Jong Hyuk Rho, Seungmin Jiangxi Univ Finance & Econ Sch Informat Technol Jiangxi Key Lab Data & Knowledge Engn Nanchang Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul South Korea Korea Univ Sch Elect Engn Seoul South Korea

Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertainty exists in these data because sensors are not precise enough. Hence, the intuitive method to store this kind of data is using uncertain database. Finding the top-k entities according to one or more attributes is a powerful technique when the uncertain database contains large quantity of data. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. We propose a method to process entity-based global top-k aggregate queries in uncertain database, which returns the top-k entities that have the highest aggregate value. Our method has two levels, entity state generation and G-topk-E query processing. In the former level, entity states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming-based global top-k entity query processing is employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.

关键词： G-topk-E-Agg query e-Tuple, dynamic programming algorithm Uncertain database Multi-sensory data

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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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Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph 61

Joint Document-Level Event Extraction via Token-Token Bidire...

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61st Annual Meeting of the the Association-for-Computational-Linguistics (ACL)

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Liu, Dexi Li, Chenliang Zheng, Bolong Liu, Xiping Hu, Rong Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China SUNY Stony Brook Coll Business Stony Brook NY 11794 USA Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Scie Tech Wuhan Peoples R China

ISBN: (纸本)9781959429722

We solve the challenging document-level event extraction problem by proposing a joint exaction methodology that can avoid inefficiency and error propagation issues in classic pipeline methods. Essentially, we address the three crucial limitations in existing studies. First, the autoregressive strategy of path expansion heavily relies on the orders of argument roles. Second, the number of events in documents must be specified in advance. Last, unexpected errors usually exist when decoding events based on the entity-entity adjacency matrix. This paper designs a Token-Token Bidirectional Event Completed Graph (TT-BECG) in which the relation eType-Role1-Role2 serves as the edge type, precisely revealing which tokens play argument roles in an event of a specific event type. Exploiting the token-token adjacency matrix of the TT-BECG, we develop an edge-enhanced joint document-level event extraction model. Guided by the target token-token adjacency matrix, the predicted token-token adjacency matrix can be obtained during model training. Then, the event records in a document are decoded based on the predicted matrix, including the graph structure and edge-type decoding. Extensive experiments are conducted on two public datasets, and the results confirm the effectiveness of our method and its superiority over the state-of-the-art baselines.

关键词： Decoding

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A RFID Mixed Coding Scheme Based on Huffman Algorithm

A RFID Mixed Coding Scheme Based on Huffman Algorithm

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

作者： Jiang Jian Liao Guoqiong Wang Hao Wan Qizhi School of Information Technology Jiangxi university of Finance and Economics China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang Nanchang China

In RFID application systems with multiple packaging layers, labeling packaging relationship of objects in different packaging layers by encoding methods is a important technology field. Prefix-based labeling scheme is a method by which the packaging relationship of objects amounts to testing whether one object is an prefix of the other, whereas the region numbering scheme is a method by which an ancestor query amount to an interval containment test on the labels. This paper first presents a mixed-encoding scheme based on Huffman algorithm, then propose a variant of this encoding scheme in which new "virtual nodes" are involved in order to improve the stability of encoding. Our experiments prove that the mixed-based encoding scheme is effective due to the enhancement of update performance and query efficiency.

关键词： Encoding labeling Radiofrequency identification XML Algorithm design and analysis Testing

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