The standard essential patents are some patents for which the patent technology solutions are adopted by the technological standard. The identification of standard essential patents is an important task in the standar...
The standard essential patents are some patents for which the patent technology solutions are adopted by the technological standard. The identification of standard essential patents is an important task in the standardization. With the sharply increasing number of patents and much more diversity of standard domains, the manual standard essential patents identification methods to check the patent by word for word, are not applicable for the big patent dataprocessing. The automatic standard essential patents identification methods face some challenges from the extraction and representation of features to the construction and learning of models. To solve the above issues, we develop a graph convolutional network based standard essential patents identification. We propose a hypothesis that the standard essential patents under the same standard are structure-association and the associated relations can be used in standard essential patents identification. Herein, we extract the co-occurrence relations including keyword-keyword relation, document-keyword relation. We construct a heterogeneous graph consisting of patent nodes, keyword nodes and their co-occurrence relations, which include rich structural information for the identification of standard essential patents. Thereafter, a graph convolutional network is developed to identify the potential standard essential patents. Since the number of standard essential patents and nonstandard essential patents is imbalance in practice, we utilize the sampling method to alleviate the imbalanced issue on the graph. To validate the effectiveness of the proposed model, we conduct experiments on five datasets of patent in the real world and the experimental results show that the proposed model outperforms existing research in the identification of standard essential patents.
With increasing global competition of intellectual property, a large number of unstructured patent texts are generated for technology protection. The ocean of patent texts include many long sentences about technologie...
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作者：
Liu, LinZhou, Jian-TaoXing, Hai-FengGuo, Xiao-YongCollege of Computer Science
Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology
Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management
Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China
Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based ...
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Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Re...
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Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.
Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducin...
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Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.
Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migr...
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Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.
Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks. However, with the increase of ChIP...
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With the intense competition of global intellectual property, the increasing patents promote the potentiality of patent transactions. Patent valuation is the premise of the patent transaction. Automatic patent valuati...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
With the intense competition of global intellectual property, the increasing patents promote the potentiality of patent transactions. Patent valuation is the premise of the patent transaction. Automatic patent valuation faces some challenging issues from valuation feature to valuation model. To solve the above issues, we propose a Bayesian graph convolutional neural network based patent valuation model. In the model, the valuation objects are defined, from which to some valuation features are extracted. Valuation scenario is the constructed, on which Bayesian graph convolutional neural network is used to generate patent value. We evaluate our model by comparing the state-of-the-art model on patent data sets. The results show that our model outperforms other models in the evaluation measurements.
Fractal represents a special feature of nature and functional objects. However, fractal based computing can be applied to many research domains because of its fixed property resisted deformation, variable parameters a...
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Fractal represents a special feature of nature and functional objects. However, fractal based computing can be applied to many research domains because of its fixed property resisted deformation, variable parameters a...
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Fractal represents a special feature of nature and functional objects. However, fractal based computing can be applied to many research domains because of its fixed property resisted deformation, variable parameters and many unpredictable changes. Theoretical research and practical application of fractal based computing have been hotspots for 30 years and will be continued. There are many pending issues awaiting solutions in this domain, thus this thematic issue containing 14 papers publishes the state-of-the-art developments in theorem and application of fractal based computing, including mathematical analysis and novel engineering applications. The topics contain fractal and multifractal features in application and solution of nonlinear odes and equation.
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