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International Joint Conference on Neural Networks (IJCNN)

作者： Ding, Yi Rao, Yuan Yang, Fan Xi An Jiao Tong Univ Sch Software Engn Lab Social Intelligence & Complex Data Proc Xian Peoples R China

ISBN: (纸本)9780738133669

The incompleteness of the knowledge base (KB) limits the performance of open domain question answering (QA).Represent the incomplete KB with graph attention network (GAT) and complement the incomplete KB by extra text achieves great success to boost the QA system when the KB is incomplete. In this paper, we propose a Graph-based KB and Text Fusion Interaction Network (GTFIN) to improve the performance of the incomplete QA system by utilizing the KB and text *** GTFIN, to reduce the influence of the query-unrelated noisy information of GAT on final answer prediction, we first design a global-normalization graph attention network (GGAT) by determining the query-related edge weights from the global perspective, and then a coarse-to-fine text reader (CFReader) is proposed to both exploit the relation information and obtain the entity mention representation in the text to enhance the incomplete KB. We further incorporate a bi-attention mechanism to enhance the interaction between question and entity representation which could find more query-related entities for final answer prediction. On the widely used KBQA benchmark WebQSP, our model achieves state-of-the-art performance.

关键词： open domain question answering incomplete knowledge base coarse-to-fine text reader bi-attention

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A CPS-Based intelligence-Awareness Platform for IT Service Management

A CPS-Based Intelligence-Awareness Platform for IT Service M...

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Chinese Automation Congress (CAC)

作者： Zhao, Yanqing Rao, Yuan Xi An Jiao Tong Univ Sch Software Lab Social Intelligence & Complex Data Proc Xian 710049 Peoples R China

ISBN: (纸本)9781538635247

Inorder to solve a series of problems about IT service management in Enterprise, such as lower efficiency and service quality, complexed business processes and communication patterns, huge static management data and dynamic control data, etc., an intelligence-awareness IT service management platform, which is based on the Cyber Physical System (CPS) for constructing smart Industry 4.0, is proposed to optimize the management and service process for IT resources in this paper. The whole IT resources of enterprise mainly include software, hardware and employee resources. Ilow to aware and integrate all kinds of IT resources with intelligence method together for analyzing and providing better service is a big challenges, especially, the physical state-awareness about all hardware and network facilities, the business process- awareness about management software and business data, the knowledge awareness and service among the employees in Enterprise. Based on these critical IT resources, a new smart ITSM platform is designed and programed, which is not only use CPS meta-model to build the state-awareness smart environment for IT service, but also integrate ITIL (Information Technology Infrastructure Library) meta-model as a standard management process to aware the change about the process and knowledge library. The application results show that the new CPS-Awareness ITSM platform promotes the efficiency and qualify of IT service and provides early-alarm in the management process of the whole life cycle of IT resources in enterprise.

关键词： CPS ITIL Meta-Model IT Service Platform Event Management

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A Review of Sentiment Semantic Analysis Technology and Progress 13

A Review of Sentiment Semantic Analysis Technology and Progr...

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13th International Conference on Computational intelligence and Security (CIS)

作者： Wang, Yiming Rao, Yuan Wu, Lianwei Xi An Jiao Tong Univ Sch Software Lab Social Intelligence & Complex Data Proc Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538648223

Sentiment computing promotes some new application opportunities and technique challenges in artificial intelligence of the next generation, and it has become a fascinating research field. In this paper, the conception of sentiment computing with some core elements and feature vectors is defined, and some vital issues are proposed. Based on the theories mentioned above, the subjective content or objective content is classified by some special algorithms in the scenarios of single modal, such as text, image, audio and video data. Furthermore, the method to merge these different kinds of data and to further form the multimodal analysis methods for emotion detection is an important problem, and the fusion strategy is summarized in the paper. Finally, some trends about the sentiment cognition and sentiment generation are analyzed, which provides new ways for further research work.

关键词： sentiment analysis multimodal social sentiment opinion mining sentiment generation

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Expert Information Automatic Extraction for IOT Knowledge Base 7

Expert Information Automatic Extraction for IOT Knowledge Ba...

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7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Yi, Lu Yuan, Rao Long, Sun Xue, Li Xi An Jiao Tong Univ Sch Software Lab Social Intelligence & Complex Data Proc Xian 710049 Shaanxi Peoples R China

With the rapid development of IOT technology, the requirement of effective and accurate retrieval of domain knowledge is growing. Automatically extract various information of expert from the massive web pages and generate a dynamic and wholeness profile model are important for knowledge base. However, the obvious differences in structure and content semantics of web pages between any two websites shows traditional web crawler are hard to understand the semantic of the web page and extract the critical information of expert. Therefore, a six-dimension expert profile model was introduced and then a sequence tagging method with LSTM-CRF model was proposed to automatically extract rich semantic information basing on organization structure, meaning of words and attributes of experts. The results of the experiment on test data sets illustrated that the precision rate and recall rate about the job experience and research field of experts are 67.8%, 66.6% and 82.4%, 79.6%, respectively. In addition, the overall average F value about some obvious features of expert, such as name, title, email, achievement, etc., reaches 82.5%, which is better than the results by MEMM and LSTM algorithms. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Internet of Things Expert profile model Deep Learning Sequence Tagging

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False Information Detection on social Media via a Hybrid Deep Model 10th

False Information Detection on Social Media via a Hybrid Dee...

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10th International Conference on social Informatics (SocInfo)

作者： Wu, Lianwei Rao, Yuan Yu, Hualei Wang, Yiming Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Lab Social Intelligence & Complex Data Proc Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9783030011598;9783030011581

There is not only low-cost, easy-access, real-time and valuable information on social media, but also a large amount of false information. False information causes great harm to individuals, the society and the country. So how to detect false information? In the paper, we analyze false information further. We rationally select three information evaluation metrics to distinguish false information. We pioneer the division of information into 5 types and introduce them in detail from the definition, the focus, features, etc. Moreover, in this work, we propose a hybrid deep model to represent text semantics of information with context and capture sentiment semantics features for false information detection. Finally, we apply the model to a benchmark dataset and a Weibo dataset, which shows the model is well-performed.

关键词： Information credibility evaluation Rumor detection social media Text classification False information

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Expert Information Automatic Extraction for IOT Knowledge Base

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Procedia Computer Science 2019年 147卷 288-294页

作者： Lu Yi Rao Yuan Sun Long Li Xue Lab of Social Intelligence & Complex Data Processing School of Software Xi’an Jiaotong University Xi’an 710049 China

关键词： Internet of Things Expert profile model Deep Learning Sequence Tagging

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A Method of Chinese Named Entity Recognition Based on CNN-BILSTM-CRF Model

A Method of Chinese Named Entity Recognition Based on CNN-BI...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Sun Long Rao Yuan Lu Yi Li Xue Lab of Social Intelligence & Complex Data Processing School of Software Xi'an Jiaotong University

The main task of naming entity recognition is to identify the person names, location names, organization names, meaningful time, dates and other quantitative phrases and also classifying them into different categories. Yet,there are lots of field terms, meaningful entities, complicated location names and complex name of organizations contained in all kinds of these field terms is the most important mission in digging out the text now. The extraction of Chinese is more difficult than English entities owing to the lack of definite boundary and size characteristics of Chinese words. Therefore, we would train and test the marked corpus which is based on the CNN-BILSTM-CRF neutral network model in order to make good use of CNN to get the presentation character of words and label the words by using BILSTM and CRF. It makes the extraction of entity in the common came true. The experimental results show that the accuracy rate, recall rate and F value that we got in an unaddressed artificial features condition are 98.81%, 90.70% and 91.57% respectively which is better than the results we got by using the method of BILSTM+CRF and conditional random field(CRF).

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Research Progress of Knowledge Graph Based on Knowledge Base Embedding

Research Progress of Knowledge Graph Based on Knowledge Base...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Tang Caifang Rao Yuan Yu Hualei Cheng Jiamin Lab of Social Intelligence & Complex Data Processing School of Software Xi'an Jiaotong University

The knowledge Graph(KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph's construction technology based on knowledge base embedding. The basic idea is that the knowledge graph will be treated as a recursive process. Through utilizing the knowledge base's resources and the semantic representation of text characteristic, we can extend the new features that improve learning performance and knowledge graph completeness. In this paper, we give a general overview of knowledge graph's construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.

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Content-Based Hybrid Deep Neural Network Citation Recommendation Method 6th

Content-Based Hybrid Deep Neural Network Citation Recommenda...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Wang, Leipeng Rao, Yuan Bian, Qinyu Wang, Shuo Shenzhen Research Institute of Xi’an Jiaotong University Shenzhen518057 China Lab of Social Intelligence and Complex Data Processing Software School Xi’an Jiaotong University Xi’an710049 China Xi’an710049 China

ISBN: (纸本)9789811579837

The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query documents, which leads to the low quality of recommendation results. In this paper, DeepCite, a content-based hybrid neural network citation recommendation method is proposed. First, the BERT model was used to extract the high-level semantic representation vectors in the text, then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence, and the text vectors were matched in depth to generate candidate sets. Further, the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multi-source features. In the reranking stage, a variety of Metapath features were extracted from the citation network, and added to the deep neural network to learn, and the ranking of recommendation results were optimized. Compared with PWFC, ClusCite, BM25, RW, NNRank models, the results of the Deepcite algorithm presented in the ANN datasets show that the precision (P@20), recall rate (R@20), MRR and MAP indexesrise by 2.3%, 3.9%, 2.4% and 2.1% respectively. Experimental results on DBLP datasets show that the improvement is 2.4%, 4.3%, 1.8% and 1.2% respectively. Therefore, the algorithm proposed in this paper effectively improves the quality of citation recommendation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Relation Extraction Based on Dual Attention Mechanism

Relation Extraction Based on Dual Attention Mechanism

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2019国际计算机前沿大会

作者： Xue Li Yuan Rao Long Sun Yi Lu Lab of Social Intelligence and Complex Data Processing School of SoftwareXi'an Jiaotong University

The traditional deep learning model has problems that the longdistance dependent information cannot be learned,and the correlation between the input and output of the model is not *** the information processing on the sentence set is still *** at the above problems,a relation extraction method combining bidirectional GRU network and multiattention mechanism is *** word-level attention mechanism was used to extract the word-level features from the sentence,and the sentence-level attention mechanism was used to focus on the characteristics of sentence *** experimental verification in the NYT dataset was *** experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.

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