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Graph-ranking collective Chinese entity linking algorithm

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Frontiers of Computer Science 2020年第2期14卷 291-303页

作者： Tao XIE Bin WU Bingjing JIA Bai WANG Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China

entity linking(EL)systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge *** EL approaches usually ignore the semantic correlation between the mentions in the text,and are limited to the scale of the local knowledge *** this paper,we propose a novel graphranking collective Chinese entity linking(GRCCEL)algorithm,which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge *** improved weighted word2vec textual similarity and improved PageRank algorithm,more semantic information and structural information can be captured in the *** an incremental evidence mining process,more powerful discrimination capability for similar entities can be *** evaluate the performance of our algorithm on some open domain *** results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

关键词： collective entity linking knowledge mapping word embedding entity correlation graph PageRank

Enriching Context Information for entity Linking with Web Data

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Journal of Computer Science & Technology 2020年第4期35卷 724-738页

作者： Yi-Ting Wang Jie Shen Zhi-Xu Li Qiang Yang An Liu Peng-Peng Zhao Jia-Jie Xu Lei Zhao Xun-Jie Yang Institute of Artificial Intelligence School of Computer Science and TechnologySoochou University Suzhou 215006China iFLYTEK Research Suzhou 215000China State Key Laboratory of Cognitive Intelligence iFLYTEKHefei 230000China Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology Jeddah 23955Saudi Arabia Anhui Toycloud Technologg Co. Ltd.Hefei 230000Chira

entity linking(EL)is the task of determining the identity of textual entity mentions given a predefined knowledge base(KB).Plenty of existing efforts have been made on this task using either"local"information(contextual information of the mention in the text),or"global"information(relations among candidate entities).However,either local or global information might be insufficient especially when the given text is *** get richer local and global information for entity linking,we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines(WSE).Based on the intuition above,two novel attempts are *** first one adds web-searched results into an embedding-based method to expand the mention's local information,where we try two different methods to help generate high-quality web contexts:one is to apply the attention mechanism and the other is to use the abstract extraction *** second one uses the web contexts to extend the global information,i.e.,finding and utilizing more extra relevant mentions from the web contexts with a graph-based ***,we combine the two models we propose to use both extended local and global information from the extra web *** empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

关键词： entity linking web search engine (WSE) attention mechanism abstract extraction

Modeling Topic-Based Human Expertise for Crowd entity Resolution

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Journal of Computer Science & Technology 2018年第6期33卷 1204-1218页

作者： Sai-Sai Gong Wei Hu Wei-Yi Ge Yu-Zhong Qu State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007 China

entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world *** ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate orerroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found thatcorrectly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number ofthem assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have variedexpertise on different topics （e.g., music versus sport）. In this paper, we deal with crowd ER in the Semantic Web *** identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similartask clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model thatcomputes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluationresults on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposedmodel achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.

关键词： entity resolution crowdsourcing human expertise topic modeling task similarity

DEM:Deep entity Matching Across Heterogeneous Information Networks

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Journal of Computer Science & Technology 2020年第4期35卷 739-750页

作者： Chao Kong Bao-Xiang Chen Li-Ping Zhang School of Computer and Information Anhui Polytechnic UniversityWuhu 241000China

Heterogeneous information networks,which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects,are ubiquitous in the real *** this paper,we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network,and we propose a new method named DEM short for Deep entity *** contrast to the traditional entity matching methods,DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of ***,we incorporate DEM with the network embedding methodology,enabling highly efficient computing in a vectorized ***'s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks *** illustrate its functionality,we apply the DEM algorithm to two real-world entity matching applications:user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation *** experiments on real-world datasets demonstrate DEM's effectiveness and rationality.

关键词： heterogeneous information network entity matching network embedding multi-layer perceptron

A Survey on Blocking Technology of entity Resolution

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Journal of Computer Science & Technology 2020年第4期35卷 769-793页

作者： Bo-Han Li Yi Liu An-Man Zhang Wen-Huan Wang Shuo Wan College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Safety-Critical Software Ministry of Industry and Information TechnologyNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China

entity resolution(ER)is a significant task in data integration,which aims to detect all entity profiles that correspond to the same real-world *** to its inherently quadratic complexity,blocking was proposed to ameliorate ER,and it offers an approximate solution which clusters similar entity profiles into blocks so that it suffices to perform pair-wise comparisons inside each block in order to reduce the computational cost of *** paper presents a comprehensive survey on existing blocking *** summarize and analyze all classic blocking methods with emphasis on different blocking construction and optimization *** find that traditional blocking ER methods which depend on the fixed schema may not work in the context of highly heterogeneous information *** to use schema information flexibly is of great significance to efficiently process data with the new features of this *** learning is an important tool for ER,but end-to-end and efficient machine learning methods still need to be *** also sum up and provide the most promising trend for future work from the directions of real-time blocking ER,incremental blocking ER,deep learning with ER,etc.

关键词： blocking construction blocking optimization data linkage entity resolution

Chinese Named entity Recognition with Character-Level BLSTM and Soft Attention Model

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Journal of Beijing Institute of Technology 2020年第1期29卷 60-71页

作者： Jize Yin Senlin Luo Zhouting Wu Limin Pan Information System and Security&Countermeasures Experimental Center Beijing Institute of TechnologyBeijing 100081China

Unlike named entity recognition(NER)for English,the absence of word boundaries reduces the final accuracy for Chinese *** avoid accumulated error introduced by word segmentation,a deep model extracting character-level features is carefully built and becomes a basis for a new Chinese NER method,which is proposed in this *** method converts the raw text to a character vector sequence,extracts global text features with a bidirectional long short-term memory and extracts local text features with a soft attention model.A linear chain conditional random field is also used to label all the characters with the help of the global and local text *** based on the Microsoft Research Asia(MSRA)dataset are designed and *** show that the proposed method has good performance compared to other methods,which proves that the global and local text features extracted have a positive influence on Chinese *** more variety in the test domains,a resume dataset from Sina Finance is also used to prove the effectiveness of the proposed method.

关键词： Chinese named entity recognition(NER) character-level bidirectional long short-term memory soft attention model

Improving SPARQL query performance with algebraic expression tree based caching and entity caching

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第4期13卷 281-294页

作者： Gang WU Meng-dong YANG College of Information Science and Engineering Northeastern UniversityShenyang 110004China MOE Key Laboratory of Medical Image Computing Northeastern UniversityShenyang 110004China School of Computer Science andEngmeermg Southeast UniversityNanjing 210096China

To obtain comparable high query performance with relational databases,diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework(RDF) data and SPARQL *** caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality ***,existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query *** semantic caching approaches work effectively in this case,little work has been done in this *** this paper,we try to improve SPARQL query performance with semantic caching approaches,i.e.,SPARQL algebraic expression tree(AET) based caching and entity *** queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two *** approaches are implemented on a two-level-storage *** main memory stores the most frequently accessed cache items,and items swapped out are stored on the disk for future possible *** results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our *** with previous research are also provided.

关键词： SPARQL Resource Description Framework (RDF) Semantic caching Algebraic expression tree (AET) entity

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

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Frontiers of Information Technology & Electronic Engineering 2019年第6期20卷 872-884页

作者： Ya QIN Guo-wei SHEN Wen-bo ZHAO Yan-ping CHEN Miao YU Xin JIN College of Computer Science and Technology Guizhou University Guiyang 550025 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang 550025 China Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China

By network security threat intelligence analysis based on a security knowledge graph(SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

关键词： Network security entity Security knowledge graph (SKG) entity recognition Feature template Neural network

Chunk Parsing and entity Relation Extracting to Chinese Text by Using Conditional Random Fields Model

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Journal of Intelligent Learning Systems and Applications 2010年第3期2卷 139-146页

作者： Junhua Wu Longxia Liu College of Electronics and Information Engineering Nanjing University of Technology

Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural lan-guage. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction. Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. This paper models chunk and entity relation problems in Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction.

关键词： Information Extraction Chunk Parsing entity Relation Extraction