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An integrated pipeline model for biomedical entity alignment

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Frontiers of Computer Science 2021年第3期15卷 81-95页

作者： Yu HU tiezheng nie Derong SHEN Yue KOU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

Biomedical entity alignment,composed of two subtasks:entity identification and entity-concept mapping,is of great research value in biomedical text mining while these techniques are widely used for name entity standardization,information retrieval,knowledge acquisition and ontology *** works made many efforts on feature engineering to employ feature-based models for entity identification and ***,the models depended on subjective feature selection may suffer error propagation and are not able to uti-lize the hidden *** rapid development in health-related research,researchers need an effective method to explore the large amount of available biomedical ***,we propose a two-stage entity alignment process,biomedical entity exploring model,to identify biomedical entities and align them to the knowledge base *** model aims to automatically obtain semantic information for extracting biomedical entities and mining semantic relations through the standard biomedical knowledge *** experiments show that the proposed method achieves better performance on entity *** proposed model dramatically improves the FI scores of the task by about 4.5%in entity identification and 2.5%in entity-concept mapping.

关键词： entity alignment biomedical text mining neural network model

Strongly connected components based efficient computation of page rank

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Frontiers of Computer Science 2018年第6期12卷 1208-1219页

作者： Hongguo YANG Derong SHEN Yue KOU tiezheng nie Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110004China

In this paper, an efficient page rank (PR) exact algorithm is proposed, which can improve the computation efficiency without sacrificing results accuracy. The existing exact algorithms are generally based on the original power method (PM). In order to reduce the number of I/Os required to improve efficiency, they partition the big graph into multiple smaller ones that can be totally fitted in memory. The algorithm proposed in this paper can further reduce the required number of I/Os. Instead of partitioning the graph into the general subgraphs, our algorithm partitions graph into a special kind of subgraphs: SCCs (strongly connected components), the nodes in which are reachable to each other. By exploiting the property of SCC, some theories are proposed, based on which the computation iterations can be constrained on these SCC subgraphs. Our algorithm can reduce lots of I/Os and save a large amount of computations, as well as keeping the results accuracy. In a word, our algorithm is more efficient among the existing exact algorithms. The experiments demonstrate that the algorithms proposed in this paper can make an obvious efficiency improvement and can attain high accurate results.

关键词： page rank strongly connected component power method I/Os

Discovering context-aware conditional functional dependencies

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Frontiers of Computer Science 2017年第4期11卷 688-701页

作者： Yuefeng DU Derong SHEN tiezheng nie Yue KOU Ge YU College of Information Science and Engineering Northeastern University Shenyang 110819 China

Conditional functional dependencies（CFDs） are important techniques for data consistency. However, CFDs are limited to 1） provide the reasonable values for consistency repairing and 2） detect potential errors. This paper presents context-aware conditional functional dependencies（CCFDs） which contribute to provide reasonable values and detect po- tential errors. Especially, we focus on automatically discov- ering minimal CCFDs. In this paper, we present context rela- tivity to measure the relationship of CFDs. The overlap of the related CFDs can provide reasonable values which result in more accuracy consistency repairing, and some related CFDs are combined into CCFDs. Moreover, we prove that discover- ing minimal CCFDs is NP-complete and we design the pre- cise method and the heuristic method. We also present the dominating value to facilitate the process in both the precise method and the heuristic method. Additionally, the context relativity of the CFDs affects the cleaning results. We will give an approximate threshold of context relativity accord- ing to data distribution for suggestion. The repairing results are approved more accuracy, even evidenced by our empirical evaluation.

关键词： conditional functional dependencies contextaware rules discovery

Biomedical entity linking based on less labeled data

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Frontiers of Computer Science 2022年第3期16卷 219-221页

作者： Yu HU Derong SHEN tiezheng nie Yue KOU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

1 Introduction Biomedical entity linking aims to align the natural language entity to the knowledge base concept referring to the same real-world *** solutions in the biomedical field focus on the embedding-based method to jointly model the texts and the knowledge graph into a multi-dimensional entity ***,the task of a biomedical entity linking is still facing tough challenges:1)The ambiguity of natural language descriptions,including polysemy and abbreviation.

关键词： abbreviation tough ambiguity

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Efficient private multi-party numerical records matching

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Frontiers of Computer Science 2020年第5期14卷 233-235页

作者： Shumin Han Derong Shen tiezheng nie Yue Kou Ge Yu College of Computer Science and Engineering Northeastern UniversityShenyang110167China

1 Introduction and main contributions Private entity matching(PEM)[1]is to find records from two or more data sources that refer to the same or similar individuals,without revealing other information besides the match... 详细信息

关键词： matching matched revealing

A genetic algorithm based entity resolution approach with active learning

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Frontiers of Computer Science 2017年第1期11卷 147-159页

作者： Chenchen SUN Derong SHEN Yue KOU tiezheng nie Ge YU Sohool of Information Science and Engineering Northeastern University Shenyang 110819 China

Entity resolution is a key aspect in data quality and data integration, identifying which records correspond to the same real world entity in data sources. Many existing ap- proaches require manually designed match rules to solve the problem, which always needs domain knowledge and is time consuming. We propose a novel genetic algorithm based en- tity resolution approach via active learning. It is able to learn effective match rules by logically combining several different attributes＇ comparisons with proper thresholds. We use ac- tive learning to reduce manually labeled data and speed up the learning process. The extensive evaluation shows that the proposed approach outperforms the sate-of-the-art entity res- olution approaches in accuracy.

关键词： entity resolution genetic algorithm active learning data quality data integration

SAMES： deadline-constraint scheduling in MapReduce

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Frontiers of Computer Science 2015年第1期9卷 128-141页

作者： Xite WANG Derong SHEN Mei BAI tiezheng nie Yue KOU Ge YU College of Information Science & Engineering Northeastern University Shenyang 110819 China

MapReduce is a popular parallel data-processing system, and task scheduling is one of the kernel techniques in MapReduce. In many applications, users have requirements that their MapReduce jobs should be completed before specific deadlines. Hence, in this paper, a novel scheduling algorithm based on the most effective sequence （SAMES） is proposed for deadline-constraint jobs in MapReduce. First, according to the characteristics of MapReduce, we propose a novel sequence-based execution strategy for MapReduce jobs and a new concept, the effective sequence （ES）. Then, we design some efficient approaches for finding ESes and choose the most effective sequence （MES） for job execution. We also propose methods for MES-updates and exception handling. Finally, we verify the effectiveness of SAMES through experiments. The experimental results show that SAMES is an efficient scheduling algorithm for deadline-constraint jobs in MapReduce.

关键词： MapReduce scheduling deadline

HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data

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Big Data Mining and Analytics 2018年第2期1卷 146-159页

作者： Meihui Shi Derong Shen tiezheng nie Yue Kou Ge Yu the College of Computer Science and Engineering Northeastern University

A lot of scholars have focused on developing effective techniques for package queries, and a lot of excellent approaches have been proposed. Unfortunately, most of the existing methods focus on a small volume of data. The rapid increase in data volume means that traditional methods of package queries find it difficult to meet the increasing requirements. To solve this problem, a novel optimization method of package queries(HPPQ) is proposed in this paper. First, the data is preprocessed into regions. Data preprocessing segments the dataset into multiple subsets and the centroid of the subsets is used for package queries, this effectively reduces the volume of candidate results. Furthermore, an efficient heuristic algorithm is proposed(namely IPOL-HS) based on the preprocessing results. This improves the quality of the candidate results in the iterative stage and improves the convergence rate of the heuristic algorithm. Finally, a strategy called HPR is proposed, which relies on a greedy algorithm and parallel processing to accelerate the rate of query. The experimental results show that our method can significantly reduce time consumption compared with existing methods.

关键词： package queries heuristic algorithms parallel processing opposition-based learning

Extracting Result Schema Based on Query Instances in the Deep Web

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Wuhan University Journal of Natural Sciences 2007年第5期12卷 835-839页

作者： nie tiezheng YU Ge SHEN Derong KOU Yue LIU Wei College of Information Science and Engineering Northeastern University Shenyang 110004 Liaoning China

Deep Web sources contain a large of high-quality and query-related structured date. One of the challenges in the Deep Web is extracting result schemas of Deep Web sources. To address this challenge, this paper describes a novel approach that extracts both result data and the result schema of a Web database. The approach first models the query interface of a Deep Web source and fills in it with a specifically query instance. Then the result pages of the Deep Web sources are formatted in the tree structure to retrieve subtrees that contain elements of the query instance, Next, result schema of the Deep Web source is extracted by matching the subtree＇ nodes with the query instance, in which, a two-phase schema extraction method is adopted for obtaining more accurate result schema. Finally, experiments on real Deep Web sources show the utility of our approach, which provides a high precision and recall.

关键词： Deep Web schema extraction result schema query instance