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5th china conference on knowledge graph, and semantic computing, ccks 2020

ISBN: (纸本)9789811619632

the proceedings contain 26 papers. the special focus in this conference is on knowledge graph, and semantic computing. the topics include: IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extraction;hybrid Syntactic graph Convolutional Networks for Chinese Event Detection;meta Learning for Event Argument Extraction via Domain-Specific Information Enhanced;a Survey on Event Relation Identification;employing Multi-cues to Identify Event Factuality;end-to-End Event Factuality Identification via Hybrid Neural Networks;on Building a knowledge graph of Open Courses;improving Question Answering over knowledge Base with External Linguistic knowledge;effectively Incorporating knowledge in Open-Domain Dialogue Generation;sememe Tree Prediction for English-Chinese Word Pairs;what Linguistic Information Does Reading Comprehension Require?;enhancing Embedding via Two-Level Features for Machine Reading Comprehension;obstetric Diagnosis Assistant via knowledge Powered Attention and Information-Enhanced Strategy;emotion Role Identification in Social Network;academic Field and Future Influence Prediction for Scholar Profile Construction;exploiting knowledge Embedding to Improve the Description for Image Captioning;knowledge-Enhanced Collaborative Meta Learner for Long-Tail Recommendation;FGN: Fusion Glyph Network for Chinese Named Entity Recognition;semantic Label Enhanced Named Entity Recognition with Incompletely Annotated Data;Named Entity Recognition Using a Semi-supervised Model Based on BERT and Bootstrapping;reinforcement Learning for Clue Selection in Web-Based Entity Translation Mining;neural Fusion Model for Chinese semantic Matching;improving Relation Extraction Using semantic Role and Multi-task Learning;learning Global Representations for Document-Level Biomedical Relation Extraction.

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Machine reading comprehension based named entity recognition for medical text

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Multimedia Tools and Applications 2025年 1-21页

作者： Zhang, Ziqi Zheng, Xiangwei Zhang, Jinsong School of Information Science and Engineering Shandong Normal University Jinan China

Electronic Medical Record (EMR) has been widely utilized in disease prevention, clinical diagnosis and scientific research. EMR mining based on Named Entity Recognition (NER) has become a research hotspot in the healthcare field. However, NER faces challenges of poor context modeling ability and severe entity nesting. In order to deal with the above challenges, this paper proposes a Machine Reading Comprehension based Named Entity Recognition for medical text (MRCNER). First, the context sequence and the question are inputs to an embedding and encoding module composed of BERT, Dropout, and BiLSTM to obtain text vector representations and problem vector representations that contain global semantics, as well as better understanding of the relationship between language structure and context. then, the bidirectional attention is adopted for the aim of fully learning the semantic association between text and entity categories, and focusing more on key information. Next, we propose an answer prediction module. It is consisted of a start index classifier, an end index classifier, and a match classifier. the first two classifiers determine the answer by predicting its starting and ending positions in the original context, avoiding the generation of discontinuous named entities. the match classifier is designed to predict whether the starting position and the ending position match. Finally, the predicted results are obtained. We evaluated MRCNER on the ccks2020 (china conference on knowledge graph and semantic computing) dataset and demonstrated that the accuracy, recall and F1 value of the model have all been improved to 84.48%, 82.96% and 83.71%, respectively. © the Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： semantics

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china conference on knowledge graph and semantic computing and International Joint conference on knowledge graphs 1

China Conference on Knowledge Graph and Semantic Computing a...

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丛书名： Communications in Computer and Information Science

1000年

作者： Bin Xu Bing Qin Ming Liu Yannis Tzitzikas Zhichun Wang Xian-Ling Mao Pavlos Fafalios Yongbin Liu Zhiliang Tian Michalis Mountantonakis

ISBN: (数字)9789819618095

ISBN: (纸本)9789819618088

this book constitutes the joint refereed proceedings of the 18th china conference on knowledge graph and semantic computing and the 13th International Joint conference on knowledge graphs, ccks-IJCKG 2024, held in Chongqing, china, during September 20–22, 2024. the 30 full papers and 11 other papers presented in this volume were carefully reviewed and selected from 168 submissions. they are organized in the following topical sections: knowledge representation and reasoning; knowledge graph construction and knowledge integration; graph database and knowledge management; Machine learning on graphs; knowledge retrieval and information retrieval; knowledge graph and large language model applications; knowledge graph open resources; Poster and demo; Evaluations.

关键词： Artificial Intelligence Computer Applications Information Storage and Retrieval Database Management Data Mining and knowledge Discovery

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Domain knowledge Aggregation Model Based on knowledge graph and Large Language Model

Domain Knowledge Aggregation Model Based on Knowledge Graph ...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Peng Ye Chunju Zhang Junxi Du Xueying Zhang Urban Planning and Development Institute Yangzhou University Yangzhou China School of Civil Engineering Hefei University of Technology Hefei China Key Laboratory of Virtual Geographic Environment (Ministry of Education) Nanjing Normal University Nanjing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Traditional knowledge aggregation methods are mostly limited to the physical splicing of data and information resources, lacking deep semantic correlations. Domain knowledge aggregation has emerged as a crucial research direction to enhance knowledge organization, management, and services in specialized fields. this study constructs a graph-model-driven domain knowledge aggregation framework based on knowledge graphs and large language models. By integrating the semantic computing capabilities of knowledge graphs with the natural language generation capabilities of large language models, we propose a multi-layered model encompassing knowledge resource collection, semantic association mining, text generation, and service interface integration. the study reveals that the proposed framework lays a foundation for a new paradigm in domain knowledge aggregation. the integration of knowledge graphs and large language models enhances the comprehensibility and reliability of aggregated knowledge, supporting the development of domain knowledge-sharing service platforms and open-service engines.

关键词： knowledge engineering Large language models Splicing semantics Refining knowledge graphs Organizations Reliability Usability Engines

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Intelligent Career Advisory System: Enhancing Career Matching with BERT and graph Neural Networks

Intelligent Career Advisory System: Enhancing Career Matchin...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Min Hua Mideth Abisado College of Computing and Information Technologies National University Manila Philippine School of Information Engineering Xinyang Agriculture and Forestry University Xinyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Traditional career recommendation systems often fail to capture dynamic skill demands, subtle query semantics, and potential ethical and bias issues. In this paper, we propose a hybrid model that integrates BERT for semantic understanding and graph Neural Networks (GNN) for relational reasoning to construct a dynamically updated career knowledge graph. the graph is built by fusing multi-source data from LinkedIn, O*NET, and synthetic data generated via GPT-4, and a cross-modal attention mechanism is used to fuse text and graph features. Experiments on the CareerQA dataset, consisting of 50,000 annotated queries, demonstrates that our model improves the Fl-score by 18.7% and achieves an NDCG@5 of 0.921. A user study involving 1,200 participants further validates the system's practical effectiveness with an 89.2 % positive feedback rate.

关键词： Ethics Engineering profession Social networking (online) Fuses semantics knowledge graphs graph neural networks Real-time systems Recommender systems Synthetic data

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Overview of ccks 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

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Data Intelligence 2021年第3期3卷 376-388页

作者： Xia Li Qinghua Wen Hu Lin Zengtao Jiao Jiangtao Zhang The 305th Hospital of the Chinese People’s Liberation Army Wenjin StreetXicheng DistrictBeijing 100017China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Yiducloud Beijing Technology Co. Ltd.Huayuan North RoadHaidian DistrictBeijing 100089China

the china conference on knowledge graph and semantic computing(ccks)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical *** annotated data sets and some other additional resources for these two subtasks were provided for *** evaluation competition attracted 354 teams and 46 of them successfully submitted the valid *** pre-trained language models are widely applied in this evaluation *** argumentation and external resources are also helpful.

关键词： Chinese electronic medical records Event extraction Named entity recognition Clinical text ccks

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Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records 1

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6th china conference on knowledge graph and semantic computing (ccks)

作者： Ma, Cheng Huang, Wenkang Fudan Univ Shanghai Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (数字)9789811907135

ISBN: (纸本)9789811907135;9789811907128

the china conference on knowledge graph and semantic computing (ccks) 2021 Evaluation Task 4 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets for the two subtasks were provided for participators. Our model on the test dataset achieves the strict F1Measure of 0.7684 which ranked the first place.

关键词： ccks Named entity recognition Event extraction

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An Integrated Method of semantic Parsing and Information Retrieval for knowledge Base Question Answering 1

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6th china conference on knowledge graph and semantic computing (ccks)

作者： Zhen, Shiqi Yi, Xianwei Lin, Zhishu Xiao, Weiqi Su, Haibo Liu, Yijing Cognit Intelligence LabPercent Technol Wuhan 430073 Peoples R China

ISBN: (数字)9789811907135

ISBN: (纸本)9789811907135;9789811907128

For the task of open domain knowledge Base Question Answering (KBQA) in ccks 2021, we propose an integrated method, which complementarily combines a more generalized information retrieval model and a more accurate semantic parsing model without manual involvement of templates. Our method achieves the averaged F1-score of 78.52% on the final test data, and ranks third in the KBQA task of ccks 2021.

关键词： KBQA Information retrieval semantic parsing

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A Joint Training Framework Based on Adversarial Perturbation for Video semantic Tags Classification 1

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6th china conference on knowledge graph and semantic computing (ccks)

作者： Ye, Zhenxu Zhang, Yaoyuan Cao, Shengming Tencent Shenzhen Peoples R China Microsoft Dublin Ireland

ISBN: (数字)9789811907135

ISBN: (纸本)9789811907135;9789811907128

In this paper, we present a solution of "knowledge Enhanced Video semantic Understanding" of 2021 ccks Track 14th task [1]. We separate video semantic understanding framework into two related tasks, namely the multi-classes video cate classification (VCC) task and the multi-label video tag classification (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final leaderboard, we achieved 3rd place in the competition. the source code has been at Github (https:// ***/stone-yzx/2021-ccks-Trace14-3rd-semantic-tag-classification).

关键词： Video cate classification Video tag classification Adversarial perturbations

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the Method for Plausibility Evaluation of knowledge Triple Based on QA 7th

The Method for Plausibility Evaluation of Knowledge Triple B...

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7th china conference on knowledge graph and semantic computing (ccks)

作者： Jia, Shutong Cao, Jiuxin Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China

ISBN: (纸本)9789811982996;9789811983009

At present, most of the methods for knowledge graph completion (KGC) task highly rely on external knowledge base or graph representation learning. However, how to complete this task without using any external prior knowledge is still a huge challenge and difficulty. To this end, we propose a novel framework which converts the plausibility evaluation of knowledge triple task to the question and answer (QA) task with the thought of KG-BERT and prompt learning. We also test the effect of different question types on the results. Secondly, by fine-tuning two pre-trained language models BERT-wwm-ext and ERNIE-Gram on these generated sequences, so that they can complete the QA task. We won the 5th place at ccks 2022 track 1 rematch stage, which proved the effectiveness of our method.

关键词： knowledge graph completion knowledge triple Pre-trained language model Question and answer

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