2021 IEEE International Conference on Big Knowledge (ICBK)最新文献_第2页

Unsupervised Type Constraint Inference in Bilinear Knowledge Graph Completion Models 双线性知识图补全模型中的无监督型约束推理

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00012

Yuxun Lu, R. Ichise

引用次数: 0

Fuzzy c-Means Clustering with Discriminative Projection 判别投影模糊c均值聚类

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00062

Wenjun Wu, Lingling Zhang, Yiwei Chen, Xuan Luo, Bifan Wei, Jun Liu

{"title":"Fuzzy c-Means Clustering with Discriminative Projection","authors":"Wenjun Wu, Lingling Zhang, Yiwei Chen, Xuan Luo, Bifan Wei, Jun Liu","doi":"10.1109/ICKG52313.2021.00062","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00062","url":null,"abstract":"The clustering technique plays an important role in data mining and machine learning fields. Clustering for high-dimensional data, such as texts, images, and videos, remains a challenging task due to the existence of many noise features. The widely used methods for this issue focus on mining a effective pattern in high-dimensional data using some dimensionality reduction techniques before clustering. This strategy slightly mitigates the effects of irrelevant and redundant features, but cannot significantly improve the clustering performance because the captured pattern by dimensionality reduction is not directly related to the clustering task. In this paper, we propose a unified framework to achieve discriminative dimensionality reduction and fuzzy clustering for high-dimensional data simultaneously. The proposed framework not only utilizes the clustering results to directly guide or supervise the process of discriminative dimensionality reduction, but also controls the clustering fuzziness more easily by a $F$ -norm regularization term. An efficient optimization algorithm is exploited to address the objective function of our method, which is proved to converge to the local optimal solution in theory. We evaluate the proposed method on three large-scale fine-grained image datasets, including Birds, Flowers, and Cars, for clustering and retrieval two tasks. The experimental results on metrics ACC, NMI, ARI and Recall@K indicate that our method achieves the comparable performance over the state-of-the-art methods.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"31 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122445511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global Semantics with Boundary Constraint Knowledge Graph for Chinese Financial Event Detection 基于边界约束知识图的全局语义中文金融事件检测

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00045

Yin Wang, Nan Xia, Xiangfeng Luo, Jinhui Li

{"title":"Global Semantics with Boundary Constraint Knowledge Graph for Chinese Financial Event Detection","authors":"Yin Wang, Nan Xia, Xiangfeng Luo, Jinhui Li","doi":"10.1109/ICKG52313.2021.00045","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00045","url":null,"abstract":"Chinese financial event detection has a great significance in the application of financial risk analysis, en-terprise management and decision-making. The existing tasks of Chinese event detection are mainly regarded as character-based or word-based classification, which suffers from the ambiguity of trigger words. These tasks only concentrate on local information (e.g character and word), which loses sight of global information like sentence semantics. Furthermore, in the finance field, there exists the problem of fuzzy boundary between different event types. In this paper, we propose a global semantics with boundary constraint knowledge graph (BCKG) for Chinese financial event detection, which considers both sentence semantics and boundary knowledge. At first, Chinese financial dataset (CFD) is constructed by considering the complexity in financial area. And then, the sentence seman-tics embedding is obtained by pre-training BERT fine-tuning mechanism to address the problem of ambiguity of trigger words, which considers both syntactic information and context sentence semantics comprehensively. Finally, we construct the BCKG for financial event, which can add additional prior knowledge to solve fuzzy boundary problem. The proposed method for event detection achieves outstanding performance on standard ACE 2005 Chinese dataset and constructed CFD. The experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Answer-Centric Local and Global Information Fusion for Conversational Question Generation 会话问题生成中以答案为中心的局部和全局信息融合

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00067

Panpan Lei, Xiao Sun

{"title":"Answer-Centric Local and Global Information Fusion for Conversational Question Generation","authors":"Panpan Lei, Xiao Sun","doi":"10.1109/ICKG52313.2021.00067","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00067","url":null,"abstract":"Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124959532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Knowledge Enhanced Chinese GaoKao Reading Comprehension Method 一种知识强化的中国高考阅读理解方法

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00053

Xiao Zhang, Heqi Zheng, Heyan Huang, Zewen Chi, Xian-Ling Mao

引用次数: 0

UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases 一种发现不确定图数据库中频繁子图模式的新技术

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00042

Riddho Ridwanul Haque, Chowdhury Farhan Ahmed, M. Samiullah, C. Leung

{"title":"UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases","authors":"Riddho Ridwanul Haque, Chowdhury Farhan Ahmed, M. Samiullah, C. Leung","doi":"10.1109/ICKG52313.2021.00042","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00042","url":null,"abstract":"Large graph data repositories are becoming in-creasingly common. Identifying frequently appearing subgraph patterns in such databases can reveal useful information, and such patterns have been used for a variety of applications. Im-perfections and stochasticity are often unavoidable in real-world graph data, and the existence of edges in the graphs within such databases is often uncertain. Taking this uncertainty into account while mining frequent patterns poses considerable computational challenges. However, doing so is crucial for accurately mining relevant patterns. Existing frequent subgraph mining approaches that consider uncertainty rely on approximation schemes, and are both inefficient and inaccurate. In this paper, we present UFreS, an exact algorithm for mining frequent subgraph patterns from uncertain graph databases. We also introduce Edge-Embedding graphs, the first data structure designed to efficiently and exactly infer the expected support of a subgraph pattern in an uncer-tain graph. Experimental evaluations conducted on real-world datasets show that UFreS is efficient, scalable, and outperforms the existing approaches in terms of runtime, memory usage and accuracy.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters ToFM:在集群内通过Facet传播进行特定主题的Facet挖掘

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00060

Hongxuan Li, Bifan Wei, Jun Liu, Zhaotong Guo, Jingchao Qi, Bei Wu, Yong Liu, Yuanyuan Shi

{"title":"ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters","authors":"Hongxuan Li, Bifan Wei, Jun Liu, Zhaotong Guo, Jingchao Qi, Bei Wu, Yong Liu, Yuanyuan Shi","doi":"10.1109/ICKG52313.2021.00060","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00060","url":null,"abstract":"Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132840288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Question-formed Query Suggestion 问题形成的查询建议

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00071

Y. He, Xian-Ling Mao, Wei Wei, Heyan Huang

引用次数: 1

HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining HSNP-Miner:高实用自适应无重叠模式挖掘

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00019

Motaher Hossain, Youxi Wu, Philippe Fournier-Viger, Zhao Li, Lei Guo, Yan Li

{"title":"HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining","authors":"Motaher Hossain, Youxi Wu, Philippe Fournier-Viger, Zhao Li, Lei Guo, Yan Li","doi":"10.1109/ICKG52313.2021.00019","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00019","url":null,"abstract":"Sequential pattern mining (SPM) under the nonoverlapping condition (or nonoverlapping SPM) is a type of data mining used to extract frequent gapped subsequences (known as patterns) from sequences, which is more valuable and versatile than other related methods. In nonoverlapping SPM, two occurrences cannot reuse the same sequence letter in the exact location as the occurrences. This method evaluates the frequency of the patterns in the sequence, and ignores the impact of external utility (item price or profit). Therefore, some low-frequency and essential patterns are overlooked. To address this issue, this paper introduces High Utility Self-adaptive Nonoverlapping Pattern (HSNP) mining and proposes HSNP-Miner, which includes two steps: support calculation and candi-date pattern generation. To calculate the support, we propose the NoSup algorithm, which can effectively calculate support while avoiding the creation of redundant nodes. An advanced upper bound method is employed to generate the candidate patterns more efficiently. Compared to other competitive methods, the experimental results demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence pat-tarns.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System 面向在线教育系统分层多标签分类的一致性感知多模态网络

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI: 10.1109/ICKG52313.2021.00063

Siqi Lei, Wei Huang, Shiwei Tong, Qi Liu, Zhenya Huang, Enhong Chen, Yu Su

{"title":"Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System","authors":"Siqi Lei, Wei Huang, Shiwei Tong, Qi Liu, Zhenya Huang, Enhong Chen, Yu Su","doi":"10.1109/ICKG52313.2021.00063","DOIUrl":"https://doi.org/10.1109/ICKG52313.2021.00063","url":null,"abstract":"In the online education system, predicting the knowledge of exercises is a fundamental task of many applications, such as cognitive diagnosis. Usually, experts consider this problem as Hierarchical Multi-label Classification (HMC), since the knowledge concepts exhibit a multi-level structure. However, existing methods either sacrificed knowledge consistency for classification accuracy or sacrificed classification accuracy for knowledge consistency. Maintaining the balance is difficult. To forgo this dilemma, in this paper, we develop a novel frame-work called Consistency-Aware Multi-modal Network (Cam-Net). Specifically, we develop a multi-modal embedding module to learn the representation of the multi-modal exercise. Then, we adopt a hybrid prediction method consisting of the flat prediction module and the local prediction module. The local prediction module deals with the relation between the knowledge hierarchy and the input exercise. The flat prediction module focuses on maintaining knowledge consistency. Finally, to balance classification accuracy and knowledge consistency, we combine the outputs of two modules to make a final prediction. Extensive experimental results on two real-world datasets demonstrate the high performance and the ability to reduce knowledge inconsistency of CamNet.","PeriodicalId":174126,"journal":{"name":"2021 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122395402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0