2020 IEEE International Conference on Knowledge Graph (ICKG)最新文献_第4页

PRNSGA-II: A Novel Approach for Influence Maximization and Cost Minimization Based on NSGA-II PRNSGA-II:基于NSGA-II的影响最大化和成本最小化新方法

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00063

Fulan Qian, Cunliang Zhu, Xi Chen, Shu Zhao, Yanping Zhang

{"title":"PRNSGA-II: A Novel Approach for Influence Maximization and Cost Minimization Based on NSGA-II","authors":"Fulan Qian, Cunliang Zhu, Xi Chen, Shu Zhao, Yanping Zhang","doi":"10.1109/ICBK50248.2020.00063","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00063","url":null,"abstract":"Influence maximization aims to extract a k-size seed node set to get a maximum influence spread under a specific propagation model which is a popular research topic in viral marketing these years. Companies want to select influential people to help them increase the sales of productions. With the limited budget, companies are unable to afford the huge cost of finding influential people. Thus, how to solve the multi-objective problem i.e. influence maximization problem and cost minimization problem (IM-CM) attracts more researchers attentions. In this paper, we propose a novel framework called PRNSGA-II to solve IM-CM. As an important index in complex networks, PageRank describes the importance of nodes. So we add PageRank to our first objective function to improve the quality of seed nodes. Then to calculate the cost of nodes, we use the degree centrality as our second objective function. Finally, we adopt NSGA-II which is a classical and effective multi-objective framework to solve IM-CM. We use three public datasets to verify our algorithm. The results of experiments demonstrate the effectiveness of our algorithm.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125001398","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: 2

Context and Type Enhanced Representation Learning for Relation Extraction 面向关系抽取的上下文和类型增强表示学习

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00054

Erxin Yu, Yantao Jia, Shang Wang, Fengfu Li, Yi Chang

{"title":"Context and Type Enhanced Representation Learning for Relation Extraction","authors":"Erxin Yu, Yantao Jia, Shang Wang, Fengfu Li, Yi Chang","doi":"10.1109/ICBK50248.2020.00054","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00054","url":null,"abstract":"Relation extraction from plain text aims to extract relational facts between entities in the text, and plays an important role in knowledge graph construction, question answering, and so on. Distant supervision based methods employ an external knowledge graph to automatically generate the training data of relations between entities via linking the entities in the knowledge graph to their mentions in the texts. However, due to the noise in texts, these methods often suffer from the wrong labelling problem. To address this issue, we propose a context and type enhanced representation learning method for relation extraction (CTRL-RE). Specifically, to avoid the noise in texts, the global context information for entities within a given window size in the texts is used to generate the context-based representations of entities. The type of entities is utilized to generate the type-based representations of the entities. Then these two representations of the entities are combined with the representation of relations to form a context and type enhanced knowledge graph representation learning method for relation extraction. Experiments on benchmark datasets show our proposed method can achieve superior performance compared to analogous methods.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322126","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

Learning Knowledge Embeddings with Prior Weights for Sparse Interaction Recommendation 基于先验权值的知识嵌入学习稀疏交互推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00039

Deqing Yang, Zikai Guo, Yanghua Xiao

{"title":"Learning Knowledge Embeddings with Prior Weights for Sparse Interaction Recommendation","authors":"Deqing Yang, Zikai Guo, Yanghua Xiao","doi":"10.1109/ICBK50248.2020.00039","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00039","url":null,"abstract":"Knowledge-based recommendation models have exhibited their excellent performance in recent years. Most of these models encode knowledge into item embeddings through a graph embedding algorithm, which are useful for uncovering correlations between users and items. However, the graph embedding algorithms in these models neglect the different weights of various relations between items (entities), thus imprecise embeddings are learned resulting in unsatisfactory recommendation results. To address this problem, we propose a deep knowledge-based recommendation model which incorporates a novel graph embedding algorithm with prior relation weights, to learn precise item embeddings. Specifically, an HIN is first constructed based on the entities and relations from open knowledge graphs (KGs). Then, the embeddings of item vertices in the HIN are learned through seeking similar items in terms of various attributes (relations) with different prior weights. Next, the user representations are learned through user-tag-item relationships, based on which recommendation results are obtained by a multi-layer perceptron (MLP) fed with user presentations and item representations (embeddings). All the embeddings learned in our model are regarded as knowledge embeddings. The extensive experiments show that, our model outperforms the previous KG-based recommendation models with help of precise knowledge embeddings. Furthermore, it owns robust performance in the scenario of sparse user-item interactions, since it captures user preferences mainly based on the knowledge rather than observed user-item interactions.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134103138","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

Neural Collaborative Recommendation with Knowledge Graph 基于知识图谱的神经协同推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00038

Lei Sang, Lei Li

{"title":"Neural Collaborative Recommendation with Knowledge Graph","authors":"Lei Sang, Lei Li","doi":"10.1109/ICBK50248.2020.00038","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00038","url":null,"abstract":"Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities’ long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"258-260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749138","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

Two-stage Sequential Recommendation via Bidirectional Attentive Behavior Embedding and Long/Short-term Integration 基于双向注意行为嵌入和长短期整合的两阶段顺序推荐

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00070

Wendi Ji, Yinglong Sun, Tingwei Chen, Xiaoling Wang

{"title":"Two-stage Sequential Recommendation via Bidirectional Attentive Behavior Embedding and Long/Short-term Integration","authors":"Wendi Ji, Yinglong Sun, Tingwei Chen, Xiaoling Wang","doi":"10.1109/ICBK50248.2020.00070","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00070","url":null,"abstract":"In E-commerce applications, to predict what users will buy next is a crucial mission of sequential recommendation. Most frontier researches build end-to-end training models for sequential recommendation tasks via RNNs, CNNs or attentive models. However, a user’s historical behavior sequence carries more complex contextual information than words. In this paper, we propose a two-stage user modeling framework for sequential recommendation, which is consisted by a Bidirectional Self-attentive Behavior Embedding and a Long/Short-term Sequential Behavior Predictor. Firstly, in order to expand perceivable information, a novel self-attentive behavior embedding method is proposed to learn semantic representations not only for items, but also for other important contextual factors (e.g. actions, categories and time). Then, with the pre-trained behavior embeddings, we propose a personalized memory network for Top-N recommendation. We use recurrent network to encode the short-term intent and learn the personalized long-term memory by a self-attention block. To integrate the long/short-term preferences, we generate the predicted behavior representation by using the present intent as a query to match with user’s historical preferences via attentive memory reader. Finally, we conduct extensive experiments on two benchmark datasets provided by Tmall and Amazon. Compared with state-of-the-art techniques, experimental results demonstrate the effectiveness of our proposed framework.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129783585","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

Rule-enhanced Noisy Knowledge Graph Embedding via Low-quality Error Detection 基于低质量错误检测的规则增强噪声知识图嵌入

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00082

Y. Hong, Chenyang Bu, Tingting Jiang

引用次数: 6

GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction GCN-ALP:锚链预测中的匹配冲突寻址

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00065

Hao Gao, Yongqing Wang, Shanshan Lyu, Huawei Shen, Xueqi Cheng

{"title":"GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction","authors":"Hao Gao, Yongqing Wang, Shanshan Lyu, Huawei Shen, Xueqi Cheng","doi":"10.1109/ICBK50248.2020.00065","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00065","url":null,"abstract":"Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem anchor link prediction is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice. In this paper, we explore local structure consistency and then construct a matching graph in order to circumvent matching collisions. Furthermore, we propose graph convolution networks with mini-batch strategy, efficiently solving anchor link prediction on matching graph. The experimental results on three real application scenarios show the great potentials of our proposed method in both prediction accuracy and efficiency. In addition, the visualization of learned embeddings provides us a qualitative way to understand the inference of anchor links on the matching graph.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133184799","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: 5

TransP: A New Knowledge Graph Embedding Model by Translating on Positions* TransP:一种新的基于位置翻译的知识图嵌入模型*

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00056

Feiliang Ren, Jucheng Li, Huihui Zhang, Xiaochun Yang

{"title":"TransP: A New Knowledge Graph Embedding Model by Translating on Positions*","authors":"Feiliang Ren, Jucheng Li, Huihui Zhang, Xiaochun Yang","doi":"10.1109/ICBK50248.2020.00056","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00056","url":null,"abstract":"Embedding knowledge graph into continuous space(s) is attracting more and more research attention, and lots of novel methods have been proposed. Among them, translation based methods achieved state-of-the-art experimental results. However, most of existing work ignore following two facts. First, once a relation is fixed, its linked head and tail entities will be fixed to a certain extent. Second, in a triplet, if one of its entities and the relation are fixed, the other entity’s candidates will also be fixed to a certain extent. Taking these two facts into consideration, we propose a new knowledge graph embedding model named TransP, which defines a head entity space and a tail entity space for each relation. During embedding, TransP first projects entities into these two position spaces. Then the entities in these two position spaces are further projected into a common transformation space, in which the relation is converted into two transformation matrices. A symmetrical score function is designed to connect a correct triplet’s head and tail entity in the common space. The basic idea behind this score function is that if a correct triplet holds, its head (tail) entity should be able to be converted into its tail (head) entity when taking the relation’s transformation matrix as an intermediate bridge. Viewing the transformation matrices as decoders, this process is just like a common translation process. We evaluate TransP on triplet classification task and link prediction task. Extensive experiments show that TransP achieves much better performance than other baseline models.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"742 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122006531","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

Decentralized Mining Pool Games in Blockchain 区块链中的去中心化矿池游戏

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00067

Zhihuai Chen, Xiaoming Sun, Xiaohan Shan, Jialin Zhang

引用次数: 3

Robust and Accurate Representation Learning for High-dimensional and Sparse Matrices in Recommender Systems 推荐系统中高维稀疏矩阵的鲁棒准确表示学习

2020 IEEE International Conference on Knowledge Graph (ICKG) Pub Date : 2020-08-01 DOI: 10.1109/ICBK50248.2020.00075

Di Wu, Gang Lu, Zhicheng Xu

{"title":"Robust and Accurate Representation Learning for High-dimensional and Sparse Matrices in Recommender Systems","authors":"Di Wu, Gang Lu, Zhicheng Xu","doi":"10.1109/ICBK50248.2020.00075","DOIUrl":"https://doi.org/10.1109/ICBK50248.2020.00075","url":null,"abstract":"How to accurately represent a high-dimensional and sparse (HiDS) user-item rating matrix is a crucial issue in implementing a recommender system. A latent factor (LF) model is one of the most popular and successful approaches to address this issue. It is developed by minimizing the errors between the observed entries and the estimated ones on an HiDS matrix. Current studies commonly employ L2-norm to minimize the errors because it has a smooth gradient, making a resultant LF model can accurately represent an HiDS matrix. As is well known, however, L2-norm is very sensitive to the outlier data or called unreliable ratings in the context of the recommender system. Unfortunately, the unreliable ratings often exist in an HiDS matrix due to some malicious users. To address this issue, this paper proposes a Smooth L1-norm-oriented Latent Factor (SL1-LF) model. Its main idea is to employ smooth L1-norm rather than L2-norm to minimize the errors, making it have both high robustness and accuracy in representing an HiDS matrix. Experimental results on four HiDS matrices generated by industrial recommender systems demonstrate that the proposed SL1-LF model is robust to the outlier data and has significantly higher prediction accuracy than state-of-the-art models for the missing data of an HiDS matrix.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873055","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