Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining最新文献_第10页

Knowledge is Power: Symbolic Knowledge Distillation, Commonsense Morality, & Multimodal Script Knowledge 知识就是力量:符号知识升华、常识性道德与多模态文字知识

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3500242

Yejin Choi

{"title":"Knowledge is Power: Symbolic Knowledge Distillation, Commonsense Morality, & Multimodal Script Knowledge","authors":"Yejin Choi","doi":"10.1145/3488560.3500242","DOIUrl":"https://doi.org/10.1145/3488560.3500242","url":null,"abstract":"Scale appears to be the winning recipe in today's AI leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge. First, I will introduce \"symbolic knowledge distillation\", a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will present an experimental conceptual framework toward computational social norms and commonsense morality, so that neural language models can learn to reason that \"helping a friend\" is generally a good thing to do, but \"helping a friend spread fake news\" is not. Finally, I will discuss an approach to multimodal script knowledge demonstrating the power of complex raw data, which leads to new SOTA performances on a dozen leaderboards that require grounded, temporal, and causal commonsense reasoning.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134354730","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

From Uni-relational to Multi-relational Graph Neural Networks 从单关系到多关系图神经网络

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3502219

Juanhui Li

引用次数: 1

Hierarchical Imitation Learning via Subgoal Representation Learning for Dynamic Treatment Recommendation 基于子目标表示学习的分层模仿学习动态治疗推荐

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498535

Lu Wang, Ruiming Tang, Xiaofeng He, Xiuqiang He

{"title":"Hierarchical Imitation Learning via Subgoal Representation Learning for Dynamic Treatment Recommendation","authors":"Lu Wang, Ruiming Tang, Xiaofeng He, Xiuqiang He","doi":"10.1145/3488560.3498535","DOIUrl":"https://doi.org/10.1145/3488560.3498535","url":null,"abstract":"Dynamic Treatment Recommendation (DTR) is a sequence of tailored treatment decision rules which can be grouped as individual sub-tasks. As the reward signals in DTR are hard to design, Imitation Learning (IL) has achieved great success as it is effective in mimicking doctors' behaviors from their demonstrations without explicit reward signals. As a patient may have several different symptoms, the behaviors in doctors' demonstrations can often be grouped to handle individual symptoms. However, a single flat policy learned by IL is difficult to mimic doctors' demonstrations with such hierarchical structure, where low-level behaviors are switching from one symptom to another controlled by high-level decisions. Due to this observation, we consider Hierarchical Imitation Learning methods as good solutions for DTR. In this paper, we propose a novel Subgoal conditioned HIL framework (short for SHIL), where a high-level policy sequentially sets a subgoal for each sub-task without prior knowledge, and the low-level policy for sub-tasks is learned to reach the subgoal. To get rid of prior knowledge, a self-supervised learning method is proposed to learn an effective representation for each subgoal. More specifically, we carefully designed to encourage diverse representations among different subgoals. To demonstrate that SHIL is able to learn meaningful high-level policy and low-level policy that accurately reproduces complex doctors' demonstrations, we conduct experiments on a real-world medical data from health care domain, MIMIC-III. Compared with state-of-the-art baselines, SHIL improves the likelihood of patient survival by a significant margin and provides explainable recommendation with hierarchical structure.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132675664","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: 8

Few-shot Link Prediction in Dynamic Networks 动态网络中的少射链路预测

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498417

Cheng Yang, Chuncheng Wang, Yuanfu Lu, Xumeng Gong, Chuan Shi, Wei Wang, Xu Zhang

{"title":"Few-shot Link Prediction in Dynamic Networks","authors":"Cheng Yang, Chuncheng Wang, Yuanfu Lu, Xumeng Gong, Chuan Shi, Wei Wang, Xu Zhang","doi":"10.1145/3488560.3498417","DOIUrl":"https://doi.org/10.1145/3488560.3498417","url":null,"abstract":"Dynamic link prediction, which aims at forecasting future edges of a node in a dynamic network, is an important problem in network science and has a wide range of real-world applications. A key property of dynamic networks is that new nodes and links keep coming over time and these new nodes usually have only a few links at their arrivals. However, how to predict future links for these few-shot nodes in a dynamic network has not been well studied. Existing dynamic network representation learning methods were not specialized for few-shot scenarios and thus would lead to suboptimal performances. In this paper, we propose a novel model based on a meta-learning framework, dubbed as MetaDyGNN, for few-shot link prediction in dynamic networks. Specifically, we propose a meta-learner with hierarchical time interval-wise and node-wise adaptions to extract general knowledge behind this problem. We also design a simple and effective dynamic graph neural network (GNN) module to characterize the local structure of each node in meta-learning tasks. As a result, the learned general knowledge serves as model initializations, and can quickly adapt to new nodes with a fine-tuning process on only a few links. Experimental results show that our proposed MetaDyGNN significantly outperforms state-of-the-art methods on three publicly available datasets.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131979392","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: 23

EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs EvoKG:时间知识图推理的事件时间和网络结构联合建模

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498451

Namyong Park, Fuchen Liu, Purvanshi Mehta, D. Cristofor, C. Faloutsos, Yuxiao Dong

{"title":"EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs","authors":"Namyong Park, Fuchen Liu, Purvanshi Mehta, D. Cristofor, C. Faloutsos, Yuxiao Dong","doi":"10.1145/3488560.3498451","DOIUrl":"https://doi.org/10.1145/3488560.3498451","url":null,"abstract":"How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provide intelligent services. However, despite the prevalence of real-world data that can be represented as TKGs, most methods focus on reasoning over static knowledge graphs, or cannot predict future events. In this paper, we present a problem formulation that unifies the two major problems that need to be addressed for an effective reasoning over TKGs, namely, modeling the event time and the evolving network structure. Our proposed method EvoKG jointly models both tasks in an effective framework, which captures the ever-changing structural and temporal dynamics in TKGs via recurrent event modeling, and models the interactions between entities based on the temporal neighborhood aggregation framework. Further, EvoKG achieves an accurate modeling of event time, using flexible and efficient mechanisms based on neural density estimation. Experiments show that EvoKG outperforms existing methods in terms of effectiveness (up to 77% and 116% more accurate time and link prediction) and efficiency.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"12 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133621742","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: 30

RLMob RLMob

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498438

Ziyan Luo, Congcong Miao

{"title":"RLMob","authors":"Ziyan Luo, Congcong Miao","doi":"10.1145/3488560.3498438","DOIUrl":"https://doi.org/10.1145/3488560.3498438","url":null,"abstract":"Human mobility prediction is an important task in the field of spatiotemporal sequential data mining and urban computing. Despite the extensive work on mining human mobility behavior, little attention was paid to the problem of successive mobility prediction. The state-of-the-art methods of human mobility prediction are mainly based on supervised learning. To achieve higher predictability and adapt well to the successive mobility prediction, there are four key challenges: 1) disability to the circumstance that the optimizing target is discrete-continuous hybrid and non-differentiable. In our work, we assume that the user's demands are always multi-targeted and can be modeled as a discrete-continuous hybrid function; 2) difficulty to alter the recommendation strategy flexibly according to the changes in user needs in real scenarios; 3) error propagation and exposure bias issues when predicting multiple points in successive mobility prediction; 4) cannot interactively explore user's potential interest that does not appear in the history. While previous methods met these difficulties, reinforcement learning (RL) is an intuitive answer for this task to settle these issues. We innovatively introduce RL to the successive prediction task. In this paper, we formulate this problem as a Markov Decision Process. We further propose a framework - RLMob to solve our problem. A simulated environment is carefully designed. An actor-critic framework with an instance of Proximal Policy Optimization (PPO) is applied to adapt to our scene with a large state space. Experiments show that on the task, the performance of our approach is consistently superior to that of the compared approaches.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234833","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

Uncovering Causal Effects of Online Short Videos on Consumer Behaviors 揭示网络短视频对消费者行为的因果效应

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498513

Ziqi Tan, Shengyu Zhang, Nuanxin Hong, Kun Kuang, Yifan Yu, Jin Yu, Zhou Zhao, Hongxia Yang, Shiyuan Pan, Jingren Zhou, Fei Wu

{"title":"Uncovering Causal Effects of Online Short Videos on Consumer Behaviors","authors":"Ziqi Tan, Shengyu Zhang, Nuanxin Hong, Kun Kuang, Yifan Yu, Jin Yu, Zhou Zhao, Hongxia Yang, Shiyuan Pan, Jingren Zhou, Fei Wu","doi":"10.1145/3488560.3498513","DOIUrl":"https://doi.org/10.1145/3488560.3498513","url":null,"abstract":"In recent years, online short videos have become more popular, especially as an online advertising intermediary. To better understand their effects as advertisements, it is essential to analyze the causal relations of online short videos on consumer behaviors. Our study is based on fine-grained consumer behavior data from a world-leading e-commerce platform, i.e., Taobao.com. We first decompose the total causal effects into informative effects and persuasive effects following a common practice in the economic literature. Moreover, we extract the subjectivity scores of short videos through a dictionary-based subjectivity analysis model and evaluate the correlation between the subjectivity scores and each causal effect. The findings of this paper are as follows: First, both causal effects (i.e., informative and persuasive effects) are significant. Second, these effects have a strong correlation with the short videos' subjectivity scores. Third, the signs of these correlations vary with the prices of the products. Our results not only shed light on the research of how short videos exert influence on online consumers, but also give sellers advice on better video design and recommendation.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123749800","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

An Interactive Knowledge Graph Based Platform for COVID-19 Clinical Research 基于交互式知识图谱的新型冠状病毒临床研究平台

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3502193

Juntao Su, E. Dougherty, Shuang Jiang, Fang Jin

{"title":"An Interactive Knowledge Graph Based Platform for COVID-19 Clinical Research","authors":"Juntao Su, E. Dougherty, Shuang Jiang, Fang Jin","doi":"10.1145/3488560.3502193","DOIUrl":"https://doi.org/10.1145/3488560.3502193","url":null,"abstract":"Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals and therapeutics have been tested for COVID-19 treatment. While medical advancements and breakthroughs are well underway, the sheer number of studies, treatments, and associated reports makes it extremely challenging to keep track of the rapidly growing COVID-19 research landscape. While existing scientific literature search systems provide basic document retrieval, they fundamentally lack the ability to explore data, and in addition, do not help develop a deeper understanding of COVID-19 related clinical experiments and findings. As research expands, results do so as well, resulting in a position that is complicated and overwhelming. To address this issue, we present a named entity recognition based framework that accurately extracts COVID-19 related information from clinical test results articles, and generates an efficient and interactive visual knowledge graph. This knowledge graph platform is user friendly, and provides intuitive and convenient tools to explore and analyze COVID-19 research data and results including medicinal performances, side effects and target populations.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125080319","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

Improving Knowledge Tracing with Collaborative Information 利用协同信息改进知识追踪

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498374

Ting Long, Jiarui Qin, Jian Shen, Weinan Zhang, Wei Xia, Ruiming Tang, Xiuqiang He, Yong Yu

{"title":"Improving Knowledge Tracing with Collaborative Information","authors":"Ting Long, Jiarui Qin, Jian Shen, Weinan Zhang, Wei Xia, Ruiming Tang, Xiuqiang He, Yong Yu","doi":"10.1145/3488560.3498374","DOIUrl":"https://doi.org/10.1145/3488560.3498374","url":null,"abstract":"Knowledge tracing, which estimates students' knowledge states by predicting the probability that they correctly answer questions, is an essential task for online learning platforms. It has gained much attention in the decades due to its importance to downstream tasks like learning material arrangement, etc. The previous deep learning-based methods trace students' knowledge states with the explicitly intra-student information, i.e., they only consider the historical information of individuals to make predictions. However, they neglect the inter-student information, which contains the response correctness of other students who have similar question-answering experiences, may offer some valuable clues. Based on this consideration, we propose a method called Collaborative Knowledge Tracing (CoKT) in this paper, which sufficiently exploits the inter-student information in knowledge tracing. It retrieves the sequences of peer students who have similar question-answering experiences to obtain the inter-student information, and integrates the inter-student information with the intra-student information to trace students' knowledge states and predict their correctness in answering questions. We validate the effectiveness of our method on four real-world datasets and compare it with 11 baselines. The experimental results reveal that CoKT achieves the best performance.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114918853","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: 15

Fair Graph Representation Learning with Imbalanced and Biased Data 不平衡和有偏差数据下的公平图表示学习

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3502218

Yu Wang

{"title":"Fair Graph Representation Learning with Imbalanced and Biased Data","authors":"Yu Wang","doi":"10.1145/3488560.3502218","DOIUrl":"https://doi.org/10.1145/3488560.3502218","url":null,"abstract":"Graph-structured data is omnipresent in various fields, such as biology, chemistry, social media and transportation. Learning informative graph representations are crucial in effectively completing downstream graph-related tasks such as node/graph classification and link prediction. Graph Neural Networks (GNNs), due to their inclusiveness on handling graph-structured data and distinguished data-mining power inherited from deep learning, have achieved significant success in learning graph representations. Nonetheless, most existing GNNs are mainly designed with unrealistic data assumptions, such as the balanced and unbiased data distributions while abounding real-world networks exhibit skewed (i.e., long-tailed) node/graph class distributions and may also encode patterns of previous discriminatory decisions dominated by sensitive attributes. Even further, extensive research efforts have been invested in developing GNN architectures towards improving model utility while most of the time totally ignoring whether the obtained node/graph representations conceal any discriminatory bias, which could lead to prejudicial decisions as GNN-based machine learning models are increasingly being utilized in real-world applications. In light of the prevalence of the above two types of unfairness originated from quantity-imbalanced and discriminatory bias, my research expects to propose novel node/graph representation learning frameworks through constructing innovative GNN architectures and devising novel graph-mining algorithms to learn both fair and expressive node/graph representations that can enjoy a favorable fairness-utility tradeoff.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130864666","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: 7