Sahan Bulathwela, M. Pérez-Ortiz, Rishabh Mehrotra, D. Orlic, C. D. L. Higuera, J. Shawe-Taylor, Emine Yilmaz
Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges.
{"title":"SUM'20: State-based User Modelling","authors":"Sahan Bulathwela, M. Pérez-Ortiz, Rishabh Mehrotra, D. Orlic, C. D. L. Higuera, J. Shawe-Taylor, Emine Yilmaz","doi":"10.1145/3336191.3371883","DOIUrl":"https://doi.org/10.1145/3336191.3371883","url":null,"abstract":"Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120448","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}
Tyler Derr, Yao Ma, Wenqi Fan, Xiaorui Liu, C. Aggarwal, Jiliang Tang
A growing trend recently is to harness the structure of today's big data, where much of the data can be represented as graphs. Simultaneously, graph convolutional networks (GCNs) have been proposed and since seen rapid development. More recently, due to the scalability issues that arise when attempting to utilize these powerful models on real-world data, methodologies have sought the use of sampling techniques. More specifically, minibatches of nodes are formed and then sets of nodes are sampled to aggregate from in one or more layers. Among these methods, the two prominent ways are based on sampling nodes from either a local or global perspective. In this work, we first observe the similarities in the two sampling strategies to that of epidemic and diffusion network models. Then we harness this understanding to fuse together the benefits of sampling from both a local and global perspective while alleviating some of the inherent issues found in both through the use of a low-dimensional approximation for the path-based Katz similarity measure. Our proposed framework, Epidemic Graph Convolutional Network (EGCN), is thus able to achieve improved performance over sampling from just one of the two perspectives alone. Empirical experiments are performed on several public benchmark datasets to verify the effectiveness over existing methodologies for the node classification task and we furthermore present some empirical parameter analysis of EGCN.
{"title":"Epidemic Graph Convolutional Network","authors":"Tyler Derr, Yao Ma, Wenqi Fan, Xiaorui Liu, C. Aggarwal, Jiliang Tang","doi":"10.1145/3336191.3371807","DOIUrl":"https://doi.org/10.1145/3336191.3371807","url":null,"abstract":"A growing trend recently is to harness the structure of today's big data, where much of the data can be represented as graphs. Simultaneously, graph convolutional networks (GCNs) have been proposed and since seen rapid development. More recently, due to the scalability issues that arise when attempting to utilize these powerful models on real-world data, methodologies have sought the use of sampling techniques. More specifically, minibatches of nodes are formed and then sets of nodes are sampled to aggregate from in one or more layers. Among these methods, the two prominent ways are based on sampling nodes from either a local or global perspective. In this work, we first observe the similarities in the two sampling strategies to that of epidemic and diffusion network models. Then we harness this understanding to fuse together the benefits of sampling from both a local and global perspective while alleviating some of the inherent issues found in both through the use of a low-dimensional approximation for the path-based Katz similarity measure. Our proposed framework, Epidemic Graph Convolutional Network (EGCN), is thus able to achieve improved performance over sampling from just one of the two perspectives alone. Empirical experiments are performed on several public benchmark datasets to verify the effectiveness over existing methodologies for the node classification task and we furthermore present some empirical parameter analysis of EGCN.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122055239","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}
Alexander Bondarenko, Pavel Braslavski, Michael Völske, Rami Aly, Maik Fröbe, Alexander Panchenko, Christian Biemann, Benno Stein, Matthias Hagen
beginabstract We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple "ten blue links'' and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000~questions; 2.8%~of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches---achieving a recall of~0.6 at a perfect precision of~1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250~comparative questions using more fine-grained subclasses (e.g., should the answer be a "simple'' fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65%~of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine's knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14~categories likeconsumer electronics orhealth ), their seasonal dynamics, and possible answers from community question answering platforms. endabstract
{"title":"Comparative Web Search Questions","authors":"Alexander Bondarenko, Pavel Braslavski, Michael Völske, Rami Aly, Maik Fröbe, Alexander Panchenko, Christian Biemann, Benno Stein, Matthias Hagen","doi":"10.1145/3336191.3371848","DOIUrl":"https://doi.org/10.1145/3336191.3371848","url":null,"abstract":"beginabstract We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple \"ten blue links'' and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000~questions; 2.8%~of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches---achieving a recall of~0.6 at a perfect precision of~1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250~comparative questions using more fine-grained subclasses (e.g., should the answer be a \"simple'' fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65%~of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine's knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14~categories likeconsumer electronics orhealth ), their seasonal dynamics, and possible answers from community question answering platforms. endabstract","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531364","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}
Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
To better exploit the search logs, various click models have been proposed to extract implicit relevance feedback from user clicks. Most traditional click models are based on probability graphical models (PGMs) with manually designed dependencies. Recently, some researchers also adopt neural-based methods to improve the accuracy of click prediction. However, most of the existing click models only model user behavior in query level. As the previous iterations within the session may have an impact on the current search round, we can leverage these behavior signals to better model user behaviors. In this paper, we propose a novel neural- based Context-Aware Click Model (CACM) for Web search. CACM consists of a context-aware relevance estimator and an examination predictor. The relevance estimator utilizes session context infor- mation, i.e., the query sequence and clickthrough data, as well as the pre-trained embeddings learned from a session-flow graph to estimate the context-aware relevance of each search result. The examination predictor estimates the examination probability of each result. We further investigate several combination functions to integrate the context-aware relevance and examination probabil- ity into click prediction. Experiment results on a public Web search dataset show that CACM outperforms existing click models in both relevance estimation and click prediction tasks.
{"title":"A Context-Aware Click Model for Web Search","authors":"Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3336191.3371819","DOIUrl":"https://doi.org/10.1145/3336191.3371819","url":null,"abstract":"To better exploit the search logs, various click models have been proposed to extract implicit relevance feedback from user clicks. Most traditional click models are based on probability graphical models (PGMs) with manually designed dependencies. Recently, some researchers also adopt neural-based methods to improve the accuracy of click prediction. However, most of the existing click models only model user behavior in query level. As the previous iterations within the session may have an impact on the current search round, we can leverage these behavior signals to better model user behaviors. In this paper, we propose a novel neural- based Context-Aware Click Model (CACM) for Web search. CACM consists of a context-aware relevance estimator and an examination predictor. The relevance estimator utilizes session context infor- mation, i.e., the query sequence and clickthrough data, as well as the pre-trained embeddings learned from a session-flow graph to estimate the context-aware relevance of each search result. The examination predictor estimates the examination probability of each result. We further investigate several combination functions to integrate the context-aware relevance and examination probabil- ity into click prediction. Experiment results on a public Web search dataset show that CACM outperforms existing click models in both relevance estimation and click prediction tasks.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126335589","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}
N. Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, Hui Xiong
The next-item recommendation has attracted great research interests with both static and dynamic users' preferences considered. Existing approaches typically utilize user-item binary relations, and assume a flat preference distribution over items for each user. However, this assumption neglects the hierarchical discrimination between user intentions and user preferences, causing the methods have limited capacity to depict intention-specific preference. In fact, a consumer's purchasing behavior involves a natural sequential process, i.e., he/she first has an intention to buy one type of items, followed by choosing a specific item according to his/her preference under this intention. To this end, we propose a novel key-array memory network (KA-MemNN), which takes both user intentions and preferences into account for next-item recommendation. Specifically, the user behavioral intention tendency is determined through key addressing. Further, each array outputs an intention-specific preference representation of a user. Then, the degree of user's behavioral intention tendency and intention-specific preference representation are combined to form a hierarchical representation of a user. This representation is further utilized to replace the static profile of users in traditional matrix factorization for the purposes of reasoning. The experimental results on real-world data demonstrate the advantages of our approach over state-of-the-art methods.
{"title":"Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation","authors":"N. Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, Hui Xiong","doi":"10.1145/3336191.3371840","DOIUrl":"https://doi.org/10.1145/3336191.3371840","url":null,"abstract":"The next-item recommendation has attracted great research interests with both static and dynamic users' preferences considered. Existing approaches typically utilize user-item binary relations, and assume a flat preference distribution over items for each user. However, this assumption neglects the hierarchical discrimination between user intentions and user preferences, causing the methods have limited capacity to depict intention-specific preference. In fact, a consumer's purchasing behavior involves a natural sequential process, i.e., he/she first has an intention to buy one type of items, followed by choosing a specific item according to his/her preference under this intention. To this end, we propose a novel key-array memory network (KA-MemNN), which takes both user intentions and preferences into account for next-item recommendation. Specifically, the user behavioral intention tendency is determined through key addressing. Further, each array outputs an intention-specific preference representation of a user. Then, the degree of user's behavioral intention tendency and intention-specific preference representation are combined to form a hierarchical representation of a user. This representation is further utilized to replace the static profile of users in traditional matrix factorization for the purposes of reasoning. The experimental results on real-world data demonstrate the advantages of our approach over state-of-the-art methods.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132961017","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}
One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Trust can be improved once we understand how and why deep models carve out a particular form of decision boundary and thus make particular decisions. Robustness against adversarial examples is directly related to the decision boundary as adversarial examples are basically 'missed out' by the decision boundary between two classes. Investigating the decision boundary of deep neural networks, nevertheless, faces tremendous challenges. First, how we can generate instances near the decision boundary that are similar to real samples? Second, how we can leverage near decision boundary instances to characterize the behaviour of deep neural networks? Motivated to solve these challenges, we focus on investigating the decision boundary of deep neural network classifiers. In particular, we propose a novel approach to generate instances near decision boundary of pre-trained DNNs and then leverage these instances to characterize the behaviour of deep models.
{"title":"Decision Boundary of Deep Neural Networks: Challenges and Opportunities","authors":"Hamid Karimi, Jiliang Tang","doi":"10.1145/3336191.3372186","DOIUrl":"https://doi.org/10.1145/3336191.3372186","url":null,"abstract":"One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Trust can be improved once we understand how and why deep models carve out a particular form of decision boundary and thus make particular decisions. Robustness against adversarial examples is directly related to the decision boundary as adversarial examples are basically 'missed out' by the decision boundary between two classes. Investigating the decision boundary of deep neural networks, nevertheless, faces tremendous challenges. First, how we can generate instances near the decision boundary that are similar to real samples? Second, how we can leverage near decision boundary instances to characterize the behaviour of deep neural networks? Motivated to solve these challenges, we focus on investigating the decision boundary of deep neural network classifiers. In particular, we propose a novel approach to generate instances near decision boundary of pre-trained DNNs and then leverage these instances to characterize the behaviour of deep models.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131022440","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}
As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively/efficiently extract insightful patterns. Then, once paired with domain knowledge, we can seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Furthermore, many real-world networks can be better represented as signed networks, e.g., in an online social network such as Facebook, friendships can be represented as positive links while negative links can represent blocked users. Hence, due to signed networks being ubiquitous, in this work we seek to provide a fundamental background into the domain, a hierarchical categorization of existing work highlighting both seminal and state of the art, provide a curated collection of signed network datasets, and discuss important future directions.
{"title":"Network Analysis with Negative Links","authors":"Tyler Derr","doi":"10.1145/3336191.3372188","DOIUrl":"https://doi.org/10.1145/3336191.3372188","url":null,"abstract":"As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively/efficiently extract insightful patterns. Then, once paired with domain knowledge, we can seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Furthermore, many real-world networks can be better represented as signed networks, e.g., in an online social network such as Facebook, friendships can be represented as positive links while negative links can represent blocked users. Hence, due to signed networks being ubiquitous, in this work we seek to provide a fundamental background into the domain, a hierarchical categorization of existing work highlighting both seminal and state of the art, provide a curated collection of signed network datasets, and discuss important future directions.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134462393","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}
Sreyasi Nag Chowdhury, William Cheng, Gerard de Melo, Simon Razniewski, G. Weikum
Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.
{"title":"Illustrate Your Story: Enriching Text with Images","authors":"Sreyasi Nag Chowdhury, William Cheng, Gerard de Melo, Simon Razniewski, G. Weikum","doi":"10.1145/3336191.3371866","DOIUrl":"https://doi.org/10.1145/3336191.3371866","url":null,"abstract":"Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121764206","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}
Knowledge Graph Question Answering aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge graphs. When faced with a multi-relation question, existing embedding-based approaches take the whole topic-entity-centric subgraph into account, resulting in high time complexity. Meanwhile, due to the high cost for data annotations, it is impractical to exactly show how to answer a complex question step by step, and only the final answer is labeled, as weak supervision. To address these challenges, this paper proposes a neural method based on reinforcement learning, namely Stepwise Reasoning Network, which formulates multi-relation question answering as a sequential decision problem. The proposed model performs effective path search over the knowledge graph to obtain the answer, and leverages beam search to reduce the number of candidates significantly. Meanwhile, based on the attention mechanism and neural networks, the policy network can enhance the unique impact of different parts of a given question over triple selection. Moreover, to alleviate the delayed and sparse reward problem caused by weak supervision, we propose a potential-based reward shaping strategy, which can accelerate the convergence of the training algorithm and help the model perform better. Extensive experiments conducted over three benchmark datasets well demonstrate the effectiveness of the proposed model, which outperforms the state-of-the-art approaches.
{"title":"Stepwise Reasoning for Multi-Relation Question Answering over Knowledge Graph with Weak Supervision","authors":"Yunqi Qiu, Yuanzhuo Wang, Xiaolong Jin, Kun Zhang","doi":"10.1145/3336191.3371812","DOIUrl":"https://doi.org/10.1145/3336191.3371812","url":null,"abstract":"Knowledge Graph Question Answering aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge graphs. When faced with a multi-relation question, existing embedding-based approaches take the whole topic-entity-centric subgraph into account, resulting in high time complexity. Meanwhile, due to the high cost for data annotations, it is impractical to exactly show how to answer a complex question step by step, and only the final answer is labeled, as weak supervision. To address these challenges, this paper proposes a neural method based on reinforcement learning, namely Stepwise Reasoning Network, which formulates multi-relation question answering as a sequential decision problem. The proposed model performs effective path search over the knowledge graph to obtain the answer, and leverages beam search to reduce the number of candidates significantly. Meanwhile, based on the attention mechanism and neural networks, the policy network can enhance the unique impact of different parts of a given question over triple selection. Moreover, to alleviate the delayed and sparse reward problem caused by weak supervision, we propose a potential-based reward shaping strategy, which can accelerate the convergence of the training algorithm and help the model perform better. Extensive experiments conducted over three benchmark datasets well demonstrate the effectiveness of the proposed model, which outperforms the state-of-the-art approaches.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155423","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}
Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, E. Papalexakis
Recent studies have demonstrated that machine learning approaches like deep learning methods are easily fooled by adversarial attacks. Recently, a highly-influential study examined the impact of adversarial attacks on graph data and demonstrated that graph embedding techniques are also vulnerable to adversarial attacks. Fake users on social media and fake product reviews are examples of perturbations in graph data that are realistic counterparts of the adversarial models proposed. Graphs are widely used in a variety of domains and it is highly important to develop graph analysis techniques that are robust to adversarial attacks. One of the recent studies on generating adversarial attacks for graph data is Nettack. The Nettack model has shown to be very successful in deceiving the Graph Convolutional Network (GCN) model. Nettack is also transferable to other node classification approaches e.g. node embeddings. In this paper, we explore the properties of Nettack perturbations, in search for effective defenses against them. Our first finding is that Nettack demonstrates a very specific behavior in the spectrum of the graph: only high-rank (low-valued) singular components of the graph are affected. Following that insight, we show that a low-rank approximation of the graph, that uses only the top singular components for its reconstruction, can greatly reduce the effects of Nettack and boost the performance of GCN when facing adversarial attacks. Indicatively, on the CiteSeer dataset, our proposed defense mechanism is able to reduce the success rate of Nettack from 98% to 36%. Furthermore, we show that tensor-based node embeddings, which by default project the graph into a low-rank subspace, are robust against Nettack perturbations. Lastly, we propose LowBlow, a low-rank adversarial attack which is able to affect the classification performance of both GCN and tensor-based node embeddings and we show that the low-rank attack is noticeable and making it unnoticeable results in a high-rank attack.
{"title":"All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs","authors":"Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, E. Papalexakis","doi":"10.1145/3336191.3371789","DOIUrl":"https://doi.org/10.1145/3336191.3371789","url":null,"abstract":"Recent studies have demonstrated that machine learning approaches like deep learning methods are easily fooled by adversarial attacks. Recently, a highly-influential study examined the impact of adversarial attacks on graph data and demonstrated that graph embedding techniques are also vulnerable to adversarial attacks. Fake users on social media and fake product reviews are examples of perturbations in graph data that are realistic counterparts of the adversarial models proposed. Graphs are widely used in a variety of domains and it is highly important to develop graph analysis techniques that are robust to adversarial attacks. One of the recent studies on generating adversarial attacks for graph data is Nettack. The Nettack model has shown to be very successful in deceiving the Graph Convolutional Network (GCN) model. Nettack is also transferable to other node classification approaches e.g. node embeddings. In this paper, we explore the properties of Nettack perturbations, in search for effective defenses against them. Our first finding is that Nettack demonstrates a very specific behavior in the spectrum of the graph: only high-rank (low-valued) singular components of the graph are affected. Following that insight, we show that a low-rank approximation of the graph, that uses only the top singular components for its reconstruction, can greatly reduce the effects of Nettack and boost the performance of GCN when facing adversarial attacks. Indicatively, on the CiteSeer dataset, our proposed defense mechanism is able to reduce the success rate of Nettack from 98% to 36%. Furthermore, we show that tensor-based node embeddings, which by default project the graph into a low-rank subspace, are robust against Nettack perturbations. Lastly, we propose LowBlow, a low-rank adversarial attack which is able to affect the classification performance of both GCN and tensor-based node embeddings and we show that the low-rank attack is noticeable and making it unnoticeable results in a high-rank attack.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121240003","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：