OpenRaiser/Intern-Atlas huggingface dataset:
Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
Authors: Yang Deng, Wenxuan Zhang, Wai Lam
Venue: CIKM
Year: 2020

Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.
UPON: User Profile Transferring across Networks
Authors: Mengting Diao, Zhongbao Zhang, Sen Su, Shuai Gao, Huafeng Cao
Venue: CIKM
Year: 2020

User profiling has very important applications for many downstream tasks, such as recommender system, behavior prediction and market strategy. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Modeling user profiles under such conditions often leads to poor performance. Fortunately, we observed that not only user attributes but also user relationships are useful for user profiling and benefit the results. Meanwhile, similar users have similar behavior in different social networks. Finding user dependencies between social networks will help to infer user profiles. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design an efficient User Profile transferring acrOss Networks (UPON) framework, which transfers knowledge of user relationship from one social network with plenty of data to facilitate the user profiling on the other social network with scarce data. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model (GCN-CDAM) to find user dependencies within and between domains (referring to social networks). We then design a dual-domain weighted adversarial learning method to solve the domain shift problem existing in the transferring procedure. Experimental results on Twitter-Foursquare dataset demonstrate that UPON outperforms the state-of-the-art models.
TweetsCOV19 - A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
Authors: Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, Stefan Dietze
Venue: CIKM
Year: 2020

Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods. With respect to the recent outbreak of the Coronavirus disease 2019 (COVID-19), online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution, and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection, or entity recognition. However, obtaining, archiving, and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning October 2019 - April 2020. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis and use cases of the corpus.
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
Authors: Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi, Miao Pan
Venue: CIKM
Year: 2020

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Authors: Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu
Venue: CIKM
Year: 2020

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes only contains limited labeled instances, rendering a long-tail node class distribution. Existing node classification algorithms are unequipped to handle the few-shot node classes. As a remedy, few-shot learning has attracted a surge of attention in the research community. Yet, few-shot node classification remains a challenging problem as we need to address the following questions: (i) How to extract meta-knowledge from an attributed network for few-shot node classification? (ii) How to identify the informativeness of each labeled instance for building a robust and effective model? To answer these questions, in this paper, we propose a graph meta-learning framework -- Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform meta-learning on an attributed network and derive a highly generalizable model for handling the target classification task. Extensive experiments demonstrate the superior capability of GPN in few-shot node classification.
Ceres: Harvesting Knowledge from the Semi-structured Web
Authors: Xin Luna Dong
Venue: CIKM
Year: 2020

Knowledge graphs have been used to support a wide range of applications and enhance search and QA for Google, Bing, Amazon Alexa, etc. However, we often miss long-tail knowledge, including unpopular entities, unpopular relations, and unpopular verticals. In this talk we describe our efforts in harvesting knowledge from semi-structured websites, which are often populated according to some templates using vast volume of data stored in underlying databases. We describe our Ceres system, which extracts knowledge from semi-structured web. AutoCeres is a ClosedIE system that extracts knowledge according to existing ontology. It improves the accuracy of fully automatic knowledge extraction from 60%+ of state-of-the-art to 90%+ on semi-structured data. OpenCeres is the first-ever OpenIE system on semi-structured data, that is able to identify new relations not readily included in existing ontologies. ZeroShotCeres goes further and enables extracting knowledge for completely new domains, where there is no seed knowledge for bootstrapping the extraction. Finally, we describe our other efforts in ontology alignment, entity linkage, graph mining, and QA, that allow us to best leverage the knowledge we extract for search and QA.
Learning Formatting Style Transfer and Structure Extraction for Spreadsheet Tables with a Hybrid Neural Network Architecture
Authors: Haoyu Dong, Jiong Yang, Shi Han, Dongmei Zhang
Venue: CIKM
Year: 2020

Table formatting is a typical task for spreadsheet users to better exhibit table structures and data relationships. But quickly and effectively formatting tables is a challenge for users. Lots of manual operations are needed, especially for complex tables. In this paper, we propose techniques for table formatting style transfer, i.e., to automatically format a target table according to the style of a reference table. Considering the latent many-to-many mappings between table structures and formats, we propose CellNet, which is a novel end-to-end, multi-task model leveraging conditional Generative Adversarial Networks (cGANs) with three key components to (1) model and recognize table structures; (2) encode formatting styles; (3) learn and apply the latent mapping based on recognized table structure and encoded style, respectively. Moreover, we build up a spreadsheet table corpus containing 5,226 tables with high-quality formats and 784 tables with human-labeled structures. Our evaluation shows that CellNet is highly effective according to both quantitative metrics and human perception studies by comparing with heuristic-based and other learning-based methods.
The Utility of Context When Extracting Entities From Legal Documents
Authors: Jonathan Donnelly, Adam Roegiest
Venue: CIKM
Year: 2020

When reviewing documents for legal tasks such as Mergers and Acquisitions, granular information (such as start dates and exit clauses) need to be identified and extracted. Inspired by previous work in Named Entity Recognition (NER), we investigate how NER techniques can be leveraged to aid lawyers in this review process. Due to the extremely low prevalence of target information in legal documents, we find that the traditional approach of tagging all sentences in a document is inferior, in both effectiveness and data required to train and predict, to using a first-pass layer to identify sentences that are likely to contain the relevant information and then running the more traditional sentence-level sequence tagging. Moreover, we find that such entity-level models can be improved by training on a balanced sample of relevant and non-relevant sentences. We additionally describe the use of our system in production and how its usage by clients means that deep learning architectures tend to be cost inefficient, especially with respect to the necessary time to train models.
Learning Discriminative Virtual Sequences for Time Series Classification
Authors: Abhilash Dorle, Fangyu Li, Wenzhan Song, Sheng Li
Venue: CIKM
Year: 2020

Temporal data are continuously collected in a wide range of domains. The increasing availability of such data has led to significant developments of time series analysis. Time series classification, as an essential task in time series analysis, aims to assign a set of temporal sequences to different categories. Among various approaches for time series classification, the distance metric learning based ones, such as the virtual sequence metric learning (VSML), have attracted increased attention due to their remarkable performance. In VSML, virtual sequences attract samples from different classes to facilitate time series classification. However, the existing VSML methods simply employ fixed virtual sequences, which might not be optimal for the subsequent classification tasks. To address this issue, in this paper, we propose a novel time series classification method named Discriminative Virtual Sequence Learning (DVSL). Following the unified framework of sequence metric learning, our DVSL method jointly learns a set of discriminative virtual sequences that help separate time series samples in a feature space, and optimizes the temporal alignment by dynamic time warping. Extensive experiments on 15 UCR time series datasets demonstrate the efficiency of DVSL, compared with several representative baselines.
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Authors: Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu
Venue: CIKM
Year: 2020

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations. However, few prior works have noticed the camouflage behavior of fraudsters, which could hamper the performance of GNN-based fraud detectors during the aggregation process. In this paper, we introduce two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage. Existing GNNs have not addressed these two camouflages, which results in their poor performance in fraud detection problems. Alternatively, we propose a new model named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation process with three unique modules against camouflages. Concretely, we first devise a label-aware similarity measure to find informative neighboring nodes. Then, we leverage reinforcement learning (RL) to find the optimal amounts of neighbors to be selected. Finally, the selected neighbors across different relations are aggregated together. Comprehensive experiments on two real-world fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors. We integrate all GNN-based fraud detectors as an opensource toolbox https://github.com/safe-graph/DGFraud. The CARE-GNN code and datasets are available at https://github.com/YingtongDou/CARE-GNN.
Accelerating Discovery Science with an Internet of FAIR Data and Services
Authors: Michel Dumontier
Venue: CIKM
Year: 2020

Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies offer exciting opportunities to mine our collective knowledge, were we not stymied by incompatible formats, incomplete and overlapping vocabularies, confusing licensing policies, and heterogeneous data access points. In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these representations for biomedical knowledge discovery. However, only with crucial additional developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
Learning to Rank in the Position Based Model with Bandit Feedback
Authors: Beyza Ermis, Patrick Ernst, Yannik Stein, Giovanni Zappella
Venue: CIKM
Year: 2020

Personalization is a crucial aspect of many online experiences. In particular, content ranking is often a key component in delivering sophisticated personalization results. Commonly, supervised learning-to-rank methods are applied, which suffer from bias introduced during data collection by production systems in charge of producing the ranking. To compensate for this problem, we leverage contextual multi-armed bandits. We propose novel extensions of two well-known algorithms viz. LinUCB and Linear Thompson Sampling to the ranking use-case. To account for the biases in a production environment, we employ the position-based click model. Finally, we show the validity of the proposed algorithms by conducting extensive offline experiments on synthetic datasets as well as customer facing online A/B experiments.
Why is That a Background Article: A Qualitative Analysis of Relevance for News Background Linking
Authors: Marwa Essam, Tamer Elsayed
Venue: CIKM
Year: 2020

News background linking is the problem of finding online resources that can provide valuable context and background information to help the reader comprehend a given news article. While the problem has recently attracted several researchers, however, the notion of background relevance is not well-studied and it requires better understanding to ensure effective system performance. In this paper, we conduct a qualitative analysis on a sample of 25 query news articles and 152 of their corresponding background articles, in addition to 50 of non-background ones. The goal of the study is to shed some light on the relationship between the query articles and the background articles, and provide informative insights for developing more effective background retrieval models. For instance, our analysis shows that event-driven query articles are, on average, harder to process than others, hence they should be handled differently. It also shows that discussing subtopics in detail and adding new informative topics are both essential factors for highly-relevant background articles. Moreover, it shows that a high lexical similarity between a query article and a background one is neither sufficient nor necessary.
Fusing Global Domain Information and Local Semantic Information to Classify Financial Documents
Authors: Mengzhen Fan, Dawei Cheng, Fangzhou Yang, Siqiang Luo, Yifeng Luo, Weining Qian, Aoying Zhou
Venue: CIKM
Year: 2020

Many institutions are devoted to providing investment advising services to stock investors to help them make sound investment decisions. Industry analysts at these institutions need to analyze huge amounts of financial news documents, and yield investment advising reports to the service subscribers. Automatic document classification is required to organize collected financial news documents into pre-defined fine-grained categories, before the document analysis tasks. It is challenging to implement accurate fine-grained classification over massive financial documents, because documents from close fine-grained categories are highly semantically similar, while existing classification methods may fail to differentiate the subtle differences for documents from close fine-grained categories. In this paper, we implement a document classification framework, named GraphSEAT, to classify financial documents for a leading financial information service provider in China. Specifically, we build a heterogeneous graph to model the global structure of our targeting financial documents, where documents and financial named entities are deemed as nodes, and a document is connected to a contained named entity with an edge, and we then train a graph convolutional network (GCN) with attention mechanisms, to learn an embedding representation containing domain information for a document. We also extract semantic information from a document's word sequence with a neural sequence encoder, and finally form an overall embedding representation for a document and make the prediction, via fusing the two learned representations of the document with attention mechanisms. We perform extensive experiments on our real-world financial news dataset and three public datasets, to evaluate the performance of the document classification framework, and the experimental results demonstrate that GraphSEAT outperforms all compared eight baseline models, especially on our dataset.
Hybrid Dynamic Pruning for Efficient and Effective Query Processing
Authors: Wenxiu Fang, Trent G. Marbach, Gang Wang, Xiaoguang Liu
Venue: CIKM
Year: 2020

The performance of query processing has always been a concern in the field of information retrieval. Dynamic pruning algorithms have been proposed to improve query processing performance in terms of efficiency and effectiveness. However, a single pruning algorithm generally does not have both advantages. In this work, we investigate the performance of the main dynamic pruning algorithms in terms of average and tail latency as well as the accuracy of query results, and find that they are complementary. Inspired by these findings, we propose two types of hybrid dynamic pruning algorithms that choose different combinations of strategies according to the characteristics of each query. Experimental results demonstrate that our proposed methods yield a good balance between both efficiency and effectiveness.
PandaSQL: Parallel Randomized Triangle Enumeration with SQL Queries
Authors: Abir Farouzi, Ladjel Bellatreche, Carlos Ordonez, Gopal Pandurangan, Mimoun Malki
Venue: CIKM
Year: 2020

Triangles are an important pattern in large-scale graph analysis for their practical use in many real-life applications. However, with the expansion of networks, maintaining a balanced computational load is challenging especially for problems like triangle computations because of skewed vertices. On the other hand, there is a huge amount of data in database management systems (DBMSs) that can be modeled and analyzed as graphs. With these motivations in mind, we developed PandaSQL, a novel approach using SQL queries to enumerate all the triangles in a given graph based on Randomized Triangle Enumeration Algorithm. Our approach is elegant, abstract, and short compared to traditional languages like C++ or Python. Moreover, our partitioning queries ensures perfect load balancing. Thus, the triangle enumeration is independent, local, and parallel.