OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Low-level Storage of Very Large Knowledge Graphs
Authors: Jacopo Urbani, Ceriel J. H. Jacobs
Venue: WWW
Year: 2020

The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 1011 edges using inexpensive hardware, delivering competitive performance on multiple workloads.
Long-Lived Agents on the Web: Continuous Acquisition of Behaviors in Hypermedia Environments
Authors: Danai Vachtsevanou, Philip Junker, Andrei Ciortea, Iori Mizutani, Simon Mayer
Venue: WWW
Year: 2020

We demonstrate how autonomous goal-directed agents can exploit hypermedia to acquire and execute new behaviors at run time. In addition to behaviors programmed into the agents, in our system agents can discover and reuse behaviors extracted from machine-readable resource manuals. Such manuals can be published by developers, synthesized by agents through automated planning, or even specified by human users at run time. Agents can then discover and use physical and virtual resources in flexible ways, which allows them to better cope with the rapid evolution of open and dynamic Web environments.
Probabilistic Logic Graph Attention Networks for Reasoning
Authors: L. Vivek Harsha Vardhan, Guo Jia, Stanley Kok
Venue: WWW
Year: 2020

Knowledge base completion, which involves the prediction of missing relations between entities in a knowledge graph, has been an active area of research. Markov logic networks, which combine probabilistic graphical models and first order logic, have proven to be effective on knowledge graph tasks like link prediction and question answering. However, their intractable inference limits their scalability and wider applicability across various tasks. In recent times, graph attention neural networks, which capture features of neighbouring entities, have achieved superior results on highly complex graph problems like node classification and link prediction. Combining the best of both worlds, we propose Probabilistic Logic Graph Attention Network (pGAT) for reasoning. In the proposed model, the joint distribution of all possible triplets defined by a Markov logic network is optimized with a variational EM algorithm. This helps us to efficiently combine first-order logic and graph attention networks. With the goal of establishing strong baselines for future research on link prediction, we evaluate our model on various standard link prediction benchmarks, and obtain competitive results.
Open Intent Extraction from Natural Language Interactions
Authors: Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy
Venue: WWW
Year: 2020

Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance, grouping user utterances into a single intent type from a set of categories known beforehand. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task under an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply a multi-head self-attention mechanism to effectively learn dependencies between distant words. We further use adversarial training to improve performance and robustly adapt our model across varying domains. Finally, we curate and plan to release an open intent annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that our approach outperforms state-of-the-art baselines by 5-15% F1 score points. We also demonstrate the efficacy of OPINE in recognizing multiple, diverse domain intents with limited (can also be zero) training examples per unique domain.
Modeling Causal Impact of Textual Style on a Targeted Goal
Authors: Gaurav Verma, Balaji Vasan Srinivasan, Shiv Kumar Saini, Niyati Chhaya
Venue: WWW
Year: 2020

The consumption characteristics of a textual piece are influenced by both the core-content (i.e., what is being conveyed) and its stylistic attributes (i.e., how it is being conveyed). We present an approach to model stylistic attributes in text and leverage a multi-cause deconfounder model to estimate the causal effect of stylistic attributes towards a targeted goal. We show that our approach can identify causally significant attributes along with the ones considered important by conventional supervised approaches. Furthermore, we demonstrate using performance comparison on classification tasks that our approach does not compromise on the modeling capabilities. We believe that such a model can be valuable towards providing statistical feedback to an author to improve on certain style attributes to better achieve a target objective.
Using Facebook Data to Measure Cultural Distance between Countries: The Case of Brazilian Cuisine
Authors: Carolina Coimbra Vieira, Filipe Ribeiro, Pedro Olmo Stancioli Vaz de Melo, Fabrício Benevenuto, Emilio Zagheni
Venue: WWW
Year: 2020

Measuring the affinity to a particular culture has been an active area of research. Countries and their residents can be characterized by many cultural aspects, such as clothing, music, art and food. As one of the central aspects, the cuisine of a country can reflect one of the dominant aspects of its culture. As such, the number of people interested in a typical national dish can be used to estimate the prevalence of that culture inside the host region. In this study, we measure the global spread of Brazilian culture across countries by exploring Facebook user’s preferences for typical Brazilian dishes through the Facebook Advertising Platform. To decide which dish will be considered typical from Brazil, we made use of spatial analysis to understand the distribution of interests around the world and to quantify how typical the dish is in Brazil and among Brazilian immigrants. This methodology can be generalized to other countries to infer cultural elements that emigrants usually take to and preserve in the countries they migrate to. Also, the interest in Brazilian typical dishes can be used to characterize countries in terms of Brazilian cultural exposition. While evaluating the cultural distance between Brazil and the countries with more Brazilian immigrants, we explore several measures of distance to compare these in the context of affinity to Brazilian cuisine. Our results revealed that these cultural distance measures can complement other metrics of distance applied to gravity-type models, for example, in order to explain flows of people between countries.
Methods of Corporate Surveillance: a Primer on Experimental Transparency Research
Authors: Isabel Wagner
Venue: WWW
Year: 2020

News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today. The headlines – ranging from comprehensive profiling of users, to microtargeting of political messages, to discrimination based on gender, race, and age – show that big tech’s operations can cause real-world harm. However, big tech companies are reluctant to disclose how they operate and typically do not give out specific information, such as what data they collect or how their algorithms make decisions. This secretive operation counteracts ideals of transparency, openness, and accountability. This tutorial will present research methods and findings from the last 5-10 years that use large-scale experiments to systematically interact with the publicly accessible elements of big tech’s platforms. These experiments allow inferring details about big tech’s hidden operations – in essence conducting meta-surveillance against big tech. For example, findings from these experiments have documented how user tracking works, the extent of tracking on the web today, and how the collected information is used for algorithmic decision-making and ad targeting.
Traffic Flow Prediction via Spatial Temporal Graph Neural Network
Authors: Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu
Venue: WWW
Year: 2020

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.
Towards Cost Effective Server Population Estimation: A Case Study of Twitch
Authors: Caleb Wang, Hsi Chen, Polly Huang
Venue: WWW
Year: 2020

Internet services such as email and video streaming are daily necessity for many of us. To scale to the demand of service quality and continuity, major services today operate on server clusters. Growingly large, long-lasting, and dynamic, these server clusters are pretty much ecosystems themselves. Drawing from the observation, we see a window of opportunity applying techniques in ecology, e.g., Capture-Mark-Recapture (i.e., CMR) for server population estimation, a problem fundamental to cluster monitoring.
A Cue Adaptive Decoder for Controllable Neural Response Generation
Authors: Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Venue: WWW
Year: 2020

In open-domain dialogue systems, dialogue cues such as emotion, persona, and emoji can be incorporated into conversation models for strengthening the semantic relevance of generated responses. Existing neural response generation models either incorporate dialogue cue into decoder’s initial state or embed the cue indiscriminately into the state of every generated word, which may cause the gradients of the embedded cue to vanish or disturb the semantic relevance of generated words during back propagation. In this paper, we propose a Cue Adaptive Decoder (CueAD) that aims to dynamically determine the involvement of a cue at each generation step in the decoding. For this purpose, we extend the Gated Recurrent Unit (GRU) network with an adaptive cue representation for facilitating cue incorporation, in which an adaptive gating unit is utilized to decide when to incorporate cue information so that the cue can provide useful clues for enhancing the semantic relevance of the generated words. Experimental results show that CueAD outperforms state-of-the-art baselines with large margins.
A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement
Authors: Pengyang Wang, Jiaping Gui, Zhengzhang Chen, Junghwan Rhee, Haifeng Chen, Yanjie Fu
Venue: WWW
Year: 2020

Graph Convolutional Networks (GCNs) have shown to be a powerful tool for analyzing graph-structured data. Most of previous GCN methods focus on learning a good node representation by aggregating the representations of neighboring nodes, whereas largely ignoring the edge information. Although few recent methods have been proposed to integrate edge attributes into GCNs to initialize edge embeddings, these methods do not work when edge attributes are (partially) unavailable. Can we develop a generic edge-empowered framework to exploit node-edge enhancement, regardless of the availability of edge attributes? In this paper, we propose a novel framework EE-GCN that achieves node-edge enhancement. In particular, the framework EE-GCN includes three key components: (i) Initialization: this step is to initialize the embeddings of both nodes and edges. Unlike node embedding initialization, we propose a line graph-based method to initialize the embedding of edges regardless of edge attributes. (ii) Feature space alignment: we propose a translation-based mapping method to align edge embedding with node embedding space, and the objective function is penalized by a translation loss when both spaces are not aligned. (iii) Node-edge mutually enhanced updating: node embedding is updated by aggregating embedding of neighboring nodes and associated edges, while edge embedding is updated by the embedding of associated nodes and itself. Through the above improvements, our framework provides a generic strategy for all of the spatial-based GCNs to allow edges to participate in embedding computation and exploit node-edge mutual enhancement. Finally, we present extensive experimental results to validate the improved performances of our method in terms of node classification, link prediction, and graph classification.
A Semantic Similarity-based Subgraph Matching Method for Improving Question Answering over RDF
Authors: Shujun Wang, Jie Jiao, Xiaowang Zhang
Venue: WWW
Year: 2020

RDF question/answering (Q/A) system can explore RDF data by translating natural language questions into SPARQL queries. In this poster, we design a generation-and-ranking approach to translate natural language questions into SPARQL queries based on semantic similarity between questions and SPARQL queries. In the generation stage, we employ a directed super semantic query graph to extract the structural query intention of the question, based on which Q/A on RDF is reduced to the graph matching problem. After building the query graph, we generate a set of candidate queries of the question. In the ranking stage, we rank the query in the candidate query set according to the semantic similarity between the query and question. Finally, we pick the query with the highest semantic similarity to the original question.
Edgify: Resource Allocation Optimization for Edge Clouds Using Stable Matching
Authors: Jiaqi Wang, Zac Lu, Eyhab Al-Masri
Venue: WWW
Year: 2020

As more Internet of Things (IoT) devices become increasingly ubiquitous, dynamic resource allocation for edge computing environments becomes extremely time consuming and challenging task. To overcome these challenges, edge computing environments need to dynamically scale based on the availability of accessible edge clouds within existing IoT infrastructure. In this paper, we introduce Edgify: a dynamic resource provisioning model that can efficiently allocate resources across a distributed edge computing environment. We evaluate Edgify through a number of experiments that demonstrate usefulness and effectiveness of the proposed approach.
Towards IP-based Geolocation via Fine-grained and Stable Webcam Landmarks
Authors: Zhihao Wang, Qiang Li, Jinke Song, Haining Wang, Limin Sun
Venue: WWW
Year: 2020

IP-based geolocation is essential for various location-aware Internet applications, such as online advertisement, content delivery, and online fraud prevention. Achieving accurate geolocation enormously relies on the number of high-quality (i.e., the fine-grained and stable over time) landmarks. However, the previous efforts of garnering landmarks have been impeded by the limited visible landmarks on the Internet and manual time cost. In this paper, we leverage the availability of numerous online webcams that are used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation. In particular, we present a new framework called GeoCAM, which is designed to automatically generate qualified landmarks from online webcams, providing IP-based geolocation services with high accuracy and wide coverage. GeoCAM periodically monitors websites that are hosting live webcams and uses the natural language processing technique to extract the IP addresses and latitude/longitude of webcams for generating landmarks at large-scale. We develop a prototype of GeoCAM and conduct real-world experiments for validating its efficacy. Our results show that GeoCam can detect 282,902 live webcams hosted in webpages with 94.2% precision and 90.4% recall, and then generate 16,863 stable and fine-grained landmarks, which are two orders of magnitude more than the landmarks used in prior works. Thus, by correlating a large scale of landmarks, GeoCAM is able to provide a geolocation service with high accuracy and wide coverage.
Improved Touch-screen Inputting Using Sequence-level Prediction Generation
Authors: Xin Wang, Xu Li, Jinxing Yu, Mingming Sun, Ping Li
Venue: WWW
Year: 2020

Recent years have witnessed the continuing growth of people’s dependence on touchscreen devices. As a result, input speed with the onscreen keyboard has become crucial to communication efficiency and user experience. In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME). Taken input efficiency as the optimization target, we proposed a neural end-to-end candidates generation solution to handle automatic correction, reordering, insertion, deletion as well as completion. Evaluation metrics are also discussed base on real use scenarios. For a more thorough comparison, we also provide a statistical strategy for mapping touch coordinate sequences to text input candidates. The proposed model and baselines are evaluated on a real-world dataset. The experiment (conducted on the PaddlePaddle deep learning platform1) shows that the proposed model outperforms the baselines.
An End-to-end Topic-Enhanced Self-Attention Network for Social Emotion Classification
Authors: Chang Wang, Bang Wang
Venue: WWW
Year: 2020

Social emotion classification is to predict the distribution of different emotions evoked by an article among its readers. Prior studies have shown that document semantic and topical features can help improve classification performance. However, how to effectively extract and jointly exploit such features have not been well researched. In this paper, we propose an end-to-end topic-enhanced self-attention network (TESAN) that jointly encodes document semantics and extracts document topics. In particular, TESAN first constructs a neural topic model to learn topical information and generates a topic embedding for a document. We then propose a topic-enhanced self-attention mechanism to encode semantic and topical information into a document vector. Finally, a fusion gate is used to compose the document representation for emotion classification by integrating the document vector and the topic embedding. The entire TESAN is trained in an end-to-end manner. Experimental results on three public datasets reveal that TESAN outperforms the state-of-the-art schemes in terms of higher classification accuracy and higher average Pearson correlation coefficient. Furthermore, the TESAN is computation efficient and can generate more coherent topics.
Few-Sample and Adversarial Representation Learning for Continual Stream Mining
Authors: Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, Latifur Khan
Venue: WWW
Year: 2020

Deep Neural Networks (DNNs) have primarily been demonstrated to be useful for closed-world classification problems where the number of categories is fixed. However, DNNs notoriously fail when tasked with label prediction in a non-stationary data stream scenario, which has the continuous emergence of the unknown or novel class (categories not in the training set). For example, new topics continually emerge in social media or e-commerce. To solve this challenge, a DNN should not only be able to detect the novel class effectively but also incrementally learn new concepts from limited samples over time. Literature that addresses both problems simultaneously is limited. In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories. Different from existing approaches that rely on abundant labeled instances to re-train/update the model, we propose a new approach based on Few Sample and Adversarial Representation Learning (FSAR). The key novelty is that we introduce the adversarial confusion term into both the representation learning and few-sample learning process, which reduces the over-confidence of the model on the seen classes, further enhance the generalization of the model to detect and learn new categories with only a few samples. We train the FSAR operated in two stages: first, FSAR learns an intra-class compacted and inter-class separated feature embedding to detect the novel classes; next, we collect a few labeled samples belong to the new categories, utilize episode-training to exploit the intrinsic features for few-sample learning. We evaluated FSAR on different datasets, using extensive experimental results from various simulated stream benchmarks to show that FSAR effectively outperforms current state-of-the-art approaches.
Analyzing and Detecting Adversarial Spam on a Large-scale Online APP Review System
Authors: Jianyu Wang, Rui Wen, Chunming Wu, Jian Xiong
Venue: WWW
Year: 2020

The online review system provides a platform for consumers to express their opinions and make decisions. However, the spam review has become a widespread problem recently. Existing detection systems are mostly designed for fraudsters who write fake reviews to promote their products or mislead consumers in E-commerce (e.g., Amazon, Yelp, Alibaba). In this paper, we focus on spams in the large-scale online app review platform. Through an in-depth analysis of 5 million reviews from the Tencent App store, we find almost half of the reviews are spams. Even worse, most of the reviews are deliberately designed by fraud reviewers for misleading classifiers. Specially, we conclude three popular patterns of generating adversarial spams by attackers. Considering the characteristics of spam reviews are adversarial, irregular, and distributed. Human labeling is costly. We build our detection system by utilizing a collective method to spot suspicious users and reviews. First, we trained our two models based on the Behavior-Based Module (BBM) and Content-Based Module (CBM) individually. Then, they are co-trained by getting the feedback from each other. Experiment results show that our method outperforms the state-of-the-art baselines in identifying adversarial examples. Besides, the scalability and effectiveness of our approach have also been demonstrated by deploying it on Tencent Beacon Anti-Fraud System.