OpenRaiser/Intern-Atlas huggingface dataset:
High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking
Authors: Zheng Fang, Yanan Cao, Ren Li, Zhenyu Zhang, Yanbing Liu, Shi Wang
Venue: WWW
Year: 2020

Entity Linking (EL) is a task for mapping mentions in text to corresponding entities in knowledge base (KB). This task usually includes candidate generation (CG) and entity disambiguation (ED) stages. Recent EL systems based on neural network models have achieved good performance, but they still face two challenges: (i) Previous studies evaluate their models without considering the differences between candidate entities. In fact, the quality (gold recall in particular) of candidate sets has an effect on the EL results. So, how to promote the quality of candidates needs more attention. (ii) In order to utilize the topical coherence among the referred entities, many graph and sequence models are proposed for collective ED. However, graph-based models treat all candidate entities equally which may introduce much noise information. On the contrary, sequence models can only observe previous referred entities, ignoring the relevance between the current mention and its subsequent entities. To address the first problem, we propose a multi-strategy based CG method to generate high recall candidate sets. For the second problem, we design a Sequential Graph Attention Network (SeqGAT) which combines the advantages of graph and sequence methods. In our model, mentions are dealt with in a sequence manner. Given the current mention, SeqGAT dynamically encodes both its previous referred entities and subsequent ones, and assign different importance to these entities. In this way, it not only makes full use of the topical consistency, but also reduce noise interference. We conduct experiments on different types of datasets and compare our method with previous EL system on the open evaluation platform. The comparison results show that our model achieves significant improvements over the state-of-the-art methods.
CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks
Authors: Zhihan Fang, Guang Wang, Shuai Wang, Chaoji Zuo, Fan Zhang, Desheng Zhang
Venue: WWW
Year: 2020

Understanding representativeness in cellular web logs at city scale is essential for web applications. Most of the existing work on cellular web analyses or applications is built upon data from a single network in a city, which may not be representative of the overall usage patterns since multiple cellular networks coexist in most cities in the world. In this paper, we conduct the first comprehensive investigation of multiple cellular networks in a city with a 100% user penetration rate. We study web usage pattern (e.g., internet access services) correlation and difference between diverse cellular networks in terms of spatial and temporal dimensions to quantify the representativeness of web usage from a single network in usage patterns of all users in the same city. Moreover, relying on three external datasets, we study the correlation between the representativeness and contextual factors (e.g., Point-of-Interest, population, and mobility) to explain the potential causalities for the representativeness difference. We found that contextual diversity is a key reason for representativeness difference, and representativeness has a significant impact on the performance of real-world applications. Based on the analysis results, we further design a correction model to address the bias of single cellphone networks and improve representativeness by 45.8%.
Modeling Fine-Grained Human Mobility on Cellular Networks
Authors: Zhihan Fang, Guang Wang, Desheng Zhang
Venue: WWW
Year: 2020

Cellular network data has been proved as one of the most promising ways to understand large-scale human mobility due to its high penetration of cellphones and low collection cost. Most existing mobility models driven by cellular network data are based on either CDR (Call Detail Records) or data connection records. However, estimated mobility is biased with coarse granularities due to the insufficient data quality. Mobility modeling on cellular networks always suffer from the sparse spatial-temporal observations since user locations are recorded with cellphone activities. In this paper, to solve the issue, we design a system named FineCell to model fine-grained human mobility based on sparse cellular network data. The key challenge we address in FineCell is to achieve fine-grained mobility modeling with sparse cellular network data. In contrast to the existing works on human mobility, the novelty of the FineCell is to infer missing spatial and temporal observations caused by sensing gaps in cellular networks. More importantly, we evaluate FineCell with large-scale fine-grained ground truth data from the same cellular network. The evaluation results show FineCell achieve 9.8% lower error compared with state-of-the-art models.
Misinformation Battle Revisited: Counter Strategies from Clinics to Artificial Intelligence
Authors: Amir Ebrahimi Fard, Shajeeshan Lingeswaran
Venue: WWW
Year: 2020

The spread of misinformation is one of the severe challenges that societies have been dealing with for many years. However, the rapid growth of social media has accelerated the creation and circulation of such information and turned it into a potential threat to the main societal institutions such as peace and democracy. Although many of iconic figures, policymakers, business leaders and researchers have warned us of serious repercussions of misinformation, a clear course of action is not yet visible. To tackle such an issue, the preliminary step would be the evaluation of the as-is situation, which allows us to identify the deficiencies of existing solutions. This issue has been addressed in this study by a comprehensive analysis over decades of societal efforts against misinformation. In this analysis, quelling strategies from organisational and government perspectives are explained. Then they are investigated from efficacy level and governance mode. Our analyses show that, despite a seemingly suitable setting for confronting misinformation, there is a major shortcoming in governance mode of current quelling strategies.
Dark Matter: Uncovering the DarkComet RAT Ecosystem
Authors: Brown Farinholt, Mohammad Rezaeirad, Damon McCoy, Kirill Levchenko
Venue: WWW
Year: 2020

Remote Access Trojans (RATs) are a persistent class of malware that give an attacker direct, interactive access to a victim’s personal computer, allowing the attacker to steal private data, spy on the victim in real-time using the camera and microphone, and verbally harass the victim through the speaker. To date, the users and victims of this pernicious form of malware have been challenging to observe in the wild due to the unobtrusive nature of infections. In this work, we report the results of a longitudinal study of the DarkComet RAT ecosystem. Using a known method for collecting victim log databases from DarkComet controllers, we present novel techniques for tracking RAT controllers across hostname changes and improve on established techniques for filtering spurious victim records caused by scanners and sandboxed malware executions. We downloaded 6,620 DarkComet databases from 1,029 unique controllers spanning over 5 years of operation. Our analysis shows that there have been at least 57,805 victims of DarkComet over this period, with 69 new victims infected every day; many of whose keystrokes have been captured, actions recorded, and webcams monitored during this time. Our methodologies for more precisely identifying campaigns and victims could potentially be useful for improving the efficiency and efficacy of victim cleanup efforts and prioritization of law enforcement investigations.
Voice-based Reformulation of Community Answers
Authors: Simone Filice, Nachshon Cohen, David Carmel
Venue: WWW
Year: 2020

Community Question Answering (CQA) websites, such as Stack Exchange1 or Quora2, allow users to freely ask questions and obtain answers from other users, i.e., the community. Personal assistants, such as Amazon Alexa or Google Home, can also exploit CQA data to answer a broader range of questions and increase customers’ engagement. However, the voice-based interaction poses new challenges to the Question Answering scenario. Even assuming that we are able to retrieve a previously asked question that perfectly matches the user’s query, we cannot simply read its answer to the user. A major limitation is the answer length. Reading these answers to the user is cumbersome and boring. Furthermore, many answers contain non-voice-friendly parts, such as images, or URLs. In this paper, we define the Answer Reformulation task and propose a novel solution to automatically reformulate a community provided answer making it suitable for a voice interaction. Results on a manually annotated dataset3 extracted from Stack Exchange show that our models improve strong baselines.
Explainable AI in Industry: Practical Challenges and Lessons Learned
Authors: Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly
Venue: WWW
Year: 2020

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with the proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI have become far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability [11]. Model explainability is considered a prerequisite for building trust and adoption of AI systems in high stakes domains such as lending and healthcare [1] which require reliability, safety, and fairness. It is also critical to automated transportation, and other industrial applications with significant socio-economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling. As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale [14, 15, 25]. The challenges for the research community include: (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks. In this tutorial, we will first motivate the need for model interpretability and explainability in AI [6] from societal, legal, enterprise, end-user, and model developer perspectives, and present techniques & tools for providing explainability as part of AI/ML systems [13]. Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges & implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, hiring, lending, sales, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the WWW community.
Event-Related Query Classification with Deep Neural Networks
Authors: Sahaj Gandhi, Behrooz Mansouri, Ricardo Campos, Adam Jatowt
Venue: WWW
Year: 2020

Users tend to search over the Internet to get the most updated news when an event occurs. Search engines should then be capable of effectively retrieving relevant documents for event-related queries. As the previous studies have shown, different retrieval models are needed for different types of events. Therefore, the first step for improving effectiveness is identifying the event-related queries and determining their types. In this paper, we propose a novel model based on deep neural networks to classify event-related queries into four categories: periodic, aperiodic, one-time-only, and non-event. The proposed model combines recurrent neural networks (by feeding two LSTM layers with query frequencies) and visual recognition models (by transforming time-series data from a 1D signal to a 2D image - later passed to a CNN model) for effective query type estimation. Worth noting is that our method uses only the time-series data of query frequencies, without the need to resort to any external sources such as contextual data, which makes it language and domain-independent with regards to the query issued. For evaluation, we build upon the previous datasets on event-related queries to create a new dataset that fits the purpose of our experiments. The obtained results show that our proposed model can achieve an F1-score of 0.87.
Cross-domain Ontology Construction and Alignment from Online Product Reviews
Authors: Qian Geng, Siyu Deng, Danping Jia, Jian Jin
Venue: WWW
Year: 2020

Online customer product reviews often contain detailed sentiment attitudes towards different aspects of products and these online opinions help potential consumers to be familiar with products. The introduction of well-constructed domain ontology from online product reviews helps potential consumers to obtain relevant information about products quickly. Nonetheless, they may compare products in multiple domains for purchase decisions. On this basis, the comparison of products in different domains induces that ontology alignment becomes a fundamental task to form a cross-domain ontology. In this paper, a series of natural language processing approaches are applied to construct two domain ontologies from online product reviews automatically. Next, a new ontology alignment method is proposed for consumers to make purchase decisions regarding cross-domain product comparisons, in which a semantic-based algorithm and a structure-based algorithm are integrated to form a cross-domain ontology. Categories of experiments were conducted on reviews of smartphone and digital camera. Compared with benchmarked alignment tools, the proposed method performed 5% better in terms of F1 on ontology alignment. Finally, a case study with a customer friendly website is illustrated to present how the alignment of cross-domain ontology help consumers on purchase decision support.
Embedding the Scientific Record on the Web: Towards Automating Scientific Discoveries
Authors: Yolanda Gil
Venue: WWW
Year: 2020

Future AI systems will be key contributors to science, but this is unlikely to happen unless we reinvent our current publications and embed our scientific records in the Web as structured Web objects. This implies that our scientific papers of the future will be complemented with explicit, structured descriptions of the experiments, software, data, and workflows used to reach new findings. These scientific papers of the future will not only culminate the promise of open science and reproducible research, but also enable the creation of AI systems that can ingest and organize scientific methods and processes, re-run experiments and re-analyze results, and explore their own hypothesis in systematic and unbiased ways. In this talk, I will describe guidelines for writing scientific papers of the future that embed the scientific record on the Web, and our progress on AI systems capable of using them to systematically explore experiments. I will also outline a research agenda with seven key characteristics for creating AI scientists that will exploit the Web to independently make new discoveries [1]. AI scientists have the potential to transform science and the processes of scientific discovery [2, 3].
Mining Signed Networks: Theory and Applications: Tutorial proposal for the Web Conference 2020
Authors: Aristides Gionis, Antonis Matakos, Bruno Ordozgoiti, Han Xiao
Venue: WWW
Year: 2020

Signed networks transform the information encoded by conventional graphs by attaching either a positive or a negative sign to every edge. This subtle modification vastly enhances the modelling capabilities of graphs. For instance, in a social network, where edges might represent interactions between users, the sign may determine whether an exchange was friendly or hostile. However, the introduction of edge signs invalidates many established methods and results from the graph-mining toolbox, and thus, problem formulations and algorithmic techniques must be studied anew. In this tutorial we aim to provide an overview of the literature in mining signed networks. We will present the most important theoretical results since their inception to the present day, we will discuss some of the most common applications, and we will reflect on emerging applications and directions for future work.
Effective Identification of Distinctive Wordmarks
Authors: Sujatha Das Gollapalli, Jung-Jae Kim
Venue: WWW
Year: 2020

A wordmark or a logotype string is a stylized text used by companies or businesses for the purposes of identification and branding products. Pie face under the product category “baked goods and pastries” and airbus smarter fleet under the product category “data processing and aircraft maintenance software” are examples of wordmarks. The wordmark strings are manually examined by patent officers for their distinctiveness and uniqueness to be accepted as “protected intellectual property” for specific businesses. We address the problem of automatically identifying acceptable English wordmarks based on their textual content. Different from most text mining tasks, our problem involves the classification of a small set of words (often less than five) in context of a specific product description. We handle this sparsity challenge by designing a range of features for characterizing wordmarks using the syntactic and linguistic properties of words as well as by incorporating co-occurrence and similarity information from external resources such as WordNet, Wikipedia, and Word Embeddings. We investigate machine learning models for this novel task and study their classification effectiveness on a large dataset of about 71K wordmarks.
Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data
Authors: Eduardo Graells-Garrido, Irene Meta, Feliu Serra-Burriel, Patricio Reyes, Fernando M. Cucchietti
Venue: WWW
Year: 2020

Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over- and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, elders, and tourists. We find that, within the three groups, cold spots have a lower diversity of amenities and services than hot spots. Also, cold spots of women and tourists tend to have lower population income. These insights apply to the floating population of Barcelona, thus augmenting the scope of how inclusiveness can be analyzed in the city.
Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems
Authors: Eduardo Graells-Garrido, Vanessa Peña Araya
Venue: WWW
Year: 2020

The rising availability of digital traces provides a fertile ground for new solutions to both, new and old problems in cities. Even though a massive data set analyzed with Data Science methods may provide a powerful solution to a problem, its adoption by relevant stakeholders is not guaranteed, due to adoption blockers such as lack of interpretability and transparency. In this context, this paper proposes a preliminary methodology toward bridging two disciplines, Data Science and Transportation, to solve urban problems with methods that are suitable for adoption. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the building of a potentially adoptable solution. As case study, we describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data.
Discovering Mathematical Objects of Interest - A Study of Mathematical Notations
Authors: André Greiner-Petter, Moritz Schubotz, Fabian Müller, Corinna Breitinger, Howard S. Cohl, Akiko Aizawa, Bela Gipp
Venue: WWW
Year: 2020

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today’s systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking with ‘Jacobi polynomial’); (3) we extend zbMATH’s search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.
Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
Authors: Xueliang Guo, Chongyang Shi, Chuanming Liu
Venue: WWW
Year: 2020

Recently, sequential recommendation has attracted substantial attention from researchers due to its status as an essential service for e-commerce. Accurately understanding user intention is an important factor to improve the performance of recommendation system. However, user intention is highly time-dependent and flexible, so it is very challenging to learn the latent dynamic intention of users for sequential recommendation. To this end, in this paper, we propose a novel intention modeling from ordered and unordered facets (IMfOU) for sequential recommendation. Specifically, the global and local item embedding (GLIE) we proposed can comprehensively capture the sequential context information in the sequences and highlight the important features that users care about. We further design ordered preference drift learning (OPDL) and unordered purchase motivation learning (UPML) to obtain user’s the process of preference drift and purchase motivation respectively. With combining the users’ dynamic preference and current motivation, it considers not only sequential dependencies between items but also flexible dependencies and models the user purchase intention more accurately from ordered and unordered facets respectively. Evaluation results on three real-world datasets demonstrate that our proposed approach achieves better performance than the state-of-the-art sequential recommendation methods achieving improvement of AUC by an average of 2.26%.