OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering
Authors: Yunqi Qiu, Manling Li, Yuanzhuo Wang, Yantao Jia, Xiaolong Jin
Venue: WWW
Year: 2018

Topic entity detection is to find out the main entity asked in a question, which is significant in question answering. Traditional methods ignore the information of entities, especially entity types and their hierarchical structures, restricting the performance. To take full advantage of Knowledge Base(KB) and detect topic entities correctly, we propose a deep neural model to leverage type hierarchy and relations of entities in KB. Experimental results demonstrate the effectiveness of the proposed method.
Tierless Web Programming in the Large
Authors: Gabriel Radanne, Jérôme Vouillon
Venue: WWW
Year: 2018

Tierless Web programming languages allow combining client-side and server-side programming in a single program. This allows defining expressions with both client and server parts, and at the same time provides good static guarantees regarding client-server communication. However, these nice properties come at a cost: most tierless languages offer very poor support for modularity and separate compilation. To regain this modularity and offer a larger-scale notion of composition, we propose to leverage a well-known tool: ML-style modules. In modern ML languages, the module system is a layer separate from the expression language. Eliom is an extension of OCaml for tierless Web programming which provides type-safe communication and an efficient execution model. In this article, we present how the Eliom module system combines the flexibility of tierless Web programming languages with a powerful module system, thus providing good support for abstraction, modularity and separate compilation. We also show that we can provide all these advantages while providing seamless integration with OCaml and its ecosystem.
The Shifting Landscape of Web Search and Mining: Past, Present, and Future
Authors: Davood Rafiei, Eugene Agichtein, Ricardo Baeza-Yates, Jon M. Kleinberg, Jure Leskovec
Venue: WWW
Year: 2018

The Web's content has been going through major changes, triggered by multiple factors including changes in user demographic and authoring behaviour, a shift in device types that access the Web, and changes in common use cases of the Web. More specifically, the number of mobile internet users has surpassed the desktop users according to different statistics; a considerable portion of web use cases are in the form of social interactions rather than information seeking; and the authoring behaviour has transformed from compiling a page and linking resources to sharing content with like-minded followers and leaving likes and comments on posts. Those changes have influenced and are expected to shape the way the content is organized, searched, ranked and analyzed. This panel brings together researchers who have been working in different established areas related to web search and mining, web content and social network analysis, and semantics and knowledge management. The panel will draw from the experience of the panellists, dealing with changes in their respective fields. In the first (role-playing) round, each panellist will strongly take a side on where the changes are heading, arguing that one form of content will dominate in the near future. In the second round, the panellists will counter each other and will share their vision on what future holds in terms of research problems and directions. The members of the audience will participate, in a QA session with the panellists, bringing their own perspectives to the discussion.
Mapping the Invocation Structure of Online Political Interaction
Authors: Manish Raghavan, Ashton Anderson, Jon M. Kleinberg
Venue: WWW
Year: 2018

The surge in political information, discourse, and interaction has been one of the most important developments in social media over the past several years. There is rich structure in the interaction among different viewpoints on the ideological spectrum. However, we still have only a limited analytical vocabulary for expressing the ways in which these viewpoints interact. In this paper, we develop network-based methods that operate on the ways in which users share content; we construct invocation graphs on Web domains showing the extent to which pages from one domain are invoked by users to reply to posts containing pages from other domains. When we locate the domains on a political spectrum induced from the data, we obtain an embedded graph showing how these interaction links span different distances on the spectrum. The structure of this embedded network, and its evolution over time, helps us derive macro-level insights about how political interaction unfolded through 2016, leading up to the US Presidential election. In particular, we find that the domains invoked in replies spanned increasing distances on the spectrum over the months approaching the election, and that there was clear asymmetry between the left-to-right and right-to-left patterns of linkage.
A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews
Authors: Vineeth Rakesh, Weicong Ding, Aman Ahuja, Nikhil Rao, Yifan Sun, Chandan K. Reddy
Venue: WWW
Year: 2018

Online reviews have become an inevitable part of a consumer's decision making process, where the likelihood of purchase not only depends on the product's overall rating, but also on the description of its aspects. Therefore, e-commerce websites such as Amazon and Walmart constantly encourage users to write good quality re- views and categorically summarize different facets of the products. However, despite such attempts, it takes a significant effort to skim through thousands of reviews and look for answers that address the query of consumers. For example, a gamer might be interested in buying a monitor with fast refresh rates and support for Gsync and Freesync technologies, while a photographer might be interested in aspects such as color depth and accuracy. To address these chal- lenges, in this paper, we propose a generative aspect summarization model called APSUM that is capable of providing fine-grained sum- maries of online reviews. To overcome the inherent problem of aspect sparsity, we impose dual constraints: (a) a spike-and-slab prior over the document-topic distribution and (b) a linguistic su- pervision over the word-topic distribution. Using a rigorous set of experiments, we show that the proposed model is capable of out- performing the state-of-the-art aspect summarization model over a variety of datasets and deliver intuitive fine-grained summaries that could simplify the purchase decisions of consumers.
Facebook (A)Live?: Are Live Social Broadcasts Really Broadcasts?
Authors: Aravindh Raman, Gareth Tyson, Nishanth Sastry
Venue: WWW
Year: 2018

The era of live-broadcast is back but with two major changes. First, unlike traditional TV broadcasts, content is now streamed over the Internet enabling it to reach a wider audience. Second, due to various user-generated content platforms it has become possible for anyone to get involved, streaming their own content to the world. This emerging trend of going live usually happens via social platforms, where users perform live social broadcasts predominantly from their mobile devices, allowing their friends (and the general public) to engage with the stream in real-time. With the growing popularity of such platforms, the burden on the current Internet infrastructure is therefore expected to multiply. With this in mind, we explore one such prominent platform - Facebook Live. We gather 3TB of data, representing one month of global activity and explore the characteristics of live social broadcast. From this, we derive simple yet effective principles which can decrease the network burden. We then dissect global and hyper-local properties of the video while on-air, by capturing the geography of the broadcasters or the users who produce the video and the viewers or the users who interact with it. Finally, we study the social engagement while the video is live and distinguish the key aspects when the same video goes on-demand. A common theme throughout the paper is that, despite its name, many attributes of Facebook Live deviate from both the concepts of live and broadcast.
An Attention Factor Graph Model for Tweet Entity Linking
Authors: Chenwei Ran, Wei Shen, Jianyong Wang
Venue: WWW
Year: 2018

The rapid expansion of Twitter has attracted worldwide attention. With more than 500 million tweets posted per day, Twitter becomes an invaluable information and knowledge source. Many Twitter related tasks have been studied, such as event extraction, hashtag recommendation, and topic detection. A critical step in understanding and mining information from Twitter is to disambiguate entities in tweets, i.e., tweet entity linking. It is a challenging task because tweets are short, noisy, and fresh. Many tweet-specific signals have been found to solve the tweet entity linking problem, such as user interest, temporal popularity, location information and so on. However, two common weaknesses exist in previous work. First, most proposed models are not flexible and extendable to fit new signals. Second, their scalability is not good enough to handle the large-scale social network like Twitter. In this work, we formalize the tweet entity linking problem into a factor graph model which has shown its effectiveness and efficiency in many other applications. We also propose selective attention over entities to increase the scalability of our model, which brings linear complexity. To adopt the attention mechanism in the factor graph, we propose a new type of nodes called pseudo-variable nodes to solve the asymmetry attention problem caused by the undirected characteristic of the factor graph. We evaluated our model on two different manually annotated tweet datasets. The experimental results show that our model achieves better performance in terms of both effectiveness and efficiency compared with the state-of-the-art approaches.
Learning to Search for Datasets
Authors: Maarten de Rijke
Venue: WWW
Year: 2018

Over the years, search engines have developed to return a broad range of retrievable items, from documents to answers, people, locations, and products. Research datasets are increasingly being turned in retrievable items too. This raises a number of interesting challenges. Starting from the user end (What do users want from datasets) to increasing the retrievability of datasets (What kind of contextual information is available to enrich datasets so as to make the more easily retrieval) to optimizing rankers for datasets in the absence of large volumes of interaction data (How can we train learning to rank datasets algorithms in weakly supervised ways).
Automatic Generation of Quizzes from DBpedia According to Educational Standards
Authors: Oscar Rodríguez Rocha, Catherine Faron-Zucker
Venue: WWW
Year: 2018

Educational quizzes are a powerful and popular tool to test the knowledge acquired by a learner and also to deepen her/his knowledge about a specific subject in an informal and entertaining way. Their production is a time-consuming task that can be automated by taking advantage of existing knowledge bases available on the Web of Linked Open Data (LOD). For these quizzes to be useful to learners, they must be generated according to the knowledge and skills defined by official educational standards for each subject and school year. This paper shows an approach to generate quizzes automatically according to the official French educational standards, from two different knowledge bases. Likewise, we show an evaluation of both knowledge bases.
Higher-order Network Representation Learning
Authors: Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh
Venue: WWW
Year: 2018

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations. In all cases, HONE outperforms recent embedding methods that are unable to capture higher-order structures with a mean relative gain in AUC of 19% (and up to 75% gain) across a wide variety of networks and embedding methods.
Deep Inductive Network Representation Learning
Authors: Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
Venue: WWW
Year: 2018

This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks. In particular, DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that naturally generalize across-networks and are therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable inductive graph representations, and is space-efficient (by learning sparse feature vectors). In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of O(|E|), and scalable for large networks via an efficient parallel implementation. Compared with recent methods, DeepGL is (1) effective for across-network transfer learning tasks and large (attributed) graphs, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 182x speedup in runtime performance, and (4) accurate with an average improvement in AUC of 20% or more on many learning tasks and across a wide variety of networks.
Retrieving Information from Multiple Sources
Authors: Anurag Roy, Kripabandhu Ghosh, Moumita Basu, Parth Gupta, Saptarshi Ghosh
Venue: WWW
Year: 2018

The Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve information from multiple sources. We propose a novel neural network based model which integrates the embeddings from multiple sources, and thus retrieves information from them jointly, %all the sources together, as opposed to combining multiple retrieval results. The importance of the proposed model is that no document-aligned comparable data is needed. Experiments on posts related to a particular event from three different sources - Facebook, Twitter and WhatsApp - exhibit the efficacy of the proposed model.
Mile High WiFi: A First Look At In-Flight Internet Connectivity
Authors: John P. Rula, James Newman, Fabián E. Bustamante, Arash Molavi Kakhki, David R. Choffnes
Venue: WWW
Year: 2018

In-Flight Communication (IFC), available on a growing number of commercial flights, is often received by consumers with both awe for its mere availability and harsh criticism for its poor performance. Indeed, IFC provides Internet connectivity in some of the most challenging conditions with aircraft traveling at speeds in excess of 500 mph at 30,000 feet above the ground. Yet, while existing services do provide basic Internet \em accessibility, anecdotal reports rank their quality of service as, at best, poor. In this paper, we present the first characterization of deployed IFC systems. Using over 45 flight-hours of measurements, we profile the performance of IFC across the two dominant access technologies -- direct air-to-ground communication (DA2GC) and mobile satellite service (MSS). We show that IFC QoS is in large part determined by the high latencies inherent to DA2GC and MSS, with RTTs averaging 200ms and 750ms, respectively, and that these high latencies directly impact the performance of common applications such as web browsing. While each IFC technology is based on well studied wireless communication technologies, our findings reveal that IFC links experience further degraded link performance than their technological antecedents. We find median loss rates of 7%, and nearly 40% loss at the 90th percentile for MSS, 6.8x larger than recent characterizations of residential satellite networks. We extend our IFC study exploring the potential of the newly released HTTP/2 and QUIC protocols in an emulated IFC environment, finding that QUIC is able to improve page load times by as much as 7.9 times. In addition, we find that HTTP/2»s use of multiplexing multiple requests onto a single TCP connection performs up to 4.8x \em worse than HTTP/1.1 when faced with large numbers of objects. We use network emulation to explore proposed technological improvements to existing IFC systems finding that high link losses, and not bandwidth, account for the largest factor of performance degradation with applications such as web browsing.
A journey from the Physical Web to the Physical Semantic Web
Authors: Michele Ruta, Floriano Scioscia, Giuseppe Loseto, Filippo Gramegna, Saverio Ieva, Agnese Pinto, Eugenio Di Sciascio
Venue: WWW
Year: 2018

ThePhysical Semantic Web (PSW) is a novel paradigm built upon the Google Physical Web (PW) approach and devoted to improve the quality of interactions in the Web of Things. Beacons expose semantic annotations instead of basic identifiers, ıe\ machine-understandable descriptions of physical resources. This enables novel ontology-based object advertisement and discovery and --in turn-- advanced user-to-thing and autonomous thing-to-thing interactions. The demo shows the evolution from the PW to the PSW in a discovery scenario set in a winery, where bottles are equipped with Bluetooth Low Energy beacons and a customer can discover them using her smartphone. The final goal is to prove benefits of PSW over basic PW, including: rich semantic-based object annotation; dynamic annotations exploiting on-board sensors; enhanced discovery and ranking of nearby objects through semantic matchmaking; availability of interactions even without working Internet infrastructure, by means of point-to-point data exchanges.
AI Cognition in Searching for Relevant Knowledge from Scholarly Big Data, Using a Multi-layer Perceptron and Recurrent Convolutional Neural Network Model
Authors: Iqra Safder, Saeed-Ul Hassan, Naif Radi Aljohani
Venue: WWW
Year: 2018

Although, over the years, information retrieval systems have shown tremendous improvements in searching for relevant scientific literature, human cognition is still required to search for specific document elements in full text publications. For instance, pseudocodes pertaining to algorithms published in scientific publications cannot be correctly matched against user queries, hence the process requires human involvement. AlgorithmSeer, a state-of-the-art technique, claims to replace humans in this task, but one of the limitations of such an algorithm search engine is that the metadata is simply a textual description of each pseudocode, without any algorithm-specific information. Hence, the search is performed merely by matching the user query to the textual metadata and ranking the results using conventional textual similarity techniques. The ability to automatically identify algorithm-specific metadata such as precision, recall, or f-measure would be useful when searching for algorithms. In this article, we propose a set of algorithms to extract further information pertaining to the performance of each algorithm. Specifically, sentences in an article that convey information about the efficiency of the corresponding algorithm are identified and extracted using a recurrent convolutional neural network (RCNN). Furthermore, we propose improving the efficacy of the pseudocode detection task by using a multi-layer perceptron (MLP) classification trained with 15 features, which improves the classification performance of the state-of-the-art pseudocode detection methods used in AlgorithmSeer by 27%. Finally, we show the advantages of the AI-enabled search engine (based on RCNN and MLP models) over conventional text-retrieval models.
A Location-Based Virtual Reality Application for Mountain Peak Detection
Authors: Antonio La Salandra, Piero Fraternali, Darian Frajberg
Venue: WWW
Year: 2018

Location-based mobile outdoor applications are powerful tools that can engage users in social and environmental tasks and support the emerging paradigm of citizen science. In this paper we present PeakLensVR, a virtual reality location-based mobile app that enables users to capture with their mobile phone panoramic mountain images and later visualize such images, enriched with metadata about the peaks visible from the capture point, with a low-end VR device. The goal of PeakLensVR is to harness the emerging trend of geo-located augmented and virtual reality applications to foster a community of environmentally conscious users who volunteer in the collection of mountain images for environment monitoring purposes.
Structural Novelty and Diversity in Link Prediction
Authors: Javier Sanz-Cruzado, Sofía M. Pepa, Pablo Castells
Venue: WWW
Year: 2018

Link prediction has mainly been addressed as an accuracy-targeting problem in the social networks field. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the social network where they are applied. Specifically, we consider the structural effects the prediction can have if the predicted links are added to the network. We consider further utility dimensions beyond prediction accuracy, namely novelty and diversity. We discuss the adaptation, for this purpose, of specific network, novelty and diversity metrics from social network analysis, recommender systems, and information retrieval.
DISCOVER: Mining Online Chatter for Emerging Cyber Threats
Authors: Anna Sapienza, Sindhu Kiranmai Ernala, Alessandro Bessi, Kristina Lerman, Emilio Ferrara
Venue: WWW
Year: 2018

Widespread adoption of networking technologies has brought about tremendous economic and social growth, but also exposed individuals and organization to new threats from malicious cyber actors. Recent attacks by WannaCry and NotPetya ransomware crypto-worms, infected hundreds of thousands of computer systems world wide, compromising data and critical infrastructure. In order to limit their impact, it is, therefore, critical to detect---and even predict---cyber attacks before they spread. Here, we introduce DISCOVER, an early cyber threat warning system, that mines online chatter from cyber actors on social media, security blogs, and darkweb forums, to identify words that signal potential cyber attacks. We evaluate DISCOVER and find that it can identify terms related to emerging cyber threats with precision above $80%$. DISCOVER also generates a time line of related online discussions on different Web sources that can be useful for analyzing emerging cyber threats.
Contextual Web Summarization: A Supervised Ranking Approach
Authors: Amit Sarkar, G. Srinivasaraghavan
Venue: WWW
Year: 2018

We investigate the task of reading context-biased web summarization, where the goal is to extract information relevant to the current reading context from a cited web article. In certain kind of linked document sets such as Wikipedia articles, scientific papers as well as news and blogs, such contextual summaries can be useful in providing additional related information to the user helping in the reading task. In this work, we focus on web articles only and try to find out the set of key components that contribute to building up the reading context. We build a supervised model for ranking sentences from the cited document according to their contextual salience. Initial evaluation based on annotated data-set of web articles show that our ranking model performs better than the generic summaries as well as baseline context-biased summaries.