OpenRaiser/Intern-Atlas huggingface dataset:
Why Reinvent the Wheel: Let's Build Question Answering Systems Together
Authors: Kuldeep Singh, Arun Sethupat Radhakrishna, Andreas Both, Saeedeh Shekarpour, Ioanna Lytra, Ricardo Usbeck, Akhilesh Vyas, Akmal Khikmatullaev, Dharmen Punjani, Christoph Lange, Maria-Esther Vidal, Jens Lehmann, Sören Auer
Venue: WWW
Year: 2018

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
Toward Approximate Intelligence: Approximate Query Engines & Approximate Data Exploration
Authors: Dominik Slezak
Venue: WWW
Year: 2018

The AI methods are regaining a lot of attention in the areas of data analytics and decision support. Given the increasing amount of information and computational resources available, it is now possible for intelligent algorithms to learn from the data and assist humans more efficiently. Still, there is a question about the goals of learning and a form of the resulting data-driven knowledge. It is evident that humans do not operate with precise information in decision-making and, thus, it might be unnecessary to provide them with complete outcomes of analytical processes. Consequently, the next question arises whether approximate results of computations or results derived from the approximate data could be delivered more efficiently than their standard counterparts. Such questions are analogous to the ones about precision of calculations conducted by machine learning and KDD methods, whereby heuristic algorithms could be boosted by letting them rely on approximate computations. This leads us toward discussion of the importance of approximations in the areas of machine intelligence and business intelligence and, more broadly, the meaning of approximate derivations for various aspects of AI. In this talk, this discussion is supported by four industry-related case studies\footnoteThe first case study refers entirely to the author's work for Security On-Demand (\urlhttps://www.securityondemand.com/ ). The work on the second case study was co-financed by the EU Smart Growth Operational Programme 2014-2020 under the Innovation Voucher project POIR.02.03.02-14-0009/15-00. The work on the third case study is co-financed by the EU Smart Growth Operational Programme 2014-2020 under the project POIR.01.01.01-00-0831/17-00. The work on the fourth case study is co-financed by the EU Smart Growth Operational Programme 2014-2020 under the GameINN project POIR.01.02.00-00-0184/17-00 : \beginenumerate ıtem The approximate database engine based on the paradigms of rough-granular computing applied in the area of cyber-security analytics \citeslezak:queryengine,\citeslezak:scalablefeature, \citeslezak:cyberengine ıtem The similarity-based feature engineering methodology embedded into an HR support system working with heterogeneous information sources \citeslezak:jobs ; ıtem The ensemble-based attribute approximation approach that will be used in the area of online health support \citeOvu ; ıtem The process of approximate data generation that will be used for tuning an online gaming coaching platform \citeEsensei.\endenumerate
Hybrid.AI: A Learning Search Engine for Large-scale Structured Data
Authors: Sean Soderman, Anusha Kola, Maksim Podkorytov, Michael Geyer, Michael N. Gubanov
Venue: WWW
Year: 2018

Variety of Big data For example, there are millions of tables available on the Web, but the most relevant search result does not necessarily match the keyword-query exactly due to a variety of ways to represent the same information.
Handling Confounding for Realistic Off-Policy Evaluation
Authors: Saurabh Sohoney, Nikita Prabhu, Vineet Chaoji
Venue: WWW
Year: 2018

Inverse Propensity Score estimator (IPS) is a basic, unbiased, off-policy evaluation technique to measure the impact of a user-interactive system without serving live traffic. We present our work on applying IPS to real-world settings by addressing some practical challenges, thereby enabling successful policy evaluation. In particular, we show that off-policy evaluation can be impossible in the absence of a complete context and we describe a systematic way of defining the context.
MapSQ: A Plugin-based MapReduce Framework for SPARQL Queries on GPU
Authors: Jiaming Song, Xiaowang Zhang, Peng Peng, Zhiyong Feng, Lei Zou
Venue: WWW
Year: 2018

In this paper, we present a plugin-based framework (MapSQ) with three parts for SPARQL queries utilizing high-performance of GPU to accelerate answering in a convenient way. Selector chooses suitable join order according to characteristics of data and queries. Executor answers subqueries and returns intermediate solutions and GPU Computing obtains the join result of intermediate solutions through MapReduce. Finally, we evaluate MapSQ bulit on gStore and RDF-3X on the LUBM benchmark and YAGO datasets (over 200 million triples). The experimental results show that MapSQ significantly improves the performance of SPARQL query engines with speedup up to 33.
Gold Standard Creation for Microblog Retrieval: Challenges of Completeness in IRMiDis 2017
Authors: Ribhav Soni, Sukomal Pal
Venue: WWW
Year: 2018

Microblogging sites like Twitter, Facebook, etc., are important sources of first-hand accounts during disaster situations, and have the potential to significantly aid disaster relief efforts. The IRMiDis track at FIRE 2017 focused on developing and comparing IR approaches to automatically identify and match tweets that indicate the need or availability of a resource, leading to the creation of a benchmark dataset for future improvements in this task. However, based on our experiments, we argue that the gold standard data obtained in the track is substantially incomplete. We also discuss some reasons why it may have been so, and provide some suggestions for making more robust ground truth data in such tasks.
Extreme Multilabel Classification for Social Media Chairs' Welcome and Organization
Authors: Akshay Soni, Aasish Pappu, Róbert Busa-Fekete, Krzysztof Dembczynski
Venue: WWW
Year: 2018

Extreme Multilabel Classification (XMLC) is a very active and rapidly growing research area that deals with the problem of labeling an item with a small set of tags out of an extremely large number of potential tags. Applications include content understanding, document tagging, image tagging, biological sequence tagging, recommendation, etc. While the difficulty and the potential applications of XMLC are well understood in the core machine learning community, to the best of our knowledge, XMLC has not made inroads in the field of Information Retrieval (IR) and related areas. The aim of this workshop is to bring researchers from academia and industry in order to further advance this very exciting field and come up with potential applications of XMLC in new areas.
GraphChain: A Distributed Database with Explicit Semantics and Chained RDF Graphs
Authors: Mirek Sopek, Przemyslaw Gradzki, Witold Kosowski, Dominik Kuzinski, Rafal Trójczak, Robert Trypuz
Venue: WWW
Year: 2018

In this paper we present a new idea of creating a Blockchain compliant distributed database which exposes its data with explicit semantics, is easily and natively accessible, and which applies Blockchain securitization mechanisms to the RDF graph data model directly, without additional packaging or specific serialisation. Essentially, the resulting database forms the linked chain of named RDF graphs and is given a name: GraphChain. Such graphs can then be published with the help of any standard mechanisms using triplestores or as linked data objects accessible via standard web mechanisms using the HTTP protocol to make them available on the web. They can also be easily queried using techniques like SPARQL or methods typical to available RDF graphs frameworks (like rdflib, Apache Jena, RDF4J, OWL API, RDF HDT, dotnetRDF and others). The GraphChain concept comes with its own, OWL-compliant ontology that defines all the structural, invariant elements of the GraphChain and defines their basic semantics. The paper describes also a few simple, prototypical GraphChain implementations with examples created using Java, .NET/C# and JavaScript/Node.js frameworks.
Session-based News Recommendations
Authors: Gabriele Sottocornola, Panagiotis Symeonidis, Markus Zanker
Venue: WWW
Year: 2018

In the context of news recommendations, many time-aware approaches were proposed. These approaches have tried to capture the recency of news with respect to their short life span, by using either decaying weights on past articles or even forgetting them. However, most of these approaches have missed to consider sessions, which encapsulate inside them the articles that a user has interacted with in a short time period. In this paper, we provide news recommendations based on user sessions to reveal their short-term intentions. We also combine content-based with collaborative filtering to deal with the severe data sparsity problem that exists in our real-life data set. We have experimentally seen that the users' interests evolve over time and that our strategies can adapt fast to these changes.
TempWeb 2018 Chairs' Welcome and Organization
Authors: Marc Spaniol, Ricardo Baeza-Yates, Julien Masanès
Venue: WWW
Year: 2018

Time is a key dimension to understand the Web. It is fair to say that it has not received yet all the attention it deserves and TempWeb is an attempt to help remedy this situation by putting time as the center of its reflection. Studying time in this context actually covers a large spectrum, from the extraction of temporal information and knowledge, to diachronic studies for the design of infrastructural and experimental settings enabling a proper observation of this dimension.
Patterns of Volunteer Behaviour Across Online Citizen Science
Authors: Helen Spiers, Alexandra Swanson, Lucy Fortson, Brooke D. Simmons, Laura Trouille, Samantha Blickhan, Chris J. Lintott
Venue: WWW
Year: 2018

Human-computer systems are increasingly applied to data reduction problems; citizen science platforms (e.g. the Zooniverse) are one type of such a system. These platforms function as social machines, combining volunteer efforts with automated processes to enable distributed data analysis. The rapid growth of this approach is increasing the need to understand how we can improve volunteer interaction and engagement. Here, we utilize the most comprehensive collection of online citizen science data gathered to date to examine multiple variables across 63 Zooniverse projects. Our analyses reveal how subtle design changes can influence many facets of volunteer interaction, generating insights that have implications for the design and study of citizen science projects, and future research.
Predicting Document Creation Times in News Citation Networks
Authors: Andreas Spitz, Jannik Strötgen, Michael Gertz
Venue: WWW
Year: 2018

For the temporal analysis of news articles or the extraction of temporal expressions from such documents, accurate document creation times are indispensable. While document creation times are available as time stamps or HTML metadata in many cases, depending on the document collection in question, this data can be inaccurate or incomplete in others. Especially in digitally published online news articles, publication times are often missing from the article or inaccurate due to (partial) updates of the content at a later time. In this paper, we investigate the prediction of document creation times for articles in citation networks of digitally published news articles, which provide a network structure of knowledge flows between individual articles in addition to the contained temporal expressions. We explore the evolution of such networks to motivate the extraction of suitable features, which we utilize in a subsequent prediction of document creation times, framed as a regression task. Based on our evaluation of several established machine learning regressors on a large network of English news articles, we show that the combination of temporal and local structural features allows for the estimation of document creation times from the network.
Exposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers
Authors: Bharat Srinivasan, Athanasios Kountouras, Najmeh Miramirkhani, Monjur Alam, Nick Nikiforakis, Manos Antonakakis, Mustaque Ahamad
Venue: WWW
Year: 2018

Technical Support Scams (TSS), which combine online abuse with social engineering over the phone channel, have persisted despite several law enforcement actions. Although recent research has provided important insights into TSS, these scams have now evolved to exploit ubiquitously used online services such as search and sponsored advertisements served in response to search queries. We use a data-driven approach to understand search-and-ad abuse by TSS to gain visibility into the online infrastructure that facilitates it. By carefully formulating tech support queries with multiple search engines, we collect data about both the support infrastructure and the websites to which TSS victims are directed when they search online for tech support resources. We augment this with a DNS-based amplification technique to further enhance visibility into this abuse infrastructure. By analyzing the collected data, we provide new insights into search-and-ad abuse by TSS and reinforce some of the findings of earlier research. Further, we demonstrate that tech support scammers are (1) successful in getting major as well as custom search engines to return links to websites controlled by them, and (2) they are able to get ad networks to serve malicious advertisements that lead to scam pages. Our study period of approximately eight months uncovered over 9,000 TSS domains, of both passive and aggressive types, with minimal overlap between sets that are reached via organic search results and sponsored ads. Also, we found over 2,400 support domains which aid the TSS domains in manipulating organic search results. Moreover, to our surprise, we found very little overlap with domains that are reached via abuse of domain parking and URL-shortening services which was investigated previously. Thus, investigation of search-and-ad abuse provides new insights into TSS tactics and helps detect previously unknown abuse infrastructure that facilitates these scams.
Structured Knowledge on the Web 7.0
Authors: Steffen Staab, Jens Lehmann, Ruben Verborgh
Venue: WWW
Year: 2018

Structured Knowledge on the Web had an intriguing history before it has become successful. We briefly revisit this history, before we go into the longer discussion about how structured knowledge on the Web should be devised such that it benefits even more applications. Core to this discussion will be issues like trust, information infrastructure usability and resilience, promising realms of structured knowledge and principles and practices of data sharing.
What is in a Web View: An Analysis of Progressive Web App Features When the Means of Web Access is not a Web Browser
Authors: Thomas Steiner
Venue: WWW
Year: 2018

Progressive Web Apps (PWA) are a new class of Web applications, enabled for the most part by the Service Workers APIs. Service Workers allow apps to work offline by intercepting network requests to deliver programmatic or cached responses, Service Workers can receive push notifications and synchronize data in the background even when the app is not running, andtogether with Web App Manifestsallow users to install PWAs to their devices' home screens. Service Workers being a Web standard, support has landed in several stand-alone Android Web browsersamong them (but not limited to) Chrome and its open-source foundation Chromium, Firefox, Edge, Opera, UC Browser, Samsung Internet, andeagerly awaitediOS Safari. In this paper, we examine the PWA feature support situation in Web Views, that is, in-app Web experiences that are explicitly not stand-alone browsers. Such in-app browsers can commonly be encountered in chat applications like WeChat or WhatsApp, online social networks like Facebook or Twitter, but also email clients like Gmail, or simply anywhere where Web content is displayed inside native apps. We have developed an open-source application called PWA Feature Detector that allows for easily testing in-app browsers (and naturally stand-alone browsers), and have evaluated the level of support for PWA features on different devices and Web Views. On the one hand, our results show that there are big differences between the various Web View technologies and the browser engines they are based upon, but on the other hand, that for Android the results are independent from the devices' operating systems, which is good news given the problematic update policy of many device manufacturers. These findings help developers make educated choices when it comes to determining whether a PWA is the right approach given their target users' means of Web access.
Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity
Authors: Ana-Andreea Stoica, Christopher J. Riederer, Augustin Chaintreau
Venue: WWW
Year: 2018

As social recommendations such as friend suggestions and people to follow become increasingly popular and influential on the growth of social media, we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy. To study this imbalance in online equal opportunities, we leverage new Instagram data and offer for the first time an analysis that studies the effect of gender, homophily and growth dynamics under social recommendations. Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of color from attaining equal representation. What raises concern is that our proof shows that under fixed minority and homophily parameters the algorithmic effect is systematically larger than the glass ceiling generated by the spontaneous growth of social networks. We discuss ways to address this concern in future design.
Detecting Crowdturfing "Add to Favorites" Activities in Online Shopping
Authors: Ning Su, Yiqun Liu, Zhao Li, Yuli Liu, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2018

"Add to Favorites" is a popular function in online shopping sites which helps users to make a record of potentially interesting items for future purchases. It is usually regarded as a type of explicit feedback signal for item popularity and therefore also adopted as a ranking signal by many shopping search engines. With the increasing usage of crowdsourcing platforms, some malicious online sellers also organize crowdturfing activities to increase the numbers of "Add to Favorites" for their items. By this means, they expect the items to gain higher positions in search ranking lists and therefore boost sales. This kind of newly-appeared malicious activity proposes challenges to traditional search spam detection efforts because it involves the participation of many crowd workers who are normal online shopping users in most of the times, and these activities are composed of a series of behaviors including search, browse, click and add to favorites. To shed light on this research question, we are among the first to investigate this particular spamming activity by looking into both the task organization information in crowdsourcing platforms and the user behavior information from online shopping sites. With a comprehensive analysis of some ground truth spamming activities from the perspective of behavior, user and item, we propose a factor graph based model to identify this kind of spamming activity. Experimental results based on data collected in practical shopping search environment show that our model helps detect malicious "Add to Favorites" activities effectively.
Real-time, Scalable, Content-based Twitter Users Recommendation
Authors: Julien Subercaze, Christophe Gravier, Frédérique Laforest
Venue: WWW
Year: 2018

Real-time recommendation of Twitter users based on the content of their profiles is a very challenging task. Traditional IR methods such as TF-IDF fail to handle efficiently large datasets. In this paper we present a scalable approach that allows real time recommendation of users based on their tweets. Our model builds a graph of terms, driven by the fact that users sharing similar interests will share similar terms. We show how this model can be encoded as a compact binary footprint, that allows very fast comparison and ranking, taking full advantage of modern CPU architectures. We validate our approach through an empirical evaluation against the Apache Lucene's implementation of TF-IDF. We show that our approach is in average two hundred times faster than standard optimised implementation of TF-IDF with a precision of 58%. The work presented here has been published in The Web Intelligence Journal.
Computational Creative Advertisements
Authors: Wei Sun, Ying Li, Anshul Sheopuri, Thales Teixeira
Venue: WWW
Year: 2018

Making successful video advertisements has long been considered a combination of art and business acumen. In this work, we propose a system to assist human designers to produce more effective advertisements with predictable outcomes. We formalize this concept with a dynamic Bayesian network (DBN), where we represent the knowledge base with data collected from large-scale field experiments in a novel setting. Face and eye tracking which continuously measures viewers emotional responses and viewing interest on 169 television advertisements for 2334 participants, along with moment-to-moment branding activities in the advertisements are used to estimate the model. The resulting DBN represents relationships across advertisement content, viewers emotional responses, as well as effectiveness metrics such as ad avoidance, sharing and influence on purchase. Conditioned on the specified requirement on the ad, a human designer can draw high scoring samples from the DBN, which represent the optimized sequences of branding activities and entertainment content.
Domain-specific Insight Graphs (DIG)
Authors: Pedro A. Szekely, Mayank Kejriwal
Venue: WWW
Year: 2018

The DARPA Memex program was established with the goal of funding research into building domain-specific search systems that integrated state-of-the-art focused crawling (domain discovery) information extraction and semantic search, and that could be used by users and domain experts with no programming or technical experience. Domain-specific Insight Graphs (DIG) was proposed and funded under Memex and has led to an end-to-end search system currently being used by over 200 law enforcement for combating human trafficking, by investigators from the Securities and Exchange Commission (SEC) in the US for investigating securities fraud, and for numerous other domains of a difficult, socially consequential (e.g., investigative) and unusual nature.