OpenRaiser/Intern-Atlas huggingface dataset:
The PLASMA Framework: Laying the Path to Domain-Specific Semantics in Dataspaces
Authors: Alexander Paulus, André Pomp, Tobias Meisen
Venue: WWW
Year: 2023

Modern data management is evolving from centralized integration-based solutions to a non-integration-based process of finding, accessing and processing data, as observed within dataspaces. Common reference dataspace architectures assume that sources publish their own domain-specific schema. These schemas, also known as semantic models, can only be partially created automatically and require oversight and refinement by human modellers. Non-expert users, such as mechanical engineers or municipal workers, often have difficulty building models because they are faced with multiple ontologies, classes, and relations, and existing tools are not designed for non-expert users. The PLASMA framework consists of a platform and auxiliary services that focus on providing non-expert users with an accessible way to create and edit semantic models, combining automation approaches and support systems such as a recommendation engine. It also provides data conversion from raw data to RDF. In this paper we highlight the main features, like the modeling interface and the data conversion engine. We discuss how PLASMA as a tool is suitable for building semantic models by non-expert users in the context of dataspaces and show some applications where PLASMA has already been used in data management projects.
The One Hundred Year Web
Authors: Steven Pemberton
Venue: WWW
Year: 2023

The year 2023 marks the thirty-second anniversary of the World Wide Web being announced. In the intervening years, the web has become an essential part of the fabric of society. Part of that is that huge amounts of information that used to be available (only) on paper is now available (only) electronically. One of the dangers of this is that owners of information often treat the data as ephemeral, and delete old information once it becomes out of date. As a result society is at risk of losing large parts of its history. So it is time to assess how we use the web, how it has been designed, and what we should do to ensure that in one hundred years time (and beyond) we will still be able to access, and read, what we are now producing. We can still read 100 year-old books; that should not be any different for the web. This paper takes a historical view of the web, and discusses the web from its early days: why it was successful compared with other similar systems emerging at the time, the things it did right, the mistakes that were made, and how it has developed to the web we know today, to what extent it meets the requirements needed for such an essential part of society's infrastructure, and what still needs to be done.
Query-Driven Knowledge Graph Construction using Question Answering and Multimodal Fusion
Authors: Yang Peng
Venue: WWW
Year: 2023

Over recent years, large knowledge bases have been constructed to store massive knowledge graphs. However, these knowledge graphs are highly incomplete. To solve this problem, we propose a web-based question answering system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. To utilize unstructured information from the Web for knowledge graph construction, we design multimodal features and question templates to extract missing facts, which can achieve good quality with very few questions. The question answering system also employs structured information from knowledge bases, such as entity types and entity-to-entity relatedness, to help improve extraction quality. To improve system efficiency, we utilize a few query-driven techniques for web-based question answering to reduce the runtime and provide fast responses to user queries. Extensive experiments have been conducted to demonstrate the effectiveness and efficiency of our system.
Cross-lingual Search for e-Commerce based on Query Translatability and Mixed-Domain Fine-Tuning
Authors: Jesus Perez-Martin, Jorge Gomez-Robles, Asier Gutiérrez-Fandiño, Pankaj Adsul, Sravanthi Rajanala, Leonardo Lezcano
Venue: WWW
Year: 2023

Online stores in the US offer a unique scenario for Cross-Lingual Information Retrieval (CLIR) due to the mix of Spanish and English in user queries. Machine Translation (MT) provides an opportunity to lift relevance by translating the Spanish queries to English before delivering them to the search engine. However, polysemy-derived problems, high latency and context scarcity in product search, make generic MT an impractical solution. The wide diversity of products in marketplaces injects non-translatable entities, loanwords, ambiguous morphemes, cross-language ambiguity and a variety of Spanish dialects in the communication between buyers and sellers, posing a thread to the accuracy of MT. In this work, we leverage domain adaptation on a simplified architecture of Neural Machine Translation (NMT) to make both latency and accuracy suitable for e-commerce search. Our NMT model is fine-tuned on a mixed-domain corpus based on engagement data expanded with catalog back-translation techniques. Beyond accuracy, and given that translation is not the goal but the means to relevant results, the problem of Query Translatability is addressed by a classifier on whether the translation should be automatic or explicitly requested. We assembled these models into a query translation system that we tested and launched at Walmart.com , with a statistically significant lift in Spanish GMV and an nDCG gain for Spanish queries of +70%.
Auctions without commitment in the auto-bidding world
Authors: Andrés Perlroth, Aranyak Mehta
Venue: WWW
Year: 2023

Advertisers in online ad auctions are increasingly using auto-bidding mechanisms to bid into auctions instead of directly bidding their value manually. One of the prominent auto-bidding formats is that of target cost-per-acquisition (tCPA) which maximizes the volume of conversions subject to a return-of-investment constraint. From an auction theoretic perspective however, this trend seems to go against foundational results that postulate that for profit-maximizing (aka quasi-linear) bidders, it is optimal to use a classic bidding system like marginal CPA (mCPA) bidding rather than using strategies like tCPA. In this paper we rationalize the adoption of such seemingly sub-optimal bidding within the canonical quasi-linear framework. The crux of the argument lies in the notion of commitment. We consider a multi-stage game where first the auctioneer declares the auction rules; then bidders select either the tCPA or mCPA bidding format (and submit bids accordingly); and then, if the auctioneer lacks commitment, it can revisit the rules of the auction (e.g., may readjust reserve prices depending on the bids submitted by the bidders). Our main result is that so long as a bidder believes that the auctioneer lacks commitment to follow the rule of the declared auction then the bidder will make a higher profit by choosing the tCPA format compared to the classic mCPA format. We then explore the commitment consequences for the auctioneer. In a simplified version of the model where there is only one bidder, we show that the tCPA subgame admits a credible equilibrium while the mCPA format does not. That is, when the bidder chooses the tCPA format the auctioneer can credibly implement the auction rules announced at the beginning of the game. We also show that, under some mild conditions, the auctioneer’s revenue is larger when the bidder uses the tCPA format rather than mCPA. We further quantify the value for the auctioneer to be able to commit to the declared auction rules.
Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories
Authors: Youri Peskine, Raphaël Troncy, Paolo Papotti
Venue: WWW
Year: 2023

Online misinformation has become a major concern in recent years, and it has been further emphasized during the COVID-19 pandemic. Social media platforms, such as Twitter, can be serious vectors of misinformation online. In order to better understand the spread of these fake-news, lies, deceptions, and rumours, we analyze the correlations between the following textual features in tweets: emotion, sentiment, political bias, stance, veracity and conspiracy theories. We train several transformer-based classifiers from multiple datasets to detect these textual features and identify potential correlations using conditional distributions of the labels. Our results show that the online discourse regarding some topics, such as COVID-19 regulations or conspiracy theories, is highly controversial and reflects the actual U.S. political landscape.
Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces
Authors: Danh Le Phuoc, Sonja Schimmler, Anh Le-Tuan, Uwe A. Kuehn, Manfred Hauswirth
Venue: WWW
Year: 2023

This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.
Universal Model in Online Customer Service
Authors: Shu-Ting Pi, Cheng-Ping Hsieh, Qun Liu, Yuying Zhu
Venue: WWW
Year: 2023

Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predicting labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.
Deep Learning Provenance Data Integration: a Practical Approach
Authors: Débora B. Pina, Adriane Chapman, Daniel de Oliveira, Marta Mattoso
Venue: WWW
Year: 2023

A Deep Learning (DL) life cycle involves several data transformations, such as performing data pre-processing, defining datasets to train and test a deep neural network (DNN), and training and evaluating the DL model. Choosing a final model requires DL model selection, which involves analyzing data from several training configurations (e.g. hyperparameters and DNN architectures). Tracing training data back to pre-processing operations can provide insights into the model selection step. Provenance is a natural solution to represent data derivation of the whole DL life cycle. However, there are challenges in providing an integration of the provenance of these different steps. There are a few approaches to capturing and integrating provenance data from the DL life cycle, but they require that the same provenance capture solution is used along all the steps, which can limit interoperability and flexibility when choosing the DL environment. Therefore, in this work, we present a prototype for provenance data integration using different capture solutions. We show use cases where the integrated provenance from pre-processing and training steps can show how data pre-processing decisions influenced the model selection. Experiments were performed using real-world datasets to train a DNN and provided evidence of the integration between the considered steps, answering queries such as how the data used to train a model that achieved a specific result was processed.
Wikidata Atlas: Putting Wikidata on the Map
Authors: Benjamín Del Pino, Aidan Hogan
Venue: WWW
Year: 2023

Wikidata Atlas is an online system that allows users to explore Wikidata items on an interactive global map; for example, users can explore the global distribution of all lighthouses described by Wikidata. Designing such a system poses challenges in terms of scalability, where some classes have hundreds of thousands of instances; efficiency, where visualisations are generated live; freshness, where we want changes on Wikidata to be reflected as they happen in the system; and usability, where we aim for the system to be accessible for a broad audience. Herein we describe the design and implementation of the system in light of these challenges.
Using diversity as a source of scientific innovation for the Web
Authors: Barbara Poblete
Venue: WWW
Year: 2023

The Web has become a resource that allows us to make sense of social phenomena around the world. This started the moment users became content creators, and has grown with the emergence of social platforms tailored to our need to connect and share with others. Throughout my work, I’ve come to appreciate how social media has democratized access to real-world news and social sentiment, while also witnessing the loss of trust created by fake information. As a computer scientist from Chile in Latin America, I have worked on a range of problems that were driven by local needs. Many times, I have tried to apply state of the art solutions to well-known problems, only to find that these don’t work outside of their initial evaluation dataset. In this talk, I’ll discuss how geographical, language, and social diversity have opened new avenues for innovation and better understanding the social Web. I’ll also show that to truly create useful technological solutions, we must develop inclusive research and resources.
1st Workshop on Federated Learning Technologies
Authors: Mirko Polato, Roberto Esposito, Walter Riviera, Zenglin Xu, Irwin King
Venue: WWW
Year: 2023

AI-based systems, especially those based on machine learning technologies, have become central in modern societies. In the meanwhile, users and legislators are becoming aware of privacy issues. Users are increasingly reluctant in sharing their sensitive information, and new laws have been enacted to regulate how private data is handled (e.g., the GDPR). Federated Learning (FL) has been proposed to develop better AI systems without compromising users’ privacy and the legitimate interests of private companies. Although still in its infancy, FL has already shown significant theoretical and practical results making FL one of the hottest topics in the machine learning community. Given the considerable potential in overcoming the challenges of protecting users’ privacy while making the most of available data, we propose a workshop on Federated Learning Technologies (FLT) at TheWebConf 2023. The goal of this workshop is to focus the attention of the TheWebConf research community on addressing the open questions and challenges in this thriving research area. Given the broad range of competencies in the TheWebConf community, the workshop will welcome foundational contributions as well as contributions expanding the scope of these techniques, such as improvements in the interpretability and fairness of the learned models.
SPACE_DS: Towards a Circular Economy Data Space
Authors: André Pomp, Maike Jansen, Holger Berg, Tobias Meisen
Venue: WWW
Year: 2023

The circular economy (CE) is essential to achieving a sustainable future through resource conservation and climate protection. Efficient use of materials and products over time is a critical aspect of CE, helping to reduce CO2 emissions, waste and resource consumption. The Digital Product Passport (DPP) is a CE-specific approach that contains information about components and their origin, and can also provide environmental and social impact assessments. However, creating a DPP requires collecting and analyzing data from many different stakeholders along the supply chain and even throughout the product lifecycle. In this paper, we present a concept for the SPACE_DS, which is a data space for circular economy data. A key point here is that the SPACE_DS enables the creation of DPPs by especially considering privacy and security concerns of data providers.
Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?
Authors: Manuel Pratelli, Marinella Petrocchi, Fabio Saracco, Rocco De Nicola
Venue: WWW
Year: 2023

For more than a decade scholars have been investigating the disinformation flow on social media contextually to societal events, like, e.g., elections. In this paper, we analyze the Twitter traffic related to the US 2020 pre-election debate and ask whether it mirrors the electoral system. The U.S. electoral system provides that, regardless of the actual vote gap, the premier candidate who received more votes in one state ‘takes’ that state. Criticisms of this system have pointed out that election campaigns can be more intense in particular key states to achieve victory, so-called swing states. Our intuition is that election debate may cause more traffic on Twitter-and probably be more plagued by misinformation-when associated with swing states. The results mostly confirm the intuition. About 88% of the entire traffic can be associated with swing states, and links to non-trustworthy news are shared far more in swing-related traffic than the same type of news in safe-related traffic. Considering traffic origin instead, non-trustworthy tweets generated by automated accounts, so-called social bots, are mostly associated with swing states. Our work sheds light on the role an electoral system plays in the evolution of online debates, with, in the spotlight, disinformation and social bots.
Detecting and Limiting Negative User Experiences in Social Media Platforms
Authors: Lluís Garcia Pueyo, Vinodh Kumar Sunkara, Prathyusha Senthil Kumar, Mohit Diwan, Qian Ge, Behrang Javaherian, Vasilis Verroios
Venue: WWW
Year: 2023

Item ranking is important to a social media platform’s success. The order in which posts, videos, messages, comments, ads, used products, notifications are presented to a user greatly affects the time spent on the platform, how often they visit it, how much they interact with each other, and the quantity and quality of the content they post. To this end, item ranking algorithms use models that predict the likelihood of different events, e.g., the user liking, sharing, commenting on a video, clicking/converting on an ad, or opening the platform’s app from a notification. Unfortunately, by solely relying on such event-prediction models, social media platforms tend to over optimize for short-term objectives and ignore the long-term effects. In this paper, we propose an approach that aims at improving item ranking long-term impact. The approach primarily relies on an ML model that predicts negative user experiences. The model utilizes all available UI events: the details of an action can reveal how positive or negative the user experience has been; for example, a user writing a lengthy report asking for a given video to be taken down, likely had a very negative experience. Furthermore, the model takes into account detected integrity (e.g., hostile speech or graphic violence) and quality (e.g., click or engagement bait) issues with the content. Note that those issues can be perceived very differently from different users. Therefore, developing a personalized model, where a prediction refers to a specific user for a specific piece of content at a specific point in time, is a fundamental design choice in our approach. Besides the personalized ML model, our approach consists of two more pieces: (a) the way the personalized model is integrated with an item ranking algorithm and (b) the metrics, methodology, and success criteria for the long term impact of detecting and limiting negative user experiences. Our evaluation process uses extensive A/B testing on the Facebook platform: we compare the impact of our approach in treatment groups against production control groups. The AB test results indicate a 5% to 50% reduction in hides, reports, and submitted feedback. Furthermore, we compare against a baseline that does not include some of the crucial elements of our approach: the comparison shows our approach has a 100x to 30x lower False Positive Ratio than a baseline. Lastly, we present the results from a large scale survey, where we observe a statistically significant improvement of 3 to 6 percent in users’ sentiment regarding content suffering from nudity, clickbait, false / misleading, witnessing-hate, and violence issues.
BlinkViz: Fast and Scalable Approximate Visualization on Very Large Datasets using Neural-Enhanced Mixed Sum-Product Networks
Authors: Yimeng Qiao, Yinan Jing, Hanbing Zhang, Zhenying He, Kai Zhang, X. Sean Wang
Venue: WWW
Year: 2023

Web-based online interactive visual analytics enjoys popularity in recent years. Traditionally, visualizations are produced directly from querying the underlying data. However, for a very large dataset, this way is so time-consuming that it cannot meet the low-latency requirements of interactive visual analytics. In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. In such a way, it makes visualization faster and more scalable by decoupling visualization and data. In addition, to improve the accuracy of approximate visualizations, we propose an enhanced model by incorporating a neural network with residual structures, which can refine prediction results, especially for visual requests with low selectivity. Extensive experiments show that BlinkViz is extremely fast even on a large dataset with hundreds of millions of data records (over 30GB), responding in sub-seconds (from 2ms to less than 500ms for different requests) while keeping a low error rate. Furthermore, our approach remains scalable on latency and memory footprint size regardless of data size.