OpenRaiser/Intern-Atlas huggingface dataset:
The First International Workshop on Semantics in Dataspaces
Authors: Johannes Theissen-Lipp, Stefan Decker, Edward Curry
Venue: WWW
Year: 2023

In recent years, several dataspace initiatives were formed and are growing in both implementation and memberships. Dozens of individual dataspaces and use-cases are emerging, as listed in the International Data Spaces Association’s "Data Space Radar", Gaia-X, and even more from other initiatives. The European Union is driving this trend even further by including dataspaces as key elements of its data strategy and spending €4-6 billion to establish them in various domains. The main challenges of this trend for dataspaces are twofold. First, there is no common defnition of dataspaces and their core components, making interoperability and FAIRness difcult even at a conceptual level. This likely produces isolated solutions that are tailored to specifc use-cases. Second, the various dataspaces that emerging in the wild have diferent goals, individual implementations, and technology readiness levels. The lack of reusable core components forces stakeholders to reinvent them, leading to divergent implementations and therefore high costs. To overcome these challenges, there has been a growing interest in using semantics-based approaches in dataspaces. These approaches leverage the rich expressiveness of semantic technologies to provide a common vocabulary for representing and integrating data from diferent sources. Although implementations and scientifc notions follow diferent interpretations of the term dataspace, they all agree on some use of semantic technologies. The First International Workshop on Semantics in Dataspaces brings together people interested in dataspaces or the role of semantics in them, from diferent communities such as dataspace drivers, legal and policy makers, as well as the researchers from the Semantic Web community. Since members of these diferent communities rarely have the opportunity to interact with each other, we design the workshop to be highly interactive and communicative. This full-day workshop consists of about 2/3 formal presentations and 1/3 Open Space collaboration. This approach allows participants to
Semantics in Dataspaces: Origin and Future Directions
Authors: Johannes Theissen-Lipp, Max Kocher, Christoph Lange, Stefan Decker, Alexander Paulus, André Pomp, Edward Curry
Venue: WWW
Year: 2023

The term dataspace was coined two decades ago [12] and has evolved since then. Definitions range from (i) an abstraction for data management in an identifiable scope [15] over (iii) a multi-sided data platform connecting participants in an ecosystem [21] to (iii) interlinking data towards loosely connected (global) information [17]. Many implementations and scientific notions follow different interpretations of the term dataspace, but agree on some use of semantic technologies. For example, dataspaces such as the European Open Science Cloud and the German National Research Data Infrastructure are committed to applying the FAIR principles [11, 16]. Dataspaces built on top of Gaia-X are using semantic methods for service Self-Descriptions [13]. This paper investigates ongoing dataspace efforts and aims to provide insights on the definition of the term dataspace, the usage of semantics and FAIR principles, and future directions for the role of semantics in dataspaces.
Decency and Decentralisation: Verifiable Decentralised Knowledge Graph Querying
Authors: Aisling Third, John Domingue
Venue: WWW
Year: 2023

Increasing interest in decentralisation for data and processing on the Web brings with it the need to re-examine methods for verifying data and behaviour for scalable multi-party interactions. We consider factors relevant to verification of querying activity on knowledge graphs in a Trusted Decentralised Web, and set out ideas for future research in this area.
BeyondFacts'23: 3rd International Workshop on Knowledge Graphs for Online Discourse Analysis
Authors: Konstantin Todorov, Pavlos Fafalios, Dimitar Dimitrov, Stefan Dietze
Venue: WWW
Year: 2023

WORKSHOP SCOPE Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misand dis-information spread, bias reinforcement, echo chambers, or political agenda setting. While knowledge graphs (KGs) promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understanding strongly diverge across communities from computational social science and journalism, to argumentation mining, stance detection, or computational fact-checking. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientifc or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for KG building, the workshop
Search-based Recommendation: the Case for Difficult Predictions
Authors: Ghazaleh Haratinezhad Torbati, Gerhard Weikum, Andrew Yates
Venue: WWW
Year: 2023

Recommender systems have achieved impressive results on benchmark datasets. However, the numbers are often influenced by assumptions made on the data and evaluation mode. This work questions and revises these assumptions, to study and improve the quality, particularly for the difficult case of search-based recommendations. Users start with a personally liked item as a query and look for similar items that match their tastes. User satisfaction requires discovering truly unknown items: new authors of books rather than merely more books of known writers. We propose a unified system architecture that combines interaction-based and content-based signals and leverages language models for Transformer-powered predictions. We present new techniques for selecting negative training samples, and investigate their performance in the underexplored search-based evaluation mode.
Misbehavior and Account Suspension in an Online Financial Communication Platform
Authors: Taro Tsuchiya, Alejandro Cuevas Villalba, Thomas Magelinski, Nicolas Christin
Venue: WWW
Year: 2023

The expanding accessibility and appeal of investing have attracted millions of new retail investors. As such, investment discussion boards became the de facto communities where traders create, disseminate, and discuss investing ideas. These communities, which can provide useful information to support investors, have anecdotally also attracted a wide range of misbehavior – toxicity, spam/fraud, and reputation manipulation. This paper is the first comprehensive analysis of online misbehavior in the context of investment communities. We study TradingView, the largest online communication platform for financial trading. We collect 2.76M user profiles with their corresponding social graphs, 4.2M historical article posts, and 5.3M comments, including information on nearly 4 000 suspended accounts and 17 000 removed comments. Price fluctuations seem to drive abuse across the platform and certain types of assets, such as “meme” stocks, attract disproportionate misbehavior. Suspended user accounts tend to form more closely-knit communities than those formed by non-suspended accounts; and paying accounts are less likely to be suspended than free accounts even when posting similar levels of content violating platform policies. We conclude by offering guidelines on how to adapt content moderation efforts to fit the particularities of online investment communities.
Improve retrieval of regional titles in streaming services with dense retrieval
Authors: Bhargav Upadhyay, Tejas Khairnar, Anup Kotalwar
Venue: WWW
Year: 2023

Customers search for movie and series titles released across the world on streaming services like primevideo.com (PV), netflix.com (Netflix). In non-English speaking countries like India, Nepal and many others, the regional titles are transliterated from native language to English and are being searched in English. Given that there can be multiple transliterations possible for almost all the titles, searching for a regional title can be a very frustrating customer experience if these nuances are not handled correctly by the search system. Typing errors make the problem even more challenging. Streaming services uses spell correction and auto-suggestions/auto-complete features to address this issue up to certain extent. Auto-suggest fails when user searches keywords not in scope of the auto-suggest. Spell correction is effective at correcting common typing errors but as these titles doesn’t follow strict grammar rules and new titles constantly added to the catalog, spell correction have limited success. With recent progress in deep learning (DL), embedding vectors based dense retrieval is being used extensively to retrieve semantically relevant documents for a given query. In this work, we have used dense retrieval to address the noise introduced by transliteration variations and typing errors to improve retrieval of regional media titles. In the absent of any relevant dataset to test our hypothesis, we created a new dataset of 40K query title pairs from PV search logs. We also created a baseline by bench-marking PV’s performance on test data. We present an extensive study on the impact of 1. pre-training, 2. data augmentation, 3. positive to negative sample ratio, and 4. choice of loss function on retrieval performance. Our best model has shown 51.24% improvement in Recall@16 over PV baseline.
Towards Multimodal Knowledge Graphs for Data Spaces
Authors: Atiya Usmani, Muhammad Jaleed Khan, John G. Breslin, Edward Curry
Venue: WWW
Year: 2023

Multimodal knowledge graphs have the potential to enhance data spaces by providing a unified and semantically grounded structured representation of multimodal data produced by multiple sources. With the ability to integrate and analyze data in real-time, multimodal knowledge graphs offer a wealth of insights for smart city applications, such as monitoring traffic flow, air quality, public safety, and identifying potential hazards. Knowledge enrichment can enable a more comprehensive representation of multimodal data and intuitive decision-making with improved expressiveness and generalizability. However, challenges remain in effectively modelling the complex relationships between and within different types of modalities in data spaces and infusing common sense knowledge from external sources. This paper reviews the related literature and identifies major challenges and key requirements for effectively developing multimodal knowledge graphs for data spaces, and proposes an ontology for their construction.
NLP4KGC: Natural Language Processing for Knowledge Graph Construction
Authors: Edlira Vakaj, Sanju Tiwari, Nandana Mihindukulasooriya, Fernando Ortiz-Rodríguez, Ryan McGranaghan
Venue: WWW
Year: 2023

Welcome to the 1st NLP4KGC: Natural Language Processing for Knowledge Graph Construction Workshop! This event brings together experts and enthusiasts from both natural language processing (NLP) and knowledge graph (KG) creation felds. The main goal of this workshop is to foster collaboration and knowledge sharing among researchers, developers, and practitioners working on the cutting edge of NLP and KG creation.
Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics
Authors: Aparna S. Varde, Gerard de Melo, Boxiang Dong
Venue: WWW
Year: 2023

As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in decision-making and enabling greater public involvement. The thriving concept of smart governance goes beyond just cities, ultimately aiming at a smart planet. Ordinances (local laws) affect our life with regard to health, business, etc. This is particularly notable during major events such as the recent pandemic, which may lead to rapid changes in ordinances, pertaining for instance to public safety, disaster management, and recovery phases. However, many citizens view ordinances as impervious and complex. This position paper proposes a research agenda enabling novel forms of ordinance content analysis over time and temporal web question answering (QA) for both legislators and the broader public. Along with this, we aim to analyze social media posts so as to track the public opinion before and after the introduction of ordinances. Challenges include addressing concepts changing over time and infusing subtle human reasoning in mining, which we aim to address by harnessing terminology evolution methods and commonsense knowledge sources, respectively. We aim to make the results of the historical ordinance mining and event-driven analysis seamlessly accessible, relying on a robust semantic understanding framework to flexibly support web QA.
GNNs and Graph Generative models for biomedical applications
Authors: Michalis Vazirgiannis
Venue: WWW
Year: 2023

Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. In this talk we will present the potential of graph generative models and our recent relevant efforts in the biomedical domain. More specifically we present a novel architecture that generates medical records as graphs with privacy guarantees. We capitalize and modify the graph Variational autoencoders (VAEs) architecture. We train the generative model with the well known MIMIC medical database and achieve generated data that are very similar to the real ones yet provide privacy guarantees. We also develop new GNNs for predicting antibiotic resistance and other protein related downstream tasks such as enzymes classifications and Gene Ontology classification. We achieve there as well promising results with potential for future application in broader biomedical related tasks. Finally we present future research directions for multi modal generative models involving graphs.
Cut-matching Games for Generalized Hypergraph Ratio Cuts
Authors: Nate Veldt
Venue: WWW
Year: 2023

Many social networks and web-based datasets are characterized by multiway interactions (e.g., groups of co-purchased online retail products or group conversations in Q&A forums) and hypergraph clustering is a fundamental primitive for analyzing these types of interactions. We present an O(log n)-approximation algorithm for a broad class of hypergraph ratio cut objectives. This includes objectives involving generalized hypergraph cut functions, which allow a user to penalize cut hyperedges differently depending on the number of nodes in each cluster. Our method generalizes the cut-matching framework for graph ratio cuts, and relies only on solving maximum s-t flow problems in a special reduced graph. It is significantly faster than existing hypergraph ratio cut algorithms, while also solving a more general problem. In numerical experiments on various web-based hypergraphs, we show that it quickly finds ratio cut solutions within a small factor of optimality.
Scientific Data Extraction from Oceanographic Papers
Authors: Bartal Eyðfinsson Veyhe, Tomer Sagi, Katja Hose
Venue: WWW
Year: 2023

Scientific data collected in the oceanographic domain is invaluable to researchers when performing meta-analyses and examining changes over time in oceanic environments. However, many of the data samples and subsequent analyses published by researchers are not uploaded to a repository leaving the scientific paper as the only available source. Automated extraction of scientific data is, therefore, a valuable tool for such researchers. Specifically, much of the most valuable data in scientific papers are structured as tables, making these a prime target for information extraction research. Using the data relies on an additional step where the concepts mentioned in the tables, such as names of measures, units, and biological species, are identified within a domain ontology. Unfortunately, state-of-the-art table extraction leaves much to be desired and has not been attempted on a large scale on oceanographic papers. Furthermore, while entity linking in the context of a full paragraph of text has been heavily researched, it is still lacking in this harder task of linking single concepts. In this work, we present an annotated benchmark dataset of data tables from oceanographic papers. We further present the result of an evaluation on the extraction of these tables and the linking of the contained entities to the domain and general-purpose knowledge bases using the current state of the art. We highlight the challenges and quantify the performance of current tools for table extraction and table-concept linking.
Exploiting graph metrics to detect anomalies in cross-country money transfer temporal networks
Authors: Salvatore Vilella, Arthur Thomas Edward Capozzi Lupi, Giancarlo Ruffo, Marco Fornasiero, Dario Moncalvo, Valeria Ricci, Silvia Ronchiadin
Venue: WWW
Year: 2023

During the last decades, Anti-Financial Crime (AFC) entities and Financial Institutions have put a constantly increasing effort to reduce financial crime and detect fraudulent activities, that are changing and developing in extremely complex ways. We propose an anomaly detection approach based on network analysis to help AFC officers navigating through the high load of information that is typical of AFC data-driven scenarios. By experimenting on a large financial dataset of more than 80M cross-country wire transfers, we leverage on the properties of complex networks to develop a tool for explainable anomaly detection, that can help in identifying outliers that could be engaged in potentially malicious activities according to financial regulations. We identify a set of network metrics that provide useful insights on individual nodes; by keeping track of the evolution over time of the metric-based node rankings, we are able to highlight sudden and unexpected changes in the roles of individual nodes that deserve further attention by AFC officers. Such changes can hardly be noticed by means of current AFC practices, that sometimes can lack a higher-level, global vision of the system. This approach represents a preliminary step in the automation of AFC and AML processes, serving the purpose of facilitating the work of AFC officers by providing them with a top-down view of the picture emerging from financial data.
How Streaming Can Improve the World (Wide Web)
Authors: Lucas Vogel, Thomas Springer
Venue: WWW
Year: 2023

Since its beginnings, web pages have been based on files. This means that HTML, CSS, and JavaScript are transferred from server to client as files, which by default need to be fully loaded before the web page is displayed. This render-blocking procedure increases loading times significantly, leading to reduced user satisfaction and revenue loss due to lower conversion rates. We present a full implementation of a new approach for loading web pages by splitting up every component and loading the page via a text-based stream. Such a modification aligns with current trends of the HTTP protocol, which has been using streams internally since HTTP/2. It significantly improves loading times, independent of the total page size.
Wikidata: The Making Of
Authors: Denny Vrandecic, Lydia Pintscher, Markus Krötzsch
Venue: WWW
Year: 2023

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.
Self-Supervised Teaching and Learning of Representations on Graphs
Authors: Liangtian Wan, Zhenqiang Fu, Lu Sun, Xianpeng Wang, Gang Xu, Xiaoran Yan, Feng Xia
Venue: WWW
Year: 2023

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.
Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters
Authors: Liangtian Wan, Xiaona Li, Huijin Han, Xiaoran Yan, Lu Sun, Zhaolong Ning, Feng Xia
Venue: WWW
Year: 2023

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.