OpenRaiser/Intern-Atlas huggingface dataset:
Tweet-aware News Summarization with Dual-Attention Mechanism
Authors: Xin Zheng, Aixin Sun, Karthik Muthuswamy
Venue: WWW
Year: 2021

Neural models have been applied to many text summarization tasks recently. In general, a large number of high quality reference summaries are required to train well-performing neural models. The reference summaries, i.e., ground truth, are usually written by human, and are costly to obtain. Thus, in this paper, we focus on unsupervised summarization problem by exploring news and readers’ comments in linking tweets, i.e., tweets with URLs linking to the news. Our data analysis shows that the linking tweets, collectively highlight important information in news but may not fully cover all content in news. This inspires us to propose the dual-attention based model, named DAS, to address the observed issues above. The dual-attention mechanism extracts both important information highlighted by linking tweets and the salient content in news. Specifically, it consists of two similar structures of Transformer with multi-head attention. We propose position-dependent word salience, which reflects the effect of local context. The word salience is computed from dual-attention mechanism. Sentence salience is then estimated from the word salience. Experimental results on a benchmark dataset show that DAS outperforms state-of-the-art unsupervised models and achieves comparable results with state-of-the-art supervised models.
Attent: Active Attributed Network Alignment
Authors: Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, Hanghang Tong
Venue: WWW
Year: 2021

Network alignment finds node correspondences across multiple networks, where the alignment accuracy is of crucial importance because of its profound impact on downstream applications. The vast majority of existing works focus on how to best utilize the topology and attribute information of the input networks as well as the anchor links when available. Nonetheless, it has not been well studied on how to boost the alignment performance through actively obtaining high-quality and informative anchor links, with a few exceptions. The sparse literature on active network alignment introduces the human in the loop to label some seed node correspondence (i.e., anchor links), which are informative from the perspective of querying the most uncertain node given few potential matchings. However, the direct influence of the intrinsic network attribute information on the alignment results has largely remained unknown. In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. The key idea of the proposed method is to leverage effective and efficient influence functions defined over the alignment solution to evaluate the goodness of the candidate nodes for query. Our proposed query strategy bears three distinct advantages, including (1) effectiveness, being able to accurately quantify the influence of the candidate nodes on the alignment results; (2) efficiency, scaling linearly with 15 − 17 × speed-up over the straight-forward implementation without any quality loss; (3) generality, consistently improving alignment performance of a variety of network alignment algorithms.
How to See Smells: Extracting Olfactory References from Artworks
Authors: Mathias Zinnen
Venue: WWW
Year: 2021

1 PROBLEM Although being an essential part of how we experience the world, smell is severely undervalued in the context of cultural heritage. The Odeuropa project aims at preserving and recreating the olfactory heritage of Europe. State-of-the-art methods of artificial intelligence are applied to large corpora of visual and textual data ranging from the 16th to 20th century of European history to extract olfactory references. Creating an ontology of smells, this information is stored in the “European Olfactory Knowledge Graph (EOKG)” following standards of the semantic web. My Ph.D. addresses the visual extraction part of the project. We will create a taxonomy of visual smell references and acquire a large corpus of artworks from various early modern European digital collections. Using computer vision techniques, we will implement a pipeline for the combined recognition of olfactory objects, poses, and iconographies and annotate the images from our image corpus accordingly. Following these steps, we will address the following research questions: (i)What visual representations of smell exist in European 16th to 20th century works of art and how can these be represented in the EOKG as an ontology shared with the other work packages of the Odeuropa project? (ii)Whichmachine-learning techniques exist for the automated extraction of olfactory references in the visual arts? Particularly, which techniques are suited to cope with the domain shift problem when applying computer vision techniques to our field of research? (iii) How do the identified techniques perform in terms of established evaluation metrics? Which ones work best for the extraction of olfactory references? Both the preservation of olfactory heritage [3] and the application of machine learning (ML) to cultural heritage [1] have been addressed before. However, in most cases machine learning algorithms are treated as “black boxes” and their application does not contribute back to ML [4]. Computer vision techniques like object detection and pose estimation have successfully been applied to the domain of visual arts ([8], [2]) but have not achieved performance comparable to their application in the photographic domain. One reason for the success of computer vision on photographs is the availability of huge labeled datasets like ImageNet [10]. Datasets containing artworks
Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding
Authors: Can Wang, Sheng Zhou, Kang Yu, Defang Chen, Bolang Li, Yan Feng, Chun Chen
Venue: WWW
Year: 2022

Learning low-dimensional representations for Heterogeneous Information Networks (HINs) has drawn increasing attention recently for its effectiveness in real-world applications. Compared with homogeneous networks, HINs are characterized by meta-paths connecting different types of nodes with semantic meanings. Existing methods mainly follow the prototype of independently learning meta-path-based embeddings and integrating them into a unified embedding. However, meta-paths in a HIN are inherently correlated since they reflect different perspectives of the same object. If each meta-path is treated as an isolated semantic data resource and the correlations among them are disregarded, sub-optimality in the both the meta-path based embedding and final embedding will be resulted. To address this issue, we make the first attempt to explicitly model the correlation among meta-paths by proposing Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding (CKD). More specifically, we model the knowledge in each meta-path with two different granularities: regional knowledge and global knowledge. We learn the meta-path-based embeddings by collaboratively distill the knowledge from intra-meta-path and inter-meta-path simultaneously. Experiments conducted on six real-world HIN datasets demonstrates the effectiveness of the CKD method.
Making Decision like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning
Authors: Hao Fei, Jingye Li, Yafeng Ren, Meishan Zhang, Donghong Ji
Venue: WWW
Year: 2022

Joint aspect category sentiment analysis (ACSA) and rating prediction (RP) is a newly proposed task (namely ASAP) that integrates the characteristics of both fine-grained and coarse-grained sentiment analysis. However, the prior joint models for the ASAP task only consider the shallow interaction between the two granularities. In this work, we gain the inspiration from human intuition, presenting an innovative from-fine-to-coarse reasoning framework for better joint task performance. Our system advances mainly in three aspects. First, we additionally make use of the category label text features, co-encoding them with the input document texts, allowing to accurately capture the key clues of each category. Second, we build a fine-to-coarse hierarchical label graph, modeling the aspect categories and the overall rating as a hierarchical structure for full interaction of the two granularities. Third, we propose to perform global iterative reasoning with a cross-collaboration between the hierarchical label graph and the context graphs, enabling sufficient communication between categories and review contexts. Based on the ASAP dataset, experimental results demonstrate that our proposed framework outperforms state-of-the-art baselines by large margins. Further in-depth analyses prove that our method is effective on addressing both the unbalanced data distribution and the long-text issue.
Accepted Tutorials at The Web Conference 2022
Authors: Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lyu, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He
Venue: WWW
Year: 2022

This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.
COCTEAU: an Empathy-Based Tool for Decision-Making
Authors: Andrea Mauri, Andrea Tocchetti, Lorenzo Corti, Yen-Chia Hsu, Himanshu Verma, Marco Brambilla
Venue: WWW
Year: 2022

Traditional approaches to data-informed policymaking are often tailored to specific contexts and lack strong citizen involvement and collaboration, which are required to design sustainable policies. We argue the importance of empathy-based methods in the policymaking domain given the successes in diverse settings, such as healthcare and education. In this paper, we introduce COCTEAU (Co-Creating The European Union), a novel framework built on the combination of empathy and gamification to create a tool aimed at strengthening interactions between citizens and policy-makers. We describe our design process and our concrete implementation, which has already undergone preliminary assessments with different stakeholders. Moreover, we briefly report pilot results from the assessment. Finally, we describe the structure and goals of our demonstration regarding the newfound formats and organizational aspects of academic conferences.
Adaptively Offloading the Software for Mobile Edge Computing
Authors: Xing Chen
Venue: WWW
Year: 2022

In mobile edge computing (MEC), computation offloading is a promising way to support those resource-constrained mobile devices, since it moves some time-consuming computation activities to nearby edge servers. Owing to the geographical distribution of edge servers and mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic, so it is challenging to efficiently support computation offloading in MEC. In the face of a highly complex and dynamic runtime environment, an “adaptive” model has become the inevitable demand for computation offloading in MEC, in which software perceives the environment, changes behavior and improves performance. However, it involves two main challenges: (1) Adaptability: The software often faces changes of runtime environment in MEC, so that the adaptation on offloading is needed. However, due to the inherent architecture of software, it is hard to utilize the dispersed computing resources and change the offloading scheme. (2) Effectiveness: When the environment changes, it needs to calculate which parts are worth offloading, and the reduced execution time must be greater than the network delay caused by computation offloading. In addition, the decision-making of offloading is expected to be made at runtime. This report introduces our research on adaptively offloading the software for MEC. First, the code of software can be automatically refactored to implement a special program structure supporting dynamic offloading in MEC [2, 3]. Second, the execution costs of offloading schemes can be accurately estimated without runtime execution [1, 3]. Third, new offloading strategies are proposed to reduce decision time to an acceptable level [4].
Geotagging TweetsCOV19: Enriching a COVID-19 Twitter Discourse Knowledge Base with Geographic Information
Authors: Dimitar Dimitrov, Dennis Segeth, Stefan Dietze
Venue: WWW
Year: 2022

Various aspects of the recent COVID-19 outbreak have been extensively discussed on online social media platforms and, in particular, on Twitter. Geotagging COVID-19-related discourse data on Twitter is essential for understanding the different discourse facets and their regional relevance, including calls for social distancing, acceptance of measures implemented to contain virus spread, anti-vaccination campaigns, and misinformation. In this paper, we aim at enriching TweetsCOV19—a large COVID-19 discourse knowledge base of more than 20 million tweets—with geographic information. For this purpose, we evaluate two state-of-the-art Geotagging algorithms: (1) DeepGeo—predicting the tweet location and (2) GeoLocation—predicting the user location. We compare pre-trained models with models trained on context-specific ground truth geolocation data extracted from TweetsCOV19. Models trained on our context-specific data achieve more than 6.7% improvement in Acc@25 compared to the pre-trained models. Further, our results show that DeepGeo outperforms GeoLocation and that longer tweets are, in general, easier to geotag. Finally, we use the two geotagging methods to study the distribution of tweets per country in TweetsCOV19 and compare the geographic coverage, i.e., the number of countries and cities each algorithm can detect.
Fraship: A Framework to Support End-User Personalization of Smart Home Services with Runtime Knowledge Graph
Authors: Zhiming Huang, Jiawen Chen, Liwei Shen, Xing Chen
Venue: WWW
Year: 2022

With the continuous popularization of smart home devices, people often anticipate using different smart devices through natural language instructions and require personalized smart home services. However, existing challenges include the interoperability of smart devices and a comprehensive understanding of the user environment. This study proposes Fraship, a framework supporting smart home service personalization for end-users. It incorporates a runtime knowledge graph acting as a bridge between users’ language instructions and the corresponding operations of smart devices. The runtime knowledge graph is used to reflect contextual information in a specific smart home, based on which a language-instruction parser is proposed to allow users to manage smart home devices and services in natural language. We evaluated Fraship on a real-world smart home. Our results show that Fraship can effectively manage smart home devices and services based on the runtime knowledge graph, and it recognizes instructions more accurately than other approaches.
Exploiting Anomalous Structural Nodes in Dynamic Social Networks
Authors: Huan Wang, Chunming Qiao, Xuan Guo, Ying Sha, Zhiguo Gong
Venue: WWW
Year: 2022

As a long-lasting challenge in dynamic social networks, anomaly analysis research has attracted much attention. Unfortunately, existing methods focus on the macro representation of dynamic social networks and fail to analyze the micro-level nodes. Therefore, this research proposes a multiple-neighbor fluctuation method to exploit anomalous structural nodes in dynamic social networks. Our method proposes a new multiple-neighbor similarity index by incorporating extensional similarity indices, which introduces observation nodes and characterizes the structural similarities of nodes within multiple-neighbor ranges. Subsequently, our method maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, our method not only identifies anomalous structural nodes by detecting the anomalous structural changes of nodes, but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that our method can accurately identify anomalous structural nodes and evaluate their anomalous degrees well.
Mining Multivariate Implicit Relationships in Academic Networks
Authors: Bo Xu, Bowen Chen, Tianyu Zhang, Jiaying Liu, Chunke Liao, Zhehuan Zhao
Venue: WWW
Year: 2022

Multivariate cooperative relations exist widely in the academic society. In-depth research on multivariate relationships can effectively promote the integration of disciplines and advance scientific and technological progress. The mining and analysis of advisor-advisee relationships in the cooperative network, as a hot research issue in sociology and other disciplines, is still facing various challenges such as the lack of universal models and the difficulty in identifying multivariate relationships. The traditional advisor-advisee relationship mining methods only focus on the binary relationship, and require secondary processing of node attributes and edge attributes. Therefore, based on the attributes of the node, edge, and network, we transfered the Capsule Network to multivariate relation analysis. The experimental results proved the simplicity and reliability of this model. And we studied the effects of the network feature vectors’ dimension, routing iterations, and normalization on the performance of the Capsule Network. Considering that Capsule Network takes a long training time, we adopted Warm Restarts method to speed up the training process. In addition, we also used the model to generate a large-scale multivariate academic genealogy.
User Access Models to Event-Centric Information
Authors: Sara Abdollahi
Venue: WWW
Year: 2022

Events such as terrorist attacks and Brexit play an important role in the research of social scientists and Digital Humanities researchers. They need innovative user access models to event-centric information which support them throughout their research. Current access models such as recommendation and information retrieval methods often fail to adequately capture essential features of events and provide acceptable access to event-centric information. This PhD research aims to develop efficient and effective user access models to event-centric information by leveraging well-structured information in Knowledge Graphs. The goal is to tackle the challenges researchers encounter during their research in a workflow, from exploratory search to accessing well-defined and complete information collections. This paper presents the specific research questions and presents the approach and preliminary results.
XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
Authors: Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, Vasudeva Varma
Venue: WWW
Year: 2022

Multiple critical scenarios need automated generation of descriptive text in low-resource (LR) languages given English fact triples. For example, Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc. Previous work on fact-to-text (F2T) generation has focused on English only. Building an effective cross-lingual F2T (XF2T) system requires alignment between English structured facts and LR sentences. Either we need to manually obtain such alignment data at a large scale, which is expensive, or build automated models for cross-lingual alignment. To the best of our knowledge, there has been no previous attempt on automated cross-lingual alignment or generation for LR languages. We propose two unsupervised methods for cross-lingual alignment. We contribute XAlign, an XF2T dataset with 0.45M pairs across 8 languages, of which 5402 pairs have been manually annotated. We also train strong baseline XF2T generation models on XAlign. We make our code and dataset publicly available1, and hope that this will help advance further research in this critical area.
Improving Bundles Recommendation Coverage in Sparse Product Graphs
Authors: Saloni Agarwal, Aparupa Das Gupta, Amit Pande
Venue: WWW
Year: 2022

In e-commerce, a group of similar or complementary products is recommended as a bundle based on the product category. Existing work on modeling bundle recommendations consists of graph-based approaches. In these methods, user-product interactions provide a more personalized experience. Moreover, these approaches require robust user-product interactions and cannot be applied to cold start scenarios. When a new product is launched or for products with limited purchase history, the lack of user-product interactions will render these algorithms inaccessible. Hence, no bundles recommendations will be provided to users for such product categories. These scenarios are frequent for retailers like Target, where much of the stock is seasonal, and new brands are launched throughout the year. This work alleviates this problem by modeling product bundles recommendation as a supervised graph link prediction problem. A graph neural network (GNN) based product bundles recommendation system, BundlesSEAL is presented. First, we build a graph using add-to-cart data and then use BundlesSEAL to predict the link representing bundles relation between products represented as nodes. We also propose a heuristic to identify relevant pairs of products for efficient inference. Further, we also apply BundlesSEAL for predicting the edge weights instead of just link existence. BundlesSEAL based link prediction leads to amelioration of the above-mentioned cold start problem by increasing the coverage of product bundles recommendations in various categories by 50% while achieving a 35% increase in revenue over behavioral baseline. The model was also validated over the Amazon product metadata dataset.
Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp
Authors: Pushkal Agarwal, Aravindh Raman, Damilola Ibosiola, Nishanth Sastry, Gareth Tyson, Kiran Garimella
Venue: WWW
Year: 2022

WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, understanding misbehavior on WhatsApp is an important issue. The sending of unwanted junk messages by unknown contacts via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. We address this gap by studying junk messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp groups in India. We characterise both junk content and senders. We find that nearly 1 in 10 messages is unwanted content sent by junk senders, and a number of unique strategies are employed to reflect challenges faced on WhatsApp, e.g., the need to change phone numbers regularly. We finally experiment with on-device classification to automate the detection of junk, whilst respecting end-to-end encryption.