OpenRaiser/Intern-Atlas huggingface dataset:
PRSP: A Plugin-based Framework for RDF Stream Processing
Authors: Qiong Li, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2017

In this paper, we propose a plugin-based framework for RDF stream processing (PRSP). With this framework, we can apply SPARQL engines to process C-SPARQL queries with maintaining the high performance of those engines in a simple way. Taking advantage of PRSP, we can process large RDF streams in a distributed context via distributed SPARQL engines. Moreover, we can evaluate the performance and correctness of existing SPARQL engines in handling RDF streams in a united way, which amends the evaluation of them ranging from static RDF to dynamic RDF. Finally, we experimentally evaluate the performance and correctness of PRSP on YABench. The experiments show that PRSP can still maintain the high performance with SPARQL engines in RDF stream processing although there are some slight differences among them.
CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems
Authors: Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun
Venue: WWW
Year: 2017

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.
Changing News Media Landscape in South Korea
Authors: Hongjun Lim, Choongho Chung, Ji Hee Kim, Juho Kim, Sue B. Moon, Meeyoung Cha
Venue: WWW
Year: 2017

A recent disclosure of a presidential scandal and the following development of subsequent events have been a major news topic in South Korea. We conducted a data-driven study to examine public reactions to the scandal and their effect on the Korean media landscape. Our analysis is based on 59,224 news articles published by five popular newsrooms that received a total of 47,906,770 Likes on Facebook. The data reveal notable changes in media ranks throughout the scandal, where a relatively young TV news network outgrew its audience over other half-a-century old established newspapers in less than a month. Topical similarity of news headlines also remained high over an extended period of time, despite the varying political stance of each newsroom. The topical similarity further shows a gradual divergence, indicating re-positioning of media stance takes place over time. Implications of these findings and suggestions for future directions are discussed based on data analysis of this extraordinary event.
Assessing Percolation Threshold Based on High-Order Non-Backtracking Matrices
Authors: Yuan Lin, Wei Chen, Zhongzhi Zhang
Venue: WWW
Year: 2017

Percolation threshold of a network is the critical value such that when nodes or edges are randomly selected with probability below the value, the network is fragmented but when the probability is above the value, a giant component connecting a large portion of the network would emerge. Assessing the percolation threshold of networks has wide applications in network reliability, information spread, epidemic control, etc. The theoretical approach so far to assess the percolation threshold is mainly based on spectral radius of adjacency matrix or non-backtracking matrix, which is limited to dense graphs or locally treelike graphs, and is less effective for sparse networks with non-negligible amount of triangles and loops. In this paper, we study high-order non-backtracking matrices and their application to assessing percolation threshold. We first define high-order non-backtracking matrices and study the properties of their spectral radii. Then we focus on the 2nd-order non-backtracking matrix and demonstrate analytically that the reciprocal of its spectral radius gives a tighter lower bound than those of adjacency and standard non-backtracking matrices. We further build a smaller size matrix with the same largest eigenvalue as the 2nd-order non-backtracking matrix to improve computation efficiency. Finally, we use both synthetic networks and 42 real networks to illustrate that the use of the 2nd-order non-backtracking matrix does give better lower bound for assessing percolation threshold than adjacency and standard non-backtracking matrices.
SpiderX: Fast XML Exploration System
Authors: Chunbin Lin, Jianguo Wang
Venue: WWW
Year: 2017

Keyword search in XML has gained popularity as it enables users to easily access XML data without the need of learning query languages and studying complex data schemas. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA), e.g., SLCA and ELCA. However, LCA-based search methods depend heavily on hierarchical structures of XML data, which may result in meaningless answers. To obtain desired answers, a successful system should be able to (i) match a semantic entity for each keyword, (ii) discover the relationships of the matched entities, (iii) support efficient query processing, (iv) release users from having the knowledge of the XML content, and (v) visualize the search results. None of the existing XML keyword search systems completely meet the above requirements.
Location-sensitive Query Auto-completion
Authors: Chunbin Lin, Jianguo Wang, Jiaheng Lu
Venue: WWW
Year: 2017

This paper studies the location-sensitive auto-completion problem. We propose an efficient algorithm SQA running on a native index combining both IR-tree and Trie index. The experiments on real-life datasets demonstrate that SQA outperforms baseline methods by one order of magnitude.
Legion: Enriching Internet Services with Peer-to-Peer Interactions
Authors: Albert van der Linde, Pedro Fouto, João Leitão, Nuno M. Preguiça, Santiago J. Castiñeira, Annette Bieniusa
Venue: WWW
Year: 2017

Many web applications are built around direct interactions among users, from collaborative applications and social networks to multi-user games. Despite being user-centric, these applications are usually supported by services running on servers that mediate all interactions among clients. When users are in close vicinity of each other, relying on a centralized infrastructure for mediating user interactions leads to unnecessarily high latency while hampering fault-tolerance and scalability.
Constructing Knowledge Nets from Real-world Natural Language Texts
Authors: Wei Liu
Venue: WWW
Year: 2017

Mining knowledge from textual data has traditionally been applied on web-based publicly available resources, such as Wikipedia, online news, scientific publications and social medial such as Facebook and Twitter. In recent years, the increased maturity and accessibility of natural language processing and text mining algorithms have now raised serious interests beyond academia to uncover the chunk of corporate knowledge buried in hard-to-process natural language descriptions and reports. Such textual data are often in larger volumes and contain more valuable than numerical data.
Distributed Machine Learning: Foundations, Trends, and Practices
Authors: Tie-Yan Liu, Wei Chen, Taifeng Wang
Venue: WWW
Year: 2017

In recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play very critical roles in the success. In this tutorial, we will first review popular machine learning algorithms and the optimization techniques they use. Second, we will introduce widely used ways of parallelizing machine learning algorithms (including both data parallelism and model parallelism, both synchronous and asynchronous parallelization), and discuss their theoretical properties, strengths, and weakness. Third, we will present some recent works that try to improve standard parallelization mechanisms. Last, we will provide some practical examples of parallelizing given machine learning algorithms in online application (e.g. Recommendation and Ranking) by using popular distributed platforms, such as Spark MlLib, DMTK, and Tensorflow. By listening to this tutorial, the audience can form a clear knowledge framework about distributed machine learning, and gain some hands-on experiences on parallelizing a given machine learning algorithm using popular distributed systems.
TruthDiscover: Resolving Object Conflicts on Massive Linked Data
Authors: Wenqiang Liu, Jun Liu, Haimeng Duan, Jian Zhang, Wei Hu, Bifan Wei
Venue: WWW
Year: 2017

Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existing methods cannot be trivially extended to resolve conflicts in Linked Data because Linked Data has a scale-free property. In this demonstration, we present a novel system called TruthDiscover, to identify the truth in Linked Data with a scale-free property. First, TruthDiscover leverages the topological properties of the Source Belief Graph to estimate the priori beliefs of sources, which are utilized to smooth the trustworthiness of sources. Second, the Hidden Markov Random Field is utilized to model interdependencies among objects for estimating the trust values of objects accurately. TruthDiscover can visualize the process of resolving conflicts in Linked Data.
Numerical Facet Range Partition: Evaluation Metric and Methods
Authors: Xueqing Liu, ChengXiang Zhai, Wei Han, Onur Güngör
Venue: WWW
Year: 2017

Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.
Understanding Online Collection Growth Over Time: A Case Study of Pinterest
Authors: Caroline Lo, Justin Cheng, Jure Leskovec
Venue: WWW
Year: 2017

A common feature of content discovery applications is the ability of users to save and organize digital items into collections, e.g., images into photo albums or songs into playlists. Understanding how these collections grow over time is important for user retention and collection as well as item recommendation. Here we study factors that affect collection growth over a long period of time. We conduct a large-scale longitudinal analysis of over 2.6 million collections, known as boards, on Pinterest, over a period of three years. We study the inter-event time distribution of pins saved to boards and find that it can be accurately described by a two-component lognormal mixture model. The mixture components reveal that board growth can be characterized by short-term fast-paced sprees of activity, and longer breaks between these sprees. Commonalities emerge in spree behavior; for example, sprees have consistent temporal dynamics and the content saved within the same spree is more focused compared to between sprees. Surprisingly, we observe that boards with longer initial sprees are less likely to have long-term growth. On the other hand, boards with more frequently occurring sprees continue growing for a longer time, and tend to have a larger size. Finally, we synthesize our findings into a series of predictive models which show that initial board evolution is a strong signal for long-term board growth in terms of size and lifespan. Overall, our research has important implications for the design of online content discovery applications and has immediate applications in user modeling and recommendation systems.
Selected Advanced Data Visualizations: "The UX-Machine", Cultural Visualisation, Cognitive Big Data, and Communication of Health and Wellness Data
Authors: Artur Lugmayr, Adam Greenfeld, Danjing Joy Zhang
Venue: WWW
Year: 2017

The goal of this set of this demonstration presentation is to describe a set of visualizations that underline the importance of appropriate data visualizations in particular contexts. The goal is to showcase different types of visualizations, and illustrate how emerging media technology can be utilized for this purpose. First, we showcase "The UX-Machine", which is a tool for visualizing bio-feedback data; second, we illustrate a visualization of a cultural photographic collection; third, we present Cognitive Big Data as emerging new technology in the domain of Big Data, and fourth present visualization as a matter of communication and culture. The goal of this paper is to introduce and describe the key aspects of visualization on some concrete examples. The demonstrators should also illustrate the use of emerging media technology as part of advanced concepts for data visualization.
Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level
Authors: Denis Lukovnikov, Asja Fischer, Jens Lehmann, Sören Auer
Venue: WWW
Year: 2017

Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer natural language questions using facts contained in a knowledge graph. Simple questions, which can be answered by the extraction of a single fact, constitute a large part of questions asked on the web but still pose challenges to QA systems, especially when asked against a large knowledge resource. Existing QA systems usually rely on various components each specialised in solving different sub-tasks of the problem (such as segmentation, entity recognition, disambiguation, and relation classification etc.). In this work, we follow a quite different approach: We train a neural network for answering simple questions in an end-to-end manner, leaving all decisions to the model. It learns to rank subject-predicate pairs to enable the retrieval of relevant facts given a question. The network contains a nested word/character-level question encoder which allows to handle out-of-vocabulary and rare word problems while still being able to exploit word-level semantics. Our approach achieves results competitive with state-of-the-art end-to-end approaches that rely on an attention mechanism.
ESearch: Incorporating Text Corpus and Structured Knowledge for Open Domain Entity Search
Authors: Denghao Ma, Yueguo Chen, Jun Chen, Xiaoyong Du, Xiangliang Zhang
Venue: WWW
Year: 2017

The paper introduces an open domain entity search system called ESearch, which aims at finding a list of relevant entities to an open domain entity search query (a natural language question). The system is built on top of a Wikipedia text corpus, as well as the structured DBPedia knowledge base. Entities are initially ranked by a model which effectively associates context matching (based on the contexts of entities in the unstructured text corpus) and category matching (based on the types of entities in the structured knowledge base). They are ranked further by a re-ranking component supported by blind feedback or user feedback on entities. We show that category matching is critical for the search performance and the re-ranking component can boost the performance largely. Category matching therefore needs some query entity types (especially specific entity types) as input. However, it is often hard for systems to detect specific entity types because users may not be familiar with how the types of desired entities are defined in the structured knowledge base. In ESearch, we design an effective ranking model of entity types to facilitate blind feedback and user feedback on desired entity types for category matching, so that users can effectively perform entity search without the need of explicitly providing any query entity types as inputs.
Web Mail is not Dead!: It's Just Not Human Anymore
Authors: Yoelle Maarek
Venue: WWW
Year: 2017

Many have noticed that personal communications have slowly moved from mail to social media and instant messaging platforms, especially with younger generation [6]. Yet Web Mail traffic continues to steadily grow. A paradox? Not really. We have observed at Yahoo Research that the nature of email traffic has significantly changed in the last two decades, and it is now dominated by machine-generated messages. These messages include hotel newsletters, from which users forgot to unsubscribe, repeated, and often annoying, notifications from a social media site, or critical information such as a flight e-ticket, a purchase invoice, or a telephone bill. In this talk, I first share some elements of this journey that led us to this critical finding that 90% of today's Web Mail is sent by automatic scripts [1]. I then discuss the challenges and opportunities this drastic change offers. First the key challenge: namely, the need for Web mail services to revisit their usage assumptions and their traditional features in light of this change. An obvious example is the "reply" button being displayed by default below messages sent from a "[email protected]" sender. Another feature is mail classification, which has finally experienced some changes in the last few years, [4]. I then discuss the opportunities in this era of big data. One first insight is that messages that have been generated by a same script, share some semantic commonality. Being able to automatically cluster such messages, and map such clusters into "templates" brings great value for discovering meaning, for generalizing findings and predicting behaviors [5]. A second insight is that within this commonality, the differences bring even more value, which allows highlighting what makes individuals unique within a crowd. In particular we discuss extraction techniques that automatically identify these unique elements [2]. Yet, they also present a clear risk in terms of privacy and I describe the absolute need for guaranteeing k-anonymity in our mining techniques, [3]. I conclude by encouraging the research community to explore this new domain of Web mail search and data mining.
AncestryAI: A Tool for Exploring Computationally Inferred Family Trees
Authors: Eric Malmi, Marko Rasa, Aristides Gionis
Venue: WWW
Year: 2017

Many people are excited to discover their ancestors and thus decide to take up genealogy. However, the process of finding the ancestors is often very laborious since it involves comparing a large number of historical birth records and trying to manually match the people mentioned in them. We have developed AncestryAI, an open-source tool for automatically linking historical records and exploring the resulting family trees. We introduce a record-linkage method for computing the probabilities of the candidate matches, which allows the users to either directly identify the next ancestor or narrow down the search. We also propose an efficient layout algorithm for drawing and navigating genealogical graphs. The tool is additionally used to crowdsource training and evaluation data so as to improve the matching algorithm. Our objective is to build a large genealogical graph, which could be used to resolve various interesting questions in the areas of computational social science, genetics, and evolutionary studies. The tool is openly available at: http://emalmi.kapsi.fi/ancestryai/.
What's in a Name?: Understanding Profile Name Reuse on Twitter
Authors: Enrico Mariconti, Jeremiah Onaolapo, Syed Sharique Ahmad, Nicolas Nikiforou, Manuel Egele, Nick Nikiforakis, Gianluca Stringhini
Venue: WWW
Year: 2017

Users on Twitter are commonly identified by their profile names. These names are used when directly addressing users on Twitter, are part of their profile page URLs, and can become a trademark for popular accounts, with people referring to celebrities by their real name and their profile name, interchangeably. Twitter, however, has chosen to not permanently link profile names to their corresponding user accounts. In fact, Twitter allows users to change their profile name, and afterwards makes the old profile names available for other users to take.
Sustainability and Smart Technology
Authors: Dora Vasileva Marinova
Venue: WWW
Year: 2017

Today's cities have attracted the largest share of the global human population and have become symbols of advancements in knowledge and technology. They have however also begun to represent a lifestyle detached from nature and geared towards a high level of consumption. To make cities sustainable, they need to provide better living conditions for everybody, access to resource availability and security -- water, energy, food, better opportunities and respond to climate change. Smart technologies can be a tool assisting in achieving sustainability in the city. In line with the UN Sustainable Development Goals, the presentation defines the link between sustainability and smart technologies by putting community livability at its centre. The concepts of (1) community self-governance and self-organisation; and (2) ethical economics and sustainometrics are developed as the principles which can underpin success in improving the livability of urban environments for its current and future inhabitants. The example of working with the community in Geraldton, Western Australia is used to showcase the application of smart technologies for improving the livability of the city.
The Nature and Origin of Heavy Tails in Retweet Activity
Authors: Peter Mathews, Lewis Mitchell, Giang Nguyen, Nigel G. Bean
Venue: WWW
Year: 2017

Modern social media platforms facilitate the rapid spread of information online. Modelling phenomena such as social contagion and information diffusion are contingent upon a detailed understanding of the information-sharing processes. In Twitter, an important aspect of this occurs with retweets, where users rebroadcast the tweets of other users. To improve our understanding of how these distributions arise, we analyse the distribution of retweet times. We show that a power law with exponential cutoff provides a better fit than the power laws previously suggested. We explain this fit through the burstiness of human behaviour and the priorities individuals place on different tasks.
Flipping 419 Cybercrime Scams: Targeting the Weak and the Vulnerable
Authors: Gibson Mba, Jeremiah Onaolapo, Gianluca Stringhini, Lorenzo Cavallaro
Venue: WWW
Year: 2017

Most of cyberscam-related studies focus on threats perpetrated against the Western society, with a particular attention to the USA and Europe. Regrettably, no research has been done on scams targeting African countries, especially Nigeria, where the notorious and (in)famous 419 advanced-fee scam, targeted towards other countries, originated. How- ever, as we know, cybercrime is a global problem affecting all parties. In this study, we investigate a form of advance fee fraud scam unique to Nigeria and targeted at Nigerians, but unknown to the Western world. For the study, we rely substantially on almost two years worth of data harvested from an on-line discussion forum used by criminals. We complement this dataset with recent data from three other active forums to consolidate and generalize the research. We apply machine learning to the data to understand the criminals' modus operandi. We show that the criminals exploit the socio-political and economic problems prevalent in the country to craft various fraud schemes to defraud vulnerable groups such as secondary school students and unemployed graduates. The result of our research can help potential victims and policy makers to develop measures to counter the activities of these criminal groups.