OpenRaiser/Intern-Atlas huggingface dataset:
A Rule-Based Approach to Extracting Relations from Music Tidbits
Authors: Sergio Oramas, Mohamed Sordo, Luis Espinosa Anke
Venue: WWW
Year: 2015

This paper presents a rule based approach to extracting relations from unstructured music text sources. The proposed approach identifies and disambiguates musical entities in text, such as songs, bands, persons, albums and music genres. Candidate relations are then obtained by traversing the dependency parsing tree of each sentence in the text with at least two identified entities. A set of syntactic rules based on part of speech tags are defined to filter out spurious and irrelevant relations. The extracted entities and relations are finally represented as a knowledge graph. We test our method on texts from songfacts.com, a website that provides tidbits with facts and stories about songs. The extracted relations are evaluated intrinsically by assessing their linguistic quality, as well as extrinsically by assessing the extent to which they map an existing music knowledge base. Our system produces a vast percentage of linguistically correct relations between entities, and is able to replicate a significant part of the knowledge base.
Defining and Evaluating Video Hyperlinking for Navigating Multimedia Archives
Authors: Roeland J. F. Ordelman, Maria Eskevich, Robin Aly, Benoit Huet, Gareth J. F. Jones
Venue: WWW
Year: 2015

Multimedia hyperlinking is an emerging research topic in the context of digital libraries and (cultural heritage) archives. We have been studying the concept of video-to-video hyperlinking from a video search perspective in the context of the MediaEval evaluation benchmark for several years. Our task considers a use case of exploring large quantities of video content via an automatically created hyperlink structure at the media fragment level. In this paper we report on our findings, examine the features of the definition of video hyperlinking based on results, and discuss lessons learned with respect to evaluation of hyperlinking in real-life use scenarios.
Big Scholarly Data in CiteSeerX: Information Extraction from the Web
Authors: Alexander G. Ororbia II, Jian Wu, Madian Khabsa, Kyle Williams, Clyde Lee Giles
Venue: WWW
Year: 2015

We examine CiteSeerX, an intelligent system designed with the goal of automatically acquiring and organizing large-scale collections of scholarly documents from the world wide web. From the perspective of automatic information extraction and modes of alternative search, we examine various functional aspects of this complex system with an eye towards ongoing and future research developments.
Design of Interactional End-to-End Web Applications for Smart Cities
Authors: Erich Ortner, Marco von Mevius, Peter Wiedmann, Florian Kurz
Venue: WWW
Year: 2015

Nowadays, the number of flexible and fast human to application system interactions is dramatically increasing. For instance, citizens interact with the help of the internet to organize surveys or meetings (in real-time) spontaneously. These interactions are supported by technologies and application systems such as free wireless networks, web -or mobile apps. Smart Cities aim at enabling their citizens to use these digital services, e.g., by providing enhanced networks and application infrastructures maintained by the public administration. However, looking beyond technology, there is still a significant lack of interaction and support between "normal" citizens and the public administration. For instance, democratic decision processes (e.g. how to allocate public disposable budgets) are often discussed by the public administration without citizen involvement. This paper introduces an approach, which describes the design of enhanced interactional web applications for Smart Cities based on dialogical logic process patterns. We demonstrate the approach with the help of a budgeting scenario as well as a summary and outlook on further research.
A Semantic Hybrid Approach for Sound Recommendation
Authors: Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Sergio Oramas, Xavier Serra
Venue: WWW
Year: 2015

In this work we describe a hybrid recommendation approach for recommending sounds to users by exploiting and semantically enriching textual information such as tags and sounds descriptions. As a case study we used Freesound, a popular site for sharing sound samples which counts more than 4 million registered users. Tags and textual sound descriptions are exploited to extract and link entities to external ontologies such as WordNet and DBpedia. The enriched data are eventually merged with a domain specific tagging ontology to form a knowledge graph. Based on this latter, recommendations are then computed using a semantic version of the feature combination hybrid approach. An evaluation on historical data shows improvements with respect to state of the art collaborative algorithms.
Exploring Communities for Effective Location Prediction
Authors: Jun Pang, Yang Zhang
Venue: WWW
Year: 2015

Humans are social animals, they interact with different communities to conduct different activities. The literature has shown that human mobility is constrained by their social relations. In this work, we investigate the social impact on a user's mobility from his communities in order to conduct location prediction effectively. Through analysis of a real-life dataset, we demonstrate that (1) a user gets more influences from his communities than from all his friends; (2) his mobility is influenced only by a small subset of his communities; (3) influence from communities depends on social contexts. We further exploit a SVM to predict a user's future location based on his community information. Experimental results show that the model based on communities leads to more effective predictions than the one based on friends.
Mining Mobility Data
Authors: Spiros Papadimitriou, Tina Eliassi-Rad
Venue: WWW
Year: 2015

The fairly recent explosion in the availability of reasonably fast wireless and mobile data networks has spurred demand for more capable mobile computing devices. Conversely, the emergence of new devices increases demand for better networks, creating a virtuous cycle. The current concept of a smartphone as an always-connected computing device with multiple sensing modalities was brought into the mainstream by the Apple iPhone just a few years ago. Such devices are now seeing an explosive growth. Additionally, for many people in the world, such devices will be the first computers they use. Furthermore, small, cheap, always-connected devices (standalone or peripheral) with additional sensing capabilities are very recently emerging, further blurring the lines between the Web, mobile applications (a.k.a.~apps), and the real world. All of this opens up countless possibilities for data collection and analysis, for a broad range of applications. In this tutorial, we survey the state-of-the-art in terms of mining mobility data across different application areas such as ads, geo-social, privacy and security. Our tutorial consists of three parts. (1) We summarize the possibilities and challenges in the collection of data from various sensing modalities. (2) We cover cross-cutting challenges such as real-time analysis and security; and we outline cross-cutting algorithms for mobile data mining such as network inference and streaming algorithms. (3) We focus on how all of this can be usefully applied to broad classes of applications, notably mobile and location-based social, mobile advertising and search, mobile Web, and privacy and security. We conclude by showcasing the opportunities for new data collection techniques and new data mining methods to meet the challenges and applications that are unique to the mobile arena (e.g., leveraging emerging embedded computing and sensing technologies to collect a large variety and volume of new kinds of "big data").
Remix in 3D Printing: What your Sources say About You
Authors: Spiros E. Papadimitriou, Evangelos E. Papalexakis, Bin Liu, Hui Xiong
Venue: WWW
Year: 2015

Concurrently with the recent, rapid adoption of 3D printing technologies, online sharing of 3D-printable designs is growing equally rapidly, even though it has received far less attention. We study remix relationships on Thingiverse, the dominant online repository and social network for 3D printing. We collected data of designs published over five years, and we find that remix ties exhibit both homophily and inverse-homophily across numerous key metrics, which is stronger compared to other kinds of social and content links. This may have implications on graph prediction tasks, as well as on the design of 3D-printable content repositories.
Geosocial Search: Finding Places based on Geotagged Social-Media Posts
Authors: Barak Pat, Yaron Kanza, Mor Naaman
Venue: WWW
Year: 2015

Geographic search -- where the user provides keywords and receives relevant locations depicted on a map -- is a popular web application. Typically, such search is based on static geographic data. However, the abundant geotagged posts in microblogs such as Twitter and in social networks like Instagram, provide contemporary information that can be used to support geosocial search---geographic search based on user activities in social media. Such search can point out where people talk (or tweet) about different topics. For example, the search results may show where people refer to ``jogging'', to indicate popular jogging places. The difficulty in implementing such search is that there is no natural partition of the space into ``documents'' as in ordinary web search. Thus, it is not always clear how to present results and how to rank and filter results effectively. In this paper, we demonstrate a two-step process of first, quickly finding the relevant areas by using an arbitrary indexed partition of the space, and secondly, applying clustering on discovered areas, to present more accurate results. We introduce a system that utilizes geotagged posts in geographic search and illustrate how different ranking methods can be used, based on the proposed two-step search process. The system demonstrates the effectiveness and usefulness of the approach.
Diagnoses, Decisions, and Outcomes: Web Search as Decision Support for Cancer
Authors: Michael J. Paul, Ryen W. White, Eric Horvitz
Venue: WWW
Year: 2015

People diagnosed with a serious illness often turn to the Web for their rising information needs, especially when decisions are required. We analyze the search and browsing behavior of searchers who show a surge of interest in prostate cancer. Prostate cancer is the most common serious cancer in men and is a leading cause of cancer-related death. Diagnoses of prostate cancer typically involve reflection and decision making about treatment based on assessments of preferences and outcomes. We annotated timelines of treatment-related queries from nearly 300 searchers with tags indicating different phases of treatment, including decision making, preparation, and recovery. Using this corpus, we present a variety of analyses toward the goal of understanding search and decision making about treatments. We characterize search queries and the content of accessed pages for different treatment phases, model search behavior during the decision-making phase, and create an aggregate alignment of treatment timelines illustrated with a variety of visualizations. The experiments provide insights about how people who are engaged in intensive searches about prostate cancer over an extended period of time pursue and access information from the Web.
Smart Cities Governance Informatability?: Let's First Understand the Atoms
Authors: Alois Paulin
Venue: WWW
Year: 2015

In this paper we search for and analyze the atomic components of general governance systems and discuss whether or not they can be informated, i.e. tangibly represented within the digital realm of information systems. We draw a framework based on the theories of Downs, Jellinek, and Hohfeld and find that the therein identified atomic components cannot be informated directly, but only indirectly, due to the inherent complexity of governance. We outline pending research questions to be addressed in the future.
PocketTrend: Timely Identification and Delivery of Trending Search Content to Mobile Users
Authors: Gennady Pekhimenko, Dimitrios Lymberopoulos, Oriana Riva, Karin Strauss, Doug Burger
Venue: WWW
Year: 2015

Trending search topics cause unpredictable query load spikes that hurt the end-user search experience, particularly the mobile one, by introducing longer delays. To understand how trending search topics are formed and evolve over time, we analyze 21 million queries submitted during periods where popular events caused search query volume spikes. Based on our findings, we design and evaluate PocketTrend, a system that automatically detects trending topics in real time, identifies the search content associated to the topics, and then intelligently pushes this content to users in a timely manner. In that way, PocketTrend enables a client-side search engine that can instantly answer user queries related to trending events, while at the same time reducing the impact of these trends on the datacenter workload. Our results, using real mobile search logs, show that in the presence of a trending event, up to 13-17% of the overall search traffic can be eliminated from the datacenter, with as many as 19% of all users benefiting from PocketTrend.
Urban Informatics and the Web
Authors: Konstantinos Pelechrinis, Daniele Quercia
Venue: WWW
Year: 2015

Based on a recent report from the United Nations, more than 50% of the world's population currently lives in cities. This percentage is projected to increase to 70% by the year 2050 [1]. As massive amounts of people move to urban areas there is a need for cities to be run more efficiently, while at the same time improving the quality of life of their dwellers. Nevertheless, the exact same force that sets the above requirement, i.e., the proliferation of urbanization levels, makes this task much harder and challenging, especially in megacities. Despite the aforementioned conflicting dynamics, many city management operations can be facilitated by appropriate exploitation of the unprecedented amount of data that can be made available to authorities from a variety of sources. In the era of big data and ubiquitous and pervasive mobile computing, different types of sensors such as parking meters, weather sensors, traffic sensors, pipe sensors, public transportation ticket readers and even human sensors (e.g., through web technologies, social media or cell phone usage data) can assist in these efforts. Furthermore, civic applications can exploit web and mobile technologies to deliver a livable, sustainable and resilient environment to the citizens. Harnessing these information streams and technologies presents many challenges that are in the epicenter of this tutorial. In this tutorial we will present the current practices and methods in the emerging field of urban informatics as well as the open challenges. The topics to be covered in this tutorial are structured in three sessions: (i) introduction to urban studies and urban informatics, (ii) civic data and technologies for urban sensing and (iii) analytical techniques used for urban data analysis. Finally, we will also provide concrete examples of urban informatics applications.
Investigating Factors Affecting Personal Data Disclosure
Authors: Christos Perentis, Michele Vescovi, Bruno Lepri
Venue: WWW
Year: 2015

Mobile devices, sensors and social networks have dramatically increased the collection and sharing of personal and contextual information of individuals. Hence, users constantly make disclosure decisions on the basis of a difficult trade-off between using services and data protection. Understanding the factors linked to the disclosure behavior of personal information is a step forward to assist users in their decisions. In this paper, we model the disclosure of personal information and investigate their relationships not only with demographic and self-reported individual characteristics, but also with real behavior inferred from mobile phone usage. Preliminary results show that real behavior captured from mobile data relates with actual sharing behavior, providing the basis for future predictive models.
Adding Rules on Existing Hypermedia APIs
Authors: Michael Petychakis, Fenareti Lampathaki, Dimitrios Askounis
Venue: WWW
Year: 2015

During the past years, the data deluge that prevails in the World Wide Web has been accompanied by a number of APIs that expose business logic. In this paper, we discuss a novel approach to enrich existing API standards definitions with business rules. Taking advantage of the REST principles, we aim at enabling the creation of generic clients that can dynamically navigate through semantically enriched web affordances with the help of Hydra-based Hypermedia API descriptions, which encapsulate the finite state machine of possible actions into SWRL rules.
Inferring Graphs from Cascades: A Sparse Recovery Framework
Authors: Jean Pouget-Abadie, Thibaut Horel
Venue: WWW
Year: 2015

In the Graph Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. We approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascade model, for which we provide the first algorithm which recovers the graph's edges with high probability and O(s log m) measurements where s is the maximum degree of the graph and $m$ is the number of nodes. Furthermore, we show that our algorithm also recovers the edge weights (the parameters of the diffusion process) and is robust in the context of approximate sparsity. Finally we validate our approach empirically on synthetic graphs.
Exploring Heterogeneity for Multi-Domain Recommendation with Decisive Factors Selection
Authors: Shuang Qiu, Jian Cheng, Xi Zhang, Hanqing Lu
Venue: WWW
Year: 2015

To address the recommendation problems in the scenarios of multiple domains, in this paper, we propose a novel method, HMRec, which models both consistency and heterogeneity of users' multiple behaviors in a unified framework. Moreover, the decisive factors of each domain can also be captured by our approach successfully. Experiments on the real multi-domain dataset demonstrate the effectiveness of our model.
Chatty, Happy, and Smelly Maps
Authors: Daniele Quercia
Venue: WWW
Year: 2015

Mapping apps are the greatest game-changer for encouraging people to explore the city. You take your phone out and you know immediately where to go. However, the app also assumes there are only a handful of directions to the destination. It has the power to make those handful of directions the definitive direction to that destination. A few years ago, my research started to focus on understanding how people psychologically experience the city. I used computer science tools to replicate social science experiments at scale, at web scale [4,5]. I became captivated by the beauty and genius of traditional social science experiments done by Jane Jacobs, Stanley Milgram, Kevin Lynch[1,2,3]. The result of that research has been the creation of new maps, maps where one does not only find the shortest path but also the most enjoyable path [6,9].
"Roles for the Boys?": Mining Cast Lists for Gender and Role Distributions over Time
Authors: Will Radford, Matthias Gallé
Venue: WWW
Year: 2015

Film and television play an important role in popular culture. However studies that require watching and annotating video are time-consuming and expensive to run at scale. We explore information mined from media database cast lists to explore the evolution of different roles over time. We focus on the gender distribution of those roles and how this changes over time. Finally, we compare real-life census gender distributions to our web-mediated onscreen gender data. We propose these methodologies are a useful adjunct to traditional analysis that allow researchers to explore the relationship between online and onscreen gender depictions.
Online Search Evaluation with Interleaving
Authors: Filip Radlinski
Venue: WWW
Year: 2015

Online evaluation allows information retrieval systems to be assessed based on how real users respond to search results presented. Compared with traditional offline evaluation based on manual relevance assessments, online evaluation is particularly attractive in settings where reliable assessments are difficult or too expensive to obtain. However, the successful use of online evaluation requires the right metrics to be used, as real user behaviour is often difficult to interpret. I will present interleaving, a sensitive online evaluation approach that creates paired comparisons for every user query, and compare it with alternative A/B online evaluation approaches. I will also show how interleaving can be parameterized to create a family of evaluation metrics that can be chosen to best match the goals of an evaluation.
Crossing the Boundaries of Communities via Limited Link Injection for Information Diffusion In Social Networks
Authors: Dimitrios Rafailidis, Alexandros Nanopoulos
Venue: WWW
Year: 2015

We propose a new link-injection method aiming at boosting the overall diffusion of information in social networks. Our approach is based on a diffusion-coverage score of the ability of each user to spread information over the network. Candidate links for injection are identified by a matrix factorization technique and link injection is performed by attaching links to users according to their score. We additionally perform clustering to identify communities in order to inject links that cross the boundaries of such communities. In our experiments with five real world networks, we demonstrate that our method can significantly spread the information diffusion by performing limited link injection, essential to real-world applications
Repeat Consumption Recommendation Based on Users Preference Dynamics and Side Information
Authors: Dimitrios Rafailidis, Alexandros Nanopoulos
Venue: WWW
Year: 2015

We present a Coupled Tensor Factorization model to recommend items with repeat consumption over time. We introduce a measure that captures the rate with which the preferences of each user shift over time. Repeat consumption recommendations are generated based on factorizing the coupled tensor, by weighting the importance of past user preferences according to the captured rate. We also propose a variant, where the diversity of the side information is taken into account, by higher weighting users that have more rare side information. Our experiments with real-world datasets from last.fm and MovieLens demonstrate that the proposed models outperform several baselines.