OpenRaiser/Intern-Atlas huggingface dataset:
From Complex Object Exploration to Complex Crowdsourcing
Authors: Sihem Amer-Yahia, Senjuti Basu Roy
Venue: WWW
Year: 2015

Forming and exploring complex objects is at the heart of a variety of emerging web applications. Historically, existing work on complex objects has been developed in two separate areas: composite item retrieval and team formation. At the same time, emerging applications that harness the wisdom of crowd workers, such as, document editing by workers, sentence translation by fans (or fan-subbing), innovative design, citizen science or journalism, represent complex crowdsourcing, in which an object may represent a complex task formed by a set of sub-tasks or a team of workers who work together to solve the task. The goal of this tutorial is to bridge the gap between composite item retrieval and team formation and define new research directions for complex crowdsourcing applications
Improved Theoretical and Practical Guarantees for Chromatic Correlation Clustering
Authors: Yael Anava, Noa Avigdor-Elgrabli, Iftah Gamzu
Venue: WWW
Year: 2015

We study a natural generalization of the correlation clustering problem to graphs in which the pairwise relations between objects are categorical instead of binary. This problem was recently introduced by Bonchi et al. under the name of chromatic correlation clustering, and is motivated by many real-world applications in data-mining and social networks, including community detection, link classification, and entity de-duplication. Our main contribution is a fast and easy-to-implement constant approximation framework for the problem, which builds on a novel reduction of the problem to that of correlation clustering. This result significantly progresses the current state of knowledge for the problem, improving on a previous result that only guaranteed linear approximation in the input size. We complement the above result by developing a linear programming-based algorithm that achieves an improved approximation ratio of 4. Although this algorithm cannot be considered to be practical, it further extends our theoretical understanding of chromatic correlation clustering. We also present a fast heuristic algorithm that is motivated by real-life scenarios in which there is a ground-truth clustering that is obscured by noisy observations. We test our algorithms on both synthetic and real datasets, like social networks data. Our experiments reinforce the theoretical findings by demonstrating that our algorithms generally outperform previous approaches, both in terms of solution cost and reconstruction of an underlying ground-truth clustering.
Understanding Smart City Business Models: A Comparison
Authors: Leonidas G. Anthopoulos, Panos Fitsilis
Venue: WWW
Year: 2015

Smart cities have attracted the international scientific and business attention and a niche market is being evolved, which engages almost all the business sectors. In their attempt to empower and promote urban competitive advantages, local governments have approached the smart city context and they target habitants, visitors and investments. However, engaging the smart city context is not free-of-charge and corresponding investments are extensive and of high risk without the appropriate management. Moreover, investing in the smart city domain does not secure corresponding mission success and both governments and vendors require more effective instruments. This paper performs an investigation on the smart city business models and is a work in progress. Modeling can illustrate where corresponding profit comes from and how it flows, while a significant business model portfolio is eligible for smart city stakeholders.
Comparing Smart Cities with different modeling approaches
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2015

Smart cities have attracted an extensive and increasing interest from both science and industry with an increasing number of international examples emerging from across the world. However, despite the significant role that smart cities can play to deal with recent urban challenges, the concept has been criticized for being influenced by vendor hype. There are various attempts to conceptualize smart cities and various benchmarking methods have been developed to evaluate their impact. In this paper the modelling and benchmarking approaches are systematically compared. There are six common dimensions among the approaches, namely people, government, economy, mobility, environment and living. This paper utilizes existing smart city analysis models in order to review three representative smart city cases and useful outcomes are extrapolated from this comparison.
Discriminative Models for Predicting Deception Strategies
Authors: Darren Scott Appling, Erica J. Briscoe, Clayton J. Hutto
Venue: WWW
Year: 2015

Although a large body of work has previously investigated various cues predicting deceptive communications, especially as demonstrated through written and spoken language (e.g., [30]), little has been done to explore predicting kinds of de- ception. We present novel work to evaluate the use of textual cues to discriminate between deception strategies (such as exaggeration or falsification), concentrating on intention- ally untruthful statements meant to persuade in a social media context. We conduct human subjects experimenta- tion wherein subjects were engaged in a conversational task and then asked to label the kind(s) of deception they employed for each deceptive statement made. We then develop discriminative models to understand the difficulty between choosing between one and several strategies. We evaluate the models using precision and recall for strategy prediction among 4 deception strategies based on the most relevant psycholinguistic, structural, and data-driven cues. Our single strategy model results demonstrate as much as a 58% increase over baseline (random chance) accuracy and we also find that it is more difficult to predict certain kinds of de- ception than others.
The TIB AV Portal as a Future Linked Media Ecosystem
Authors: Paloma Marín-Arraiza, Sven Strobel
Venue: WWW
Year: 2015

Various techniques for video analysis, concept mapping, semantic search and metadata management are part of the current features of the TIB|AV Portal as described in this demo. The segment identification and ontology annotation make the portal a good platform to support the Linked Data and Media. Weaving into a machine-readable metadata format will complete this task.
Architecture and Implementation Issues, Towards a Dynamic Waste Collection Management System
Authors: George Asimakopoulos, Sotiris P. Christodoulou, Andreas Gizas, Vassilis Triantafillou, Giannis Tzimas, John V. Gialelis, Artemios G. Voyiatzis, Dimitrios Karadimas, Andreas Papalambrou
Venue: WWW
Year: 2015

Dynacargo is an ongoing research project that introduces a breakthrough approach for cargo management systems, as it places the hauled cargos in the center of a haulage information management system, instead of the vehicle. Dynacargo attempts to manage both distribution and collection processes, providing an integrated approach. This paper presents the Dynacargo architectural modules and interrelations between them, as well as the research issues and development progress of some selected modules. In the context of Dynacargo project, a set of durable, low cost RFID tags are placed on waste bins in order to produce crucial data that is fed via diverse communication channels into the cargo management system. Besides feeding the management system with raw data from waste bins, data mining techniques are used on archival data, in order to predict current waste bins fill status. Moreover easy-to-use mobile and web applications will be developed to encourage citizens to participate and become active information producers and consumers. Dynacargo project overall aim is to develop a near real-time monitoring system that monitors and transmits waste bins' fill level, in order to dynamically manage the waste collection more efficiently by minimizing distances covered by refuse vehicles, relying on efficient routing algorithms.
Roomba: Automatic Validation, Correction and Generation of Dataset Metadata
Authors: Ahmad Assaf, Aline Senart, Raphaël Troncy
Venue: WWW
Year: 2015

Data is being published by both the public and private sectors and covers a diverse set of domains ranging from life sciences to media or government data. An example is the Linked Open Data (LOD) cloud which is potentially a gold mine for organizations and individuals who are trying to leverage external data sources in order to produce more informed business decisions. Considering the significant variation in size, the languages used and the freshness of the data, one realizes that spotting spam datasets or simply finding useful datasets without prior knowledge is increasingly complicated. In this paper, we propose Roomba, a scalable automatic approach for extracting, validating, correcting and generating descriptive linked dataset profiles. While Roomba is generic, we target CKAN-based data portals and we validate our approach against a set of open data portals including the Linked Open Data (LOD) cloud as viewed on the DataHub. The results demonstrate that the general state of various datasets and groups, including the LOD cloud group, needs more attention as most of the datasets suffer from bad quality metadata and lack some informative metrics that are required to facilitate dataset search.
LDOW 2013: The 8th Workshop on Linked Data on the Web
Authors: Sören Auer, Tim Berners-Lee, Christian Bizer, Tom Heath
Venue: WWW
Year: 2015

This paper presents a brief summary of the eight workshop on Linked Data on the Web. The LDOW 2013 workshop is held in conjunction with the World Wide Web conference 2013. The focus is on data publishing, integration and consumption using RDF and other semantic representation formalisms and technologies.
Classification Method for Shared Information on Twitter Without Text Data
Authors: Seigo Baba, Fujio Toriumi, Takeshi Sakaki, Kousuke Shinoda, Satoshi Kurihara, Kazuhiro Kazama, Itsuki Noda
Venue: WWW
Year: 2015

During a disaster, appropriate information must be collected. For example, victims and survivors require information about shelter locations and dangerous points or advice about protecting themselves. Rescuers need information about the details of volunteer activities and supplies, especially potential shortages. However, collecting such localized information is difficult from such mass media as TV and newspapers because they generally focus on information aimed at the general public. On the other hand, social media can attract more attention than mass media under these circumstances since they can provide such localized information. In this paper, we focus on Twitter, one of the most influential social media, as a source of local information. By assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we propose a novel tweet classification method that focuses on retweets without text mining. We linked tweets based on retweets to make a retweet network that connects similar tweets and extracted clusters that contain similar tweets from the constructed network by our clustering method. We also subjectively verified the validity of our proposed classification method. Our experiment verified that the ratio of the clusters whose tweets are mutually similar in the cluster to all clusters is very high and the similarities in each cluster are obvious. Finally, we calculated the linguistic similarities of the results to clarify our proposed method's features. Our method classified topic-similar tweets, even if they are not linguistically similar.
Essential Web Pages Are Easy to Find
Authors: Ricardo Baeza-Yates, Paolo Boldi, Flavio Chierichetti
Venue: WWW
Year: 2015

In this paper we address the problem of estimating the index size needed by web search engines to answer as many queries as possible by exploiting the marked difference between query and click frequencies. We provide a possible formal definition for the notion of essential web pages as those that cover a large fraction of distinct queries --- i.e., we look at the problem as a version of MaxCover. Although in general MaxCover is approximable to within a factor of 1-1/e ~0.632 from the optimum, we provide a condition under which the greedy algorithm does find the actual best cover (or remains at a known bounded factor from it). The extra check for optimality (or for bounding the ratio from the optimum) comes at a negligible algorithmic cost. Moreover, in most practical instances of this problem, the algorithm is able to provide solutions that are provably optimal, or close to optimal. We relate this observed phenomenon to some properties of the queries' click graph. Our experimental results confirm that a small number of web pages can respond to a large fraction of the queries (e.g., 0.4% of the pages answers 20% of the queries). Our approach can be used in several related search applications, and has in fact an even more general appeal --- as a first example, our preliminary experimental study confirms that our algorithm has extremely good performances on other (social network based) MaxCover instances.
Large-scale Contextual Query-to-Ad Matching and Retrieval System for Sponsored Search: (Abstract)
Authors: Ricardo Baeza-Yates, Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Fabrizio Silvestri
Venue: WWW
Year: 2015

Semantic embeddings of words (or objects in general) into a vector space have proven to be a powerful tool in many applications. In this talk we are going to show one possible application of semantic embeddings to sponsored search. Sponsored search represents the major source of revenue for web search engines and it is based on the following mechanism: each advertiser maintains a list of keywords they deem of interest with regards to their business. According to this targeting model, when a query is issued, all advertisers with a matching keyword are entered into an auction according to the amount they bid for the query, and the winner gets to show their ad, usually paying the next largest bid (this is called second price). The main challenges is that a query may not match many keywords, resulting in lower auction value, lower ad quality, and lost revenue for both, advertisers and publishers. We address them by applying semantic embeddings to this problem by learning how to project queries and ads in a common embedding, thus sharing the same feature space. The major novelty of the techniques we show is that learning is done by jointly modeling their content (words in queries and ad metadata), as well as their context within a search session. This model has several advantages and can be applied to at least three tasks. First, it can be used to generate query rewrites with a specific bias towards rewrites able to match relevant advertising. Second, it can be used also to retrieve for a given a query a set of relevant ads to be sent to the auction phase. Third, given an ad we are able to retrieve all the queries for which that ad can be considered relevant. The major advantage of learning both content and context embeddings is in the fact that a context-based model may suffer from coverage issue: if a query or an ad does not appear in the training set it cannot be treated by the model; content-based embeddings instead can be used to also build models capturing similarities between content, e.g. for a query not appearing in the model built we may capture some of its sub-queries by using content vectors. Another very interesting characteristic of this method is that all the tasks mentioned above are basically solved by means of a simple K-nearest neighbor search over the set of vectors in the embedding. The method has been trained up to 12 billion sessions, one of the largest corpora reported so far. We report offline and online experimental results, as well as post-deployment metrics. The results show that this approach significantly outperforms existing state-of-the-art, substantially improving a number of key business metrics.
Correlation of Node Importance Measures: An Empirical Study through Graph Robustness
Authors: Mirza Basim Baig, Leman Akoglu
Venue: WWW
Year: 2015

Graph robustness is a measure of resilience to failures and targeted attacks. A large body of research on robustness focuses on how to attack a given network by deleting a few nodes so as to maximally disrupt its connectedness. As a result, literature contains a myriad of attack strategies that rank nodes by their relative importance for this task. How different are these strategies? Do they pick similar sets of target nodes, or do they differ significantly in their choices? In this paper, we perform the first large scale empirical correlation analysis of attack strategies, i.e., the node importance measures that they employ, for graph robustness. We approach this task in three ways; by analyzing similarities based on (i) their overall ranking of the nodes, (ii) the characteristics of top nodes that they pick, and (iii) the dynamics of disruption that they cause on the network. Our study of 15 different (randomized, local, distance-based, and spectral) strategies on 68 real-world networks reveals surprisingly high correlations among node-attack strategies, consistent across all three types of analysis, and identifies groups of comparable strategies. These findings suggest that some computationally complex strategies can be closely approximated by simpler ones, and a few strategies can be used as a close proxy of the consensus among all of them.
Online Experiments for Computational Social Science
Authors: Eytan Bakshy, Sean J. Taylor
Venue: WWW
Year: 2015

Experiments are the gold standard for establishing causal relationships. While Web-based experiments ("A/B tests") have routinely been used to assess alternative ranking models or user interface designs, they have become increasingly popular for answering important questions in the social sciences. This tutorial teaches attendees how to design, plan, implement, and analyze online experiments. First, we review basic concepts in causal inference and motivate the need for experiments. Then we will discuss basic statistical tools to help plan experiments: exploratory analysis, power calculations, and the use of simulation in R. We then discuss statistical methods to estimate causal quantities of interest and construct appropriate confidence intervals. We then discuss how to design and implement online experiments using PlanOut, an open-source toolkit for advanced online experimentation used at Facebook. We will show how to implement a variety of experiments, including basic A/B tests, within-subjects designs, as well as more sophisticated experiments. We demonstrate how experimental designs from social computing literature can be implemented, and then collaboratively plan and implement an experiment together. We then discuss issues with logging and common errors in the deployment and analysis of experiments. Finally, we will conclude the tutorial with a discussion of strategies and scalable methods for analyzing online experiments, including working with weighted data, and data with single and multi-way dependence. Throughout the tutorial, attendees will be given code examples and participate in the planning, implementation, and analysis of a Web application using Python, PlanOut, and R.
AutoTag 'n Search My Photos: Leveraging the Social Graph for Photo Tagging
Authors: Shobana Balakrishnan, Surajit Chaudhuri, Vivek R. Narasayya
Venue: WWW
Year: 2015

Personal photo collections are large and growing rapidly. Today, it is difficult to search such a photo collection for people who occur in them since it is tedious to manually associate face tags in photos. The key idea is to learn face models for friends and family of the user using tagged photos in a social graph such as Facebook as training examples. These face models are then used to automatically tag photos in the collection, thereby making it more searchable and easier to organize. To illustrate this idea we have developed a Windows app called AutoTag 'n Search My Photos. In this demo paper we describe the architecture, user interaction and controls, and our initial learnings from deploying the app.
Evaluating User Targeting Policies: Simulation Based on Randomized Experiment Data
Authors: Joel Barajas, Ram Akella, Marius Holtan
Venue: WWW
Year: 2015

We propose a user targeting simulator for online display advertising. Based on the response of 37 million visiting users (targeted and non-targeted) and their features, we simulate different user targeting policies. We provide evidence that the standard conversion optimization policy shows similar effectiveness to that of a random targeting, and significantly inferior to other causally optimized targeting policies.
Enhanced Publication Management Systems: A Systemic Approach Towards Modern Scientific Communication
Authors: Alessia Bardi, Paolo Manghi
Venue: WWW
Year: 2015

Enhanced Publication Information Systems (EPISs) are information systems devised for the management of enhanced publications (EP), i.e. digital publications enriched with (links to) other research outcomes such as data, processing workflows, software. Today, EPISs are typically realised with a "from scratch" approach that entails non-negligible implementation and maintenance costs.
LRMI, Learning Resource Metadata on the Web
Authors: Phil Barker, Lorna M. Campbell
Venue: WWW
Year: 2015

The Learning Resource Metadata Initiative (LRMI) is a collaborative initiative that aims to make it easier for teachers and learners to find educational materials through major search engines and specialized resource discovery services. The approach taken by LRMI is to extend the schema.org ontology so that educationally significant characteristics and relationships can be expressed. This, of course, builds on a long history developing metadata standards for learning resources. The context for LRMI, however, is different to these in several respects. LRMI builds on schema.org, and schema.org is designed as a means for marking up web pages to make them more intelligible to search engines; the aim is for it to be present in a significant proportion of pages on the web, that is, implemented at scale not just by metadata professionals. LRMI may have applications that go beyond the core aims of schema.org: it is possible to create LRMI metadata that is independent of a web page for example as JSON-LD records or as EPUB3 metadata.
VizCurator: A Visual Tool for Curating Open Data
Authors: Bahar Ghadiri Bashardoost, Christina Christodoulakis, Soheil Hassas Yeganeh, Renée J. Miller, Kelly A. Lyons, Oktie Hassanzadeh
Venue: WWW
Year: 2015

Vizcurator permits the exploration, understanding and curation of open RDF data, its schema, and how it has been linked to other sources. We provide visualizations that enable one to seamlessly navigate through RDFS and RDF layers and quickly understand the open data, how it has been mapped or linked, how it has been structured (and could be restructured), and how deeply it has been related to other open data sources. More importantly, Vizcurator provides a rich set of tools for data curation. It suggests possible improvements to the structure of the data and enables curators to make informed decisions about enhancements to the exploration and exploitation of the data. Moreover, Vizcurator facilitates the mining of temporal resources and the definition of temporal constraints through which the curator can identify conflicting facts. Finally, Vizcurator can be used to create new binary temporal relations by reifying base facts and linking them to temporal resources. We will demonstrate Vizcurator using LinkedCT.org, a five-star open data set mapped from the XML NIH clinical trials data (clinicaltrials.gov) that we have been maintaining and curating for several years.