OpenRaiser/Intern-Atlas huggingface dataset:
ROSeAnn: taming online semantic annotators
Authors: Stefano Ortona, Luying Chen, Giorgio Orsi
Venue: WWW
Year: 2014

Named entity extractors are a popular means for enriching documents with semantic annotations. Both the overlap and the increasing diversity in the capabilities and in the vocabularies of the annotators motivate the need for managing and integrating semantic annotations in a coherent and uniform fashion. ROSeAnn is a framework for the management and the reconciliation of semantic annotations. It provides end-users and programmers with a unified view over the results of multiple online and standalone annotators, linking them to an integrated ontology of their vocabularies, and supporting a variety of document formats such as: plain text, live Web pages, and PDF documents. Although ROSeAnn provides two predefined algorithms for conflict resolution -- one supervised, appropriate when representative training data is available, and one unsupervised -- it also allows application developers to define their own integration techniques, as well as extending the pool of annotators as new ones become available.
One health informatics
Authors: Hans C. Ossebaard
Venue: WWW
Year: 2014

Zoonoses are a class of infectious diseases causing growing concern of health authorities worldwide. Human and economic costs of zoonoses are substantial, especially in low-resource countries. New zoonoses emerge as a consequence of ecological, demographic, cultural, social and behavioral factors. Meanwhile, global antimicrobial resistance increases. This public health threat demands for a new approach to which the concept of "One Health" is emblematical. It emphasizes the interconnectedness of human, animal and environmental health. To protect and improve public health it is imperative that transdisciplinary collaboration and communication takes place between the human and the veterinary domain. This strategy is now widely endorsed by international, regional and national health policy and academic bodies. Nonetheless the contributions of both the social sciences and the new data sciences need more appreciation. Evidence is available that the methods and concepts they provide can budge "One Health".
Atomic distributed transactions: a RESTful design
Authors: Guy Pardon, Cesare Pautasso
Venue: WWW
Year: 2014

The REST architectural style supports the reliable interaction of clients with a single server. However, no guarantees can be made for more complex interactions which require to atomically transfer state among resources distributed across multiple servers. In this paper we describe a lightweight design for transactional composition of RESTful services. The approach -- based on the Try-Cancel/Confirm (TCC) pattern -- does not require any extension to the HTTP protocol. The design assumes that resources are designed to comply with the TCC pattern and ensures that the resources involved in the transaction are not aware of it. It delegates the responsibility of achieving the atomicity of the transaction to a coordinator which exposes a RESTful API.
A semantic web of know-how: linked data for community-centric tasks
Authors: Paolo Pareti, Ewan Klein, Adam Barker
Venue: WWW
Year: 2014

This paper proposes a novel framework for representing community 'know-how' on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.
Spatial semantic search in location-based web services
Authors: Jeong-Hoon Park
Venue: WWW
Year: 2014

As GPS-enabled mobile devices have advanced, the location-based service(LBS) became one of the most active subjects in the Web-based services. Major Web-based services such as Google Picasa, Twitter, Facebook, and Flicker employ LBS as one of their main features. Consequently, a large number of geotagged documents are generated by users in the Web-based services. Recently, there have been studies on the spatial keyword search which aims to find a set of documents in the Web-based services by evaluating the spatial relevance and keyword relevance. It is a combination of the spatial search and keyword search, each of which has been studied for a long time.
Semantic annotation for dynamic web environment
Authors: Jeong-Hoon Park, Chin-Wan Chung
Venue: WWW
Year: 2014

The semantic Web is a promising future Web environment. In order to realize the semantic Web, the semantic annotation should be widely available. The studies for generating the semantic annotation do not provide a solution to the 'document evolution' requirement which is to maintain the consistency between semantic annotations and Web pages. In this paper, we propose an efficient solution to the requirement, that is to separately generate the long-term annotation and the short-term annotation. The experimental results show that our approach outperforms an existing approach which is the most efficient among the automatic approaches based on static Web pages.
CrowdFill: a system for collecting structured data from the crowd
Authors: Hyunjung Park, Jennifer Widom
Venue: WWW
Year: 2014

CrowdFill is a system for collecting structured data from the crowd. Unlike a typical microtask-based approach, CrowdFill shows an entire partially-filled table to all participating workers; workers collaboratively complete the table by filling in empty cells, as well as upvoting and downvoting data entered by other workers, using CrowdFill's intuitive data entry interface. CrowdFill ensures data entry is leading to a final table that satisfies prespecified constraints, and its compensation scheme encourages workers to submit useful, high-quality work. We demonstrate how CrowdFill collects structured data from the crowd, from the perspective of a user as well as from the perspective of workers.
WebAlive: a new paradigm for bringing things to life on the web
Authors: Rahul Parundekar
Venue: WWW
Year: 2014

In this poster, we propose WebAlive, a new paradigm that brings any Thing in the realm of reality to `life' on the Web by creating an entangled virtual existence for it. For example, consider a physical object like the lamp on your desk. As a human, you are aware of its existence. You can see it, touch it, feel it, interact with it (turn it on, o, etc.), investigate its attributes (color, material, etc.). You can even alter or trash it. Now, imagine that the lamp had a virtual counterpart on the Web such that the two of them were entangled with each other. A software agent on the Web can now become aware of its existence, it can perform actions on it, learn about its attributes and even (with the right capability) alter it just how it was accessible to you. Referring to the most broadest definition of the word Thing, we can extend this entanglement concept beyond physical things like devices, objects, people, etc. to all intelligible things like entities, processes, concepts, ideas, etc. By doing so, we can open a new realm of possibilities where we could have digital access to the world around us. This poster provides an introduction to the paradigm and presents an approach to realize it using Web standards.
Acquisition of open-domain classes via intersective semantics
Authors: Marius Pasca
Venue: WWW
Year: 2014

A weakly-supervised method acquires fine-grained class labels that do not occur verbatim in the input data or underlying text collection. The method generates more specific class labels (gold mining companies listed on the toronto stock exchange) that capture the semantics of the underlying classes, out of pairs of input class labels (companies listed on the toronto stock exchange, gold mining companies) available for an instance (Golden Star Resources). When applied to Wikipedia articles and their categories, the method generates new categories for existing articles, and expands existing categories with additional articles.
Learning joint representation for community question answering with tri-modal DBM
Authors: Baolin Peng, Wenge Rong, Yuanxin Ouyang, Chao Li, Zhang Xiong
Venue: WWW
Year: 2014

One of the main research tasks in Community question answering (CQA) is to find most relevant questions for a given new query, thereby providing useful knowledge for the users. Traditionally used methods such as bag-of-words or latent semantic models consider queries, questions and answers in a same feature space. However, the correlations among queries, questions and answers imply that they lie in different feature spaces. In light of these issues, we proposed a tri-modal deep boltzmann machine (tri-DBM) to extract unified representation for query, question and answer. Experiments on Yahoo! Answers dataset reveal using these unified representation to train a classifier judging semantic matching level between query and question outperforms models using bag-of-words or LSA representation significantly.
The COMPOSE API for the internet of things
Authors: Juan Luis Pérez, Álvaro Villalba, David Carrera, Iker Larizgoitia, Vlad Trifa
Venue: WWW
Year: 2014

The COMPOSE project aims to provide an open Marketplace for the Internet of Things as well as the necessary platform to support it. A necessary component of COMPOSE is an API that allows things, COMPOSE users and the platform to communicate. The COMPOSE API allows for things to push data to the platform, the platform to initiate asynchronous actions on the things, and COMPOSE users to retrieve and process data from the things. In this paper we present the design and implementation of the COMPOSE API, as well as a detailed description of the main key requirements that the API must satisfy. The API documentation and the source code for the platform are available online.
Attributed graph models: modeling network structure with correlated attributes
Authors: Joseph J. Pfeiffer III, Sebastián Moreno, Timothy La Fond, Jennifer Neville, Brian Gallagher
Venue: WWW
Year: 2014

Online social networks have become ubiquitous to today's society and the study of data from these networks has improved our understanding of the processes by which relationships form. Research in statistical relational learning focuses on methods to exploit correlations among the attributes of linked nodes to predict user characteristics with greater accuracy. Concurrently, research on generative graph models has primarily focused on modeling network structure without attributes, producing several models that are able to replicate structural characteristics of networks such as power law degree distributions or community structure. However, there has been little work on how to generate networks with real-world structural properties and correlated attributes. In this work, we present the Attributed Graph Model (AGM) framework to jointly model network structure and vertex attributes. Our framework learns the attribute correlations in the observed network and exploits a generative graph model, such as the Kronecker Product Graph Model (KPGM) and Chung Lu Graph Model (CL), to compute structural edge probabilities. AGM then combines the attribute correlations with the structural probabilities to sample networks conditioned on attribute values, while keeping the expected edge probabilities and degrees of the input graph model. We outline an efficient method for estimating the parameters of AGM, as well as a sampling method based on Accept-Reject sampling to generate edges with correlated attributes. We demonstrate the efficiency and accuracy of our AGM framework on two large real-world networks, showing that AGM scales to networks with hundreds of thousands of vertices, as well as having high attribute correlation.
Efficient CPU-GPU work sharing for data-parallel JavaScript workloads
Authors: Xianglan Piao, Channoh Kim, Younghwan Oh, Hanjun Kim, Jae W. Lee
Venue: WWW
Year: 2014

Modern web browsers are required to execute many complex, compute-intensive applications, mostly written in JavaScript. With widespread adoption of heterogeneous processors, recent JavaScript-based data-parallel programming models, such as River Trail and WebCL, support multiple types of processing elements including CPUs and GPUs. However, significant performance gains are still left on the table since the program kernel runs on only one compute device, typically selected at kernel invocation. This paper proposes a new framework for efficient work sharing between CPU and GPU for data-parallel JavaScript workloads. The work sharing scheduler partitions the input data into smaller chunks and dynamically dispatches them to both CPU and GPU for concurrent execution. For four data-parallel programs, our framework improves performance by up to 65% with a geometric mean speedup of 33% over GPU-only execution.
Seed selection for domain-specific search
Authors: Pattisapu Nikhil Priyatam, Ajay Dubey, Krish Perumal, Sai Praneeth, Dharmesh Kakadia, Vasudeva Varma
Venue: WWW
Year: 2014

The last two decades have witnessed an exponential rise in web content from a plethora of domains, which has necessitated the use of domain-specific search engines. Diversity of crawled content is one of the crucial aspects of a domain-specific search engine. To a large extent, diversity is governed by the initial set of seed URLs. Most of the existing approaches rely on manual effort for seed selection. In this work we automate this process using URLs posted on Twitter. We propose an algorithm to get a set of diverse seed URLs from a Twitter URL graph. We compare the performance of our approach against the baseline zero similarity seed selection method and find that our approach beats the baseline by a significant margin.
User profiles based on revisitation times
Authors: Philipp A. Pushnyakov, Gleb Gusev
Venue: WWW
Year: 2014

Our work is devoted to Web revisitation patterns of individual users. Everybody revisits Web pages, but their reasons for doing so can differ. We analyzed Web interaction logs of millions users to characterize how people revisit Web content. We revealed that each user have its own distribution of revisitation times. This distribution follows Power Law with some exponent, which captures specific user peculiarities.
Extraction and integration of web sources with humans and domain knowledge
Authors: Disheng Qiu, Lorenzo Luce
Venue: WWW
Year: 2014

The extraction and integration of data from many web sources in different domains is an open issue. Two promising solutions take on this challenge: top down approaches rely on a domain knowledge that is manually crafted by an expert to guide the process and bottom up approaches try to infer the schema from many web sources to make sense of the extracted data. The first solutions scale over the number of web sources, but for settings with different domains, an expert has to manually craft an ontology for each domain. The second solutions do not require a domain expert, but high quality is achieved only with a lot of human interactions both in the extraction and integration steps. We introduce a framework that takes the best from both approaches. The framework addresses synergically both extraction and integration of data from web sources. No domain expert is required, it exploits data from a seed knowledge base to enhance the automatic extraction and integration (top down). Human workers from crowdsourcing platforms are engaged to improve the quality and the coverage of the extracted data. The framework adopts techniques to automatically extract both the schema and the data from multiple web sources (bottom up). The extracted information is then used to bootstrap the seed knowledge base, reducing in this way the human effort for future tasks.
The pursuit of urban happiness
Authors: Daniele Quercia
Venue: WWW
Year: 2014

Cities are attracting considerable research interest. The agenda behind smart cities is popular among computer scientists and engineers: new monitoring technologies promise to allocate urban resources (e.g., electricity, clean water, car traffic) more efficiently and, as such, make our cities 'smarter'. This talk offers a rare counterpoint to that dominant efficiency-driven narrative. It is about recent research on the relationship between happiness and cities [1]: which urban elements make people happy? To help answer that question, I built a web game with collaborators at the University of Cambridge in which users are shown ten pairs of urban scenes of London and, for each pair, a user needs to choose which one they consider to be most beautiful, quiet, and happy. Based on user votes, we are able to rank all urban scenes according to these three attributes. We recently analyzed the scenes with ratings using image processing tools [2]. We discovered that the amount of greenery in any given scene is associated with all the three attributes and that cars and fortress-like buildings are associated with sadness (we equated sadness to our measurement for the low end of our 'spectrum' of happiness). In contrast, public gardens and Victorian and red brick houses are associated with happiness.
Evolutionary habits on the web
Authors: Daniele Quercia
Venue: WWW
Year: 2014

For the last few years, I and my colleagues have been exploring the complex relationship between our offline and online worlds. This talk will show that, as online platforms become mature, the social behavior we have evolved over thousands of years is reflected on our actions on the web as well. It turns out that, in the context of social influence, finding the "(special) many" (of those who are able to spot trends early one) is more important than trying to find the "special few"[10]; that people with different personality traits take on different roles on both Twitter and Facebook [5,6]; that language, with its vocabulary and prescribed ways of communicating, is a symbolic resource that can be used on its own to influence others [4]; and that a Facebook relationship is more likely to break if it is not embedded in the same social circle, if it is between two people whose ages differ, and if one of the two is neurotic or introvert [3]. Interestingly, we also found that a relationship with a common female friend is more robust than that with a common male friend. More recently, we have also explored the relationship between offline and online worlds in the urban context. We have considered hypotheses put forward in the 1970s urban sociology literature [1,2] and, for the first time, we have been able to test them at scale.We have done so by building two crowdsourcing web games: one crowdsources Londoners' mental images of the city [8], and the other crowdsources the discovery of the urban elements that make people happy [7]. We have found that, as opposed to well-to-do areas, those suffering from social problems are rarely present in residents' mental maps of the city, and they tend to be characterized more by cars and fortress-like buildings than by greenery. This talk will conclude by showing how combining both web games with Flickr offers interesting applications for discovering emotionally-pleasant routes [9] and for ranking city pictures [11].
RDF-X: a language for sanitizing RDF graphs
Authors: Jyothsna Rachapalli, Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham
Venue: WWW
Year: 2014

With the advent of Semantic Web and Resource Description Framework (RDF), the web is likely to witness an unprecedented wealth of knowledge, resulting from seamless integration of various data sources. Data integration is one of the key features of RDF, however, absence of secure means for managing sensitive RDF data may prevent sharing of critical data altogether or may cause serious damage. Towards this end we present a language for sanitizing RDF graphs, which comprises a set of sanitization operations that transform a graph by concealing the sensitive data. These operations are modeled into a new SPARQL query form known as SANITIZE, which can also be leveraged towards fine grained access control and building advanced anonymization features.
Incredible: is (almost) all web content trustworthy? analysis of psychological factors related to website credibility evaluation
Authors: Maria Rafalak, Katarzyna Abramczuk, Adam Wierzbicki
Venue: WWW
Year: 2014

This paper describes the results of a study conducted in February 2013 on Amazon Mechanical Turk aimed at identifying various determinants of credibility evaluations. 2046 adult participants evaluated credibility of websites with diversified trustworthiness reference index. We concentrated on psychological factors that lead to the characteristic positive bias observed in many working social feedback systems on the Internet. We have used International Personality Item Pool (IPIP) and measured the following traits: trust, conformity, risk taking, need for cognition and intellect. Results suggest that trustworthiness and risk taking are factors clearly differentiating people with respect to tendency to overestimate, underestimate and judge accordingly websites' credibility. Intuitively people characterized by high general trust tend to be more generous in their credibility evaluations. On the other hand, people who are more willing to take risk, tend to be more critical of the Internet content. The latter indicates that high credibility evaluations are being treated as a default option, and lower ratings require special conditions. Other, more detailed psychological patterns related to websites' credibility evaluations are described in full paper.
Testsuite and harness for browser based editing
Authors: Dave Raggett
Venue: WWW
Year: 2014

The Web is still awkward when it comes to online editing. It is time to fix that, and make it easier for developers to create smarter browser based editing tools. This short paper presents work on a test framework for a cross browser open source library for browser based editing. The aim is to encourage a proliferation of different kinds of browser based editing for a wide range of purposes. The library steps around the considerable variations in the details of browser support for designMode and contentEditable, which have acted as a brake on realizing the full potential for browser based editing.
Measuring the effectiveness of multi-channel marketing campaigns using online chatter
Authors: Haggai Roitman, Gilad Barkai, David Konopnicki, Aya Soffer
Venue: WWW
Year: 2014

Measuring the effectiveness of marketing campaigns across different channels is one of the most challenging tasks for today's brand marketers. Such measurement usually relies on a combination of key performance indicators (KPIs), used for assessing various aspects of marketing outcomes. Recently, with the availability of social-media sources, new options for collecting KPIs have become available and numerous social-media monitoring tools and services have emerged. Yet, given the vast media spectrum, which goes beyond social-media channels, existing solutions fail to generalize well and the curation of marketing performance KPIs for most marketing channels still relies on labor intensive means such as surveys and questionnaires. Trying to address the challenges, we propose to demonstrate a novel solution we have developed in IBM: Multi-channel Marketing Monitoring Platform (M3P for short). M3P is better tailored for the marketing performance domain, where online chatter is being harnessed for effective collection of meaningful marketing KPIs across all possible channels. We describe M3P's main challenges and review some of its novel KPIs. We then describe the M3P solution, focusing on its KPI extraction process. Finally, we describe the planned demonstration using a real-world marketing use-case.