OpenRaiser/Intern-Atlas huggingface dataset:
Second international workshop on RESTful design (WS-REST 2011)
Authors: Cesare Pautasso, Erik Wilde, Rosa Alarcón
Venue: WWW
Year: 2011

Over the past few years, the discussion between the two major architectural styles for designing and implementing Web services, the RPC-oriented approach and the resource-oriented approach, has been mainly held outside of traditional research communities. Mailing lists, forums and developer communities have seen long and fascinating debates around the assumptions, strengths, and weaknesses of these two approaches. The Second International Workshop on RESTful Design (WS-REST 2011) has the goal of getting more researchers involved in the debate by providing a forum where discussions around the resource-oriented style of Web services design take place. Representational State Transfer (REST) is an architectural style and as such can be applied in different ways, can be extended by additional constraints, or can be specialized with more specific interaction patterns. WS-REST is the premier forum for discussing research ideas, novel applications and results centered around REST at the World Wide Web conference, which provides a great setting to host this second edition of the workshop dedicated to research on the architectural style underlying the Web.
Model characterization curves for federated search using click-logs: predicting user engagement metrics for the span of feasible operating points
Authors: Ashok Kumar Ponnuswami, Kumaresh Pattabiraman, Desmond Brand, Tapas Kanungo
Venue: WWW
Year: 2011

Modern day federated search engines aggregate heterogeneous types of results from multiple vertical search engines and compose a single search engine result page (SERP). The search engine aggregates the results and produces one ranked list, constraining the vertical results to specific slots on the SERP. The usual way to compare two ranking algorithms is to first fix their operating points (internal thresholds), and then run an online experiment that lasts multiple weeks. Online user engagement metrics are then compared to decide which algorithm is better. However, this method does not characterize and compare the behavior over the entire span of operating points. Furthermore, this time-consuming approach is not practical if we have to conduct the experiment over numerous operating points. In this paper we propose a method of characterizing the performance of models that allows us to predict answers to "what if" questions about online user engagement using click-logs over the entire span of feasible operating points. We audition verticals at various slots on the SERP and generate click-logs. This log is then used to create operating curves between variables of interest (for example between result quality and click-through). The operating point for the system then can be chosen to achieve a specific trade-off between the variables. We apply this methodology to predict i) the online performance of two different models, ii) the impact of changing internal quality thresholds on clickthrough, iii) the behavior of introducing a new feature, iv) which machine learning loss function will give better online engagement, v) the impact of sampling distribution of head and tail queries in the training process. The results are reported on a well-known federated search engine. We validate the predictions with online experiments.
A framework for evaluating network measures for functional importance
Authors: Tieyun Qian, Qing Li, Jaideep Srivastava
Venue: WWW
Year: 2011

Many metrics such as degree, closeness, and PageRank have been introduced to determine the relative importance of a node within a network. The desired function of a network, however, is domain-specific. For example, the robustness can be crucial for a communication network, while efficiency is more preferred for fast spreading of advertisements in viral marketing. The information provided by some widely used measures are often conflicting under such varying demands. In this paper, we present a novel framework for evaluating network metrics regarding typical functional requirements. We also propose an analysis of five well established measures to compare their performance of ranking nodes on functional importance in a real-life network.
Towards identifying arguments in Wikipedia pages
Authors: Hoda Sepehri Rad, Denilson Barbosa
Venue: WWW
Year: 2011

Wikipedia is one of the most widely used repositories of human knowledge today, contributed mostly by a few hundred thousand regular editors. In this open environment, inevitably, differences of opinion arise among editors of the same article. Especially for polemical topics such as religion and politics, difference of opinions among editors may lead to intense "edit wars" in which editors compete to have their opinions and points of view accepted. While such disputes can compromise the reliability of the article (or at least portions of it), they are recorded in the edit history of the articles. We posit that exposing such disputes to the reader, and pointing to the portions of the text where they manifest most prominently can be beneficial in helping concerned readers in understanding such topics. In this paper, we discuss our initial efforts towards the problem of automatic evaluation of extracting controversial points in Wikipedia pages.
A word at a time: computing word relatedness using temporal semantic analysis
Authors: Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, Shaul Markovitch
Venue: WWW
Year: 2011

Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as "war" and "peace" might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represented as time series over a corpus of temporally-ordered documents. To the best of our knowledge, this is the first attempt to incorporate temporal evidence into models of semantic relatedness. Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks.
Comparative study of clustering techniques for short text documents
Authors: Aniket Rangrej, Sayali Kulkarni, Ashish V. Tendulkar
Venue: WWW
Year: 2011

We compare various document clustering techniques including K-means, SVD-based method and a graph-based approach and their performance on short text data collected from Twitter. We define a measure for evaluating the cluster error with these techniques. Observations show that graph-based approach using affinity propagation performs best in clustering short text data with minimal cluster error.
Connecting the next billion web users
Authors: Rajeev Rastogi, Ed Cutrell, Manish Gupta, Ashok Jhunjhunwala, Ramkumar Narayan, Rajeev Sanghal
Venue: WWW
Year: 2011

With 2 billion users, the World Wide Web has indeed come a long way. However, of the 4.8 billion people living in Asia and Africa, only 1 in 5 has access to the Web. For instance, in India, the 100 million Web users constitute less than 10% of the total population of 1.2 billion. So it is universally accepted that the next billion users will come from emerging markets like Brazil, China, India, Indonesia and Russia. Emerging markets have a number of unique characteristics: Large dense populations with low incomes, Lack of infrastructure in terms of broadband, electricity, etc., Poor PC penetration due to limited affordability, High illiteracy rates and inability to read/write, Plethora of local languages and dialects, General paucity of local content, especially in local languages, Explosive growth in the number of mobile phones. The panel will debate the various technical challenges in overcoming the digital divide, and potential approaches to bring the Web to the underserved populations of the developing world.
Supporting synchronous social q&a throughout the question lifecycle
Authors: Matthew Richardson, Ryen W. White
Venue: WWW
Year: 2011

Synchronous social Q&A systems exist on the Web and in the enterprise to connect people with questions to people with answers in real-time. In such systems, askers' desire for quick answers is in tension with costs associated with interrupting numerous candidate answerers per question. Supporting users of synchronous social Q&A systems at various points in the question lifecycle (from conception to answer) helps askers make informed decisions about the likelihood of question success and helps answerers face fewer interruptions. For example, predicting that a question will not be well answered may lead the asker to rephrase or retract the question. Similarly, predicting that an answer is not forthcoming during the dialog can prompt system behaviors such as finding other answerers to join the conversation. As another example, predictions of asker satisfaction can be assigned to completed conversations and used for later retrieval.
DIDO: a disease-determinants ontology from web sources
Authors: Victoria Nebot Romero, Min Ye, Mario Albrecht, Jae-Hong Eom, Gerhard Weikum
Venue: WWW
Year: 2011

This paper introduces DIDO, a system providing convenient access to knowledge about factors involved in human diseases, automatically extracted from textual Web sources. The knowledge base is bootstrapped by integrating entities from hand-crafted sources like MeSH and OMIM. As these are short on relationships between dierent types of biomedical entities, DIDO employs flexible and robust pattern learning and constraint-based reasoning methods to automatically extract new relational facts from textual sources. These facts can then be iteratively added to the knowledge base. The result is a semantic graph of typed entities and relations between diseases, their symptoms, and their factors, with emphasis on environmental factors but covering also molecular determinants. We demonstrate the value of DIDO for knowledge discovery about causal factors and properties of complex diseases, including factor-disease chains.
Adapting a map query interface for a gesturing touch screen interface
Authors: Hanan Samet, Benjamin E. Teitler, Marco D. Adelfio, Michael D. Lieberman
Venue: WWW
Year: 2011

NewsStand is an example application of a general framework that we are developing to enable searching for information using a map query interface, where the information results from monitoring the output of over 8,000 RSS news sources and is available for retrieval within minutes of publication. The user interface of NewsStand was recently adapted so that NewsStand can execute on mobile and tablet devices with a gesturing touch screen interface such as the iPhone, iPod Touch, and iPad. This action led to a discovery of some shortcomings of current mapping APIs as well as devising some interesting new widgets. These issues are discussed, and the realization can be seen by a demo at http://newsstand.umiacs.umd.edu on any of the above Apple devices as well as other devices that support gestures such as an Android phone.
Web information extraction using Markov logic networks
Authors: Sandeepkumar Satpal, Sahely Bhadra, Sundararajan Sellamanickam, Rajeev Rastogi, Prithviraj Sen
Venue: WWW
Year: 2011

In this paper, we consider the problem of extracting structured data from web pages taking into account both the content of individual attributes as well as the structure of pages and sites. We use Markov Logic Networks (MLNs) to capture both content and structural features in a single unified framework, and this enables us to perform more accurate inference. We show that inference in our information extraction scenario reduces to solving an instance of the maximum weight subgraph problem. We develop specialized procedures for solving the maximum subgraph variants that are far more efficient than previously proposed inference methods for MLNs that solve variants of MAX-SAT. Experiments with real-life datasets demonstrate the effectiveness of our approach.
Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades
Authors: Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Jon Crowcroft
Venue: WWW
Year: 2011

Providers such as YouTube offer easy access to multimedia content to millions, generating high bandwidth and storage demand on the Content Delivery Networks they rely upon. More and more, the diffusion of this content happens on online social networks such as Facebook and Twitter, where social cascades can be observed when users increasingly repost links they have received from others.<br/><br/>In this paper we describe how geographic information extracted from social cascades can be exploited to improve caching of multimedia files in a Content Delivery Network. We take advantage of the fact that social cascades can propagate in a geographically limited area to discern whether an item is spreading locally or globally. This informs cache replacement policies, which utilize this information to ensure that content relevant to a cascade is kept close to the users who may be interested in it.<br/><br/>We validate our approach by using a novel dataset which combines social interaction data with geographic information: we track social cascades of YouTube links over Twitter and build a proof-of-concept geographic model of a realistic distributed Content Delivery Network. Our performance evaluation shows that we are able to improve cache hits with respect to cache policies without geographic and social information.
The OXPath to success in the deep web
Authors: Andrew Jon Sellers
Venue: WWW
Year: 2011

The world wide web provides access to a wealth of data. Collecting and maintaining such large amounts of data necessitates automated processing for extraction, since appropriate automation can perform extraction tasks that would be otherwise infeasible. Modern web interfaces, however, are generally designed primarily for human users, delivering sophisticated interactions through the use of client-side scripting and asynchronous server communication. To this end, we introduce OXPath, a careful extension of XPath that facilitates data extraction from the deep web. OXPath exploits XPath's familiarity and theoretical foundations. OXPath, then, achieves favourable evaluation complexity and optimal page buffering, storing only a constant number of pages for non-recursive queries. Further, OXPath provides a lightweight interface, which is easy to use and embed. This paper outlines the motivation, theoretical framework, current implementation, and preliminary results obtained so far. We conclude with proposed future work on OXPath, including an investigation of how to deploy OXPath efficiently in a highly elastic computing framework (cloud).
OXPath: little language, little memory, great value
Authors: Andrew Jon Sellers, Tim Furche, Georg Gottlob, Giovanni Grasso, Christian Schallhart
Venue: WWW
Year: 2011

Data about everything is readily available on the web-but often only accessible through elaborate user interactions. For automated decision support, extracting that data is essential, but infeasible with existing heavy-weight data extraction systems. In this demonstration, we present OXPath, a novel approach to web extraction, with a system that supports informed job selection and integrates information from several different web sites. By carefully extending XPath, OXPath exploits its familiarity and provides a light-weight interface, which is easy to use and embed. We highlight how OXPath guarantees optimal page buffering, storing only a constant number of pages for non-recursive queries.
CONQUER: a system for efficient context-aware query suggestions
Authors: Christian Sengstock, Michael Gertz
Venue: WWW
Year: 2011

Many of today's search engines provide autocompletion while the user is typing a query string. This type of dynamic query suggestion can help users to formulate queries that better represent their search intent during Web search interactions. In this paper, we demonstrate our query suggestion system called CONQUER, which allows to efficiently suggest queries for a given partial query and a number of available query context observations. The context-awareness allows for suggesting queries tailored to a given context, e.g., the user location or the time of day. CONQUER uses a suggestion model that is based on the combined probabilities of sequential query patterns and context observations. For this, the weight of a context in a query suggestion can be adjusted online, for example, based on the learned user behavior or user profiles. We demonstrate the functionality of CONQUER based on 6 million queries from an AOL query log using the time of day and the country domain of the clicked URLs in the search result as context observations.
Generalized link suggestions via web site clustering
Authors: Jangwon Seo, Fernando Diaz, Evgeniy Gabrilovich, Vanja Josifovski, Bo Pang
Venue: WWW
Year: 2011

Proactive link suggestion leads to improved user experience by allowing users to reach relevant information with fewer clicks, fewer pages to read, or simply faster because the right pages are prefetched just in time. In this paper we tackle two new scenarios for link suggestion, which were not covered in prior work owing to scarcity of historical browsing data. In the web search scenario, we propose a method for generating quick links - additional entry points into Web sites, which are shown for top search results for navigational queries - for tail sites, for which little browsing statistics is available. Beyond Web search, we also propose a method for link suggestion in general web browsing, effectively anticipating the next link to be followed by the user. Our approach performs clustering of Web sites in order to aggregate information across multiple sites, and enables relevant link suggestion for virtually any site, including tail sites and brand new sites for which little historical data is available. Empirical evaluation confirms the validity of our method using editorially labeled data as well as real-life search and browsing data from a major US search engine.
How to choose combinations in a join of search results
Authors: Mirit Shalem, Yaron Kanza
Venue: WWW
Year: 2011

We present novel measures for estimating the effectiveness of duplication-removal operations over a join of ranked lists. We introduce a duplication-removal approach, namely optimality rank, that outperforms existing approaches, according to the new measures.
REACTOR: a framework for semantic relation extraction and tagging over enterprise data
Authors: Wei Shen, Jianyong Wang, Ping Luo, Min Wang, Conglei Yao
Venue: WWW
Year: 2011

Relation extraction from Web data has attracted a lot of attention in recent years. However, little work has been done when it comes to relation extraction from enterprise data regardless of the urgent needs to such work in real applications (e.g., E-discovery). In this paper, we propose a novel unsupervised hybrid framework, called REACTOR (abbreviated for a fRamework for sEmantic relAtion extraCtion and Tagging Over enteRprise data). We evaluate REACTOR over a real-world enterprise data set and empirical results show the effectiveness of REACTOR.
Sparse hidden-dynamics conditional random fields for user intent understanding
Authors: Yelong Shen, Jun Yan, Shuicheng Yan, Lei Ji, Ning Liu, Zheng Chen
Venue: WWW
Year: 2011

Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines. However, due to the huge number of user behavior variables and coarse level intent labels defined by human editors, it is very difficult to directly model user behavioral dynamics or user intent dynamics in user search sessions. In this paper, we propose a novel Sparse Hidden-Dynamic Conditional Random Fields (SHDCRF) model for user intent learning from their search sessions. Through incorporating the proposed hidden state variables, SHDCRF aims to learn a substructure, i.e. a set of related hidden variables, for each intent label and they are used to model the intermediate dynamics between user intent labels and user behavioral variables. In addition, SHDCRF learns a sparse relation between the hidden variables and intent labels to make the hidden state variables explainable. Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that those classical solutions such as Support Vector Machine (SVM), Conditional Random Field (CRF) and Latnet-Dynamic Conditional Random Field (LDCRF).
Cooperative anti-spam system based on multilayer agents
Authors: Wenxuan Shi, Maoqiang Xie, Yalou Huang
Venue: WWW
Year: 2011

Spam is unsolicited bulk email which is extremely annoying to the recipients and the ISPs. However, most of the traditional spam filtering methods commonly neglect the bulk character of spam. This paper proposes a model of cooperative anti-spam system based on multilayer agents. We compared our model to the state-of-the-art and found that our model achieved better performance and robustness on several known corpora.
Harnessing the wisdom of crowds: video event detection based on synchronous comments
Authors: Xingtian Shi, Zhenglu Yang, Masashi Toyoda, Masaru Kitsuregawa
Venue: WWW
Year: 2011

With recent explosive growth of number of videos on Web, it becomes more important to facilitate users' demand for locating their preferred event clips in lengthy and voluminous programs. Although there has been a great deal of study on generic event detection in recent years, performance of existing approaches is still far from satisfactory. In this paper, we propose an integrated framework for general event detection. The key idea is that we utilize synchronous comments to segment video into clips with semantic text analysis, while taking into account relationship between users who write comments. By borrowing power of the wisdom of crowds, we experimentally demonstrate that our approach can effectively detect video events.
ReadAlong: reading articles and comments together
Authors: Dyut Kumar Sil, Srinivasan H. Sengamedu, Chiranjib Bhattacharyya
Venue: WWW
Year: 2011

We propose a new paradigm for displaying comments: showing comments alongside parts of the article they correspond to. We evaluate the effectiveness of various approaches for this task and show that a combination of bag of words and topic models performs the best.
DiversiWeb 2011: first international workshop on knowledge diversity on the web
Authors: Elena Simperl, Devika P. Madalli, Denny Vrandecic, Enrique Alfonseca
Venue: WWW
Year: 2011

The workshop provides an interdisciplinary forum for researchers and practitioners to present and discuss their ideas related to the challenges posed by diversity on the Web. We address a wide array of interdisciplinary questions, which need to be tackled in order to preserve the fragile balance between a world that is continually converging and growing together, the rich diversity of the global society, and the dangers of fragmentation and splintering.