OpenRaiser/Intern-Atlas huggingface dataset:
SourceRank: relevance and trust assessment for deep web sources based on inter-source agreement
Authors: Raju Balakrishnan, Subbarao Kambhampati
Venue: WWW
Year: 2011

One immediate challenge in searching the deep web databases is source selection - i.e. selecting the most relevant web databases for answering a given query. The existing database selection methods (both text and relational) assess the source quality based on the query-similarity-based relevance assessment. When applied to the deep web these methods have two deficiencies. First is that the methods are agnostic to the correctness (trustworthiness) of the sources. Secondly, the query based relevance does not consider the importance of the results. These two considerations are essential for the open collections like the deep web. Since a number of sources provide answers to any query, we conjuncture that the agreements between these answers are likely to be helpful in assessing the importance and the trustworthiness of the sources. We compute the agreement between the sources as the agreement of the answers returned. While computing the agreement, we also measure and compensate for possible collusion between the sources. This adjusted agreement is modeled as a graph with sources at the vertices. On this agreement graph, a quality score of a source that we call SourceRank, is calculated as the stationary visit probability of a random walk. We evaluate SourceRank in multiple domains, including sources in Google Base, with sizes up to 675 sources. We demonstrate that the SourceRank tracks source corruption. Further, our relevance evaluations show that SourceRank improves precision by 22-60% over the Google Base and the other baseline methods. SourceRank has been implemented in a system called Factal.
Factal: integrating deep web based on trust and relevance
Authors: Raju Balakrishnan, Subbarao Kambhampati
Venue: WWW
Year: 2011

We demonstrate "Factal"--a system for integrating deep web sources. Factal is based on the recently introduced source selection method "SourceRank"; which is a measure of trust and relevance based on the agreement between the sources. SourceRank selects popular and trustworthy sources from autonomous and open collections like the deep web. This trust and popularity awareness distinguishes Factal from the existing systems like Google Product Search. Factal selects and searches active online databases on multiple domains. The demonstration scenarios include improved trustworthiness, relevance of results, and comparison shopping. We believe that by incorporating effective source selection based on the SourceRank, Factal demonstrates a significant step towards a deep-web-scale integration system.
Context-sensitive query auto-completion
Authors: Ziv Bar-Yossef, Naama Kraus
Venue: WWW
Year: 2011

Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query.
Measuring the effectiveness of display advertising: a time series approach
Authors: Joel Barajas, Ram Akella, Marius Holtan, Jaimie Kwon, Brad Null
Venue: WWW
Year: 2011

We develop an approach for measuring the effectiveness of online display advertising at the campaign level. We present a Kalman filtering approach to deseasonalize and estimate the percentage changes of online sales on a daily basis. For this study, we analyze 3828 campaigns for 961 products on the Advertising.com network.
Choreography conformance via synchronizability
Authors: Samik Basu, Tevfik Bultan
Venue: WWW
Year: 2011

Choreography analysis has been a crucial problem in service oriented computing. Interactions among services involve message exchanges across organizational boundaries in a distributed computing environment, and in order to build such systems in a reliable manner, it is necessary to develop techniques for analyzing such interactions. Choreography conformance involves verifying that a set of services behave according to a given choreography specification that characterizes their interactions. Unfortunately this is an undecidable problem when services interact with asynchronous communication. In this paper we present techniques that identify if the interaction behavior for a set of services remain the same when asynchronous communication is replaced with synchronous communication. This is called the synchronizability problem and determining the synchronizability of a set of services has been an open problem for several years. We solve this problem in this paper. Our results can be used to identify synchronizable services for which choreography conformance can be checked efficiently. Our results on synchronizability are applicable to any software infrastructure that supports message-based interactions.
A middleware for securing mobile mashups
Authors: Florent Batard, Karima Boudaoud, Michel Riveill
Venue: WWW
Year: 2011

Mashups on traditional desktop devices are a well-known source of security risks. In this paper, we examine how these risks translate to mobile mashups and identify new risks caused by mobile-specific characteristics such as access to device features or offline operation. We describe the design of SCCM, a platform independent approach to handle the various mobile mashup security risks in a consistent and systematic manner. Evaluating an SCCM implementation for Android, we find that SCCM successfully protects against common attacks such as inserting a malicious widget from the outside.
USEWOD2011: 1st international workshop on usage analysis and the web of data
Authors: Bettina Berendt, Laura Hollink, Vera Hollink, Markus Luczak-Rösch, Knud Möller, David Vallet
Venue: WWW
Year: 2011

The USEWOD2011 workshop investigates combinations of usage data with semantics and the web of data. The analysis of usage data may be enhanced using semantic information. Now that more and more explicit knowledge is represented on the Web, the question arises how these semantics can be used to aid large scale web usage analysis and mining. Conversely, usage data analysis can enhance semantic resources as well as Semantic Web applications. Traces of users can be used to evaluate, adapt or personalize Semantic Web applications. Also, new ways of accessing information enabled by the Web of Data imply the need to develop or adapt algorithms, methods, and techniques to analyze and interpret the usage of Web data instead of Web pages. The USEWOD2011 program includes a challenge to the workshop participants: three months before the workshop two datasets consisting of server log files of Linked Open Data sources were released. Participants are invited to come up with interesting analyses, applications, alignments, etc. for these datasets.
Designing the web for an open society
Authors: Tim Berners-Lee
Venue: WWW
Year: 2011

How can we best design Web technology to support the features we would like of our society such as: openness, justice, transparency, accountability, participation, innovation, science and democracy?
Language independent identification of parallel sentences using Wikipedia
Authors: Rohit G. Bharadwaj, Vasudeva Varma
Venue: WWW
Year: 2011

This paper details a novel classification based approach to identify parallel sentences between two languages in a language independent way. We substitute the required language specific resources by the richly structured multilingual content, Wikipedia. Our approach is particularly useful to extract parallel sentences for under-resourced languages like most Indian and African languages, where resources are not readily available with necessary accuracies. We extract various statistics based on the cross lingual links present in Wikipedia and use them to generate feature vectors for each sentence pair. Binary classification of each pair of sentences into parallel or non-parallel has been done using these feature vectors. We achieved a precision upto 78% which is encouraging when compared to other state-of-art approaches.These results support our hypothesis of using Wikipedia to evaluate the parallel coefficient between sentences that can be used to build bilingual dictionaries.
Consideration set generation in commerce search
Authors: Sayan Bhattacharya, Sreenivas Gollapudi, Kamesh Munagala
Venue: WWW
Year: 2011

In commerce search, the set of products returned by a search engine often forms the basis for all user interactions leading up to a potential transaction on the web. Such a set of products is known as the consideration set. In this study, we consider the problem of generating consideration set of products in commerce search so as to maximize user satisfaction. To begin with, we model products as being associated with a set of important attributes and the user queries as composed of specified attributes. In this context, we require that the surfaced products be close to the specified attribute values in the query, and diverse with respect to the unspecified attributes. We cast this as a General Max-Sum Dispersion problem with arbitrary costs wherein we are given a set of n nodes in a metric space and the objective is to select a subset of nodes with total cost at most a given budget, and maximize the sum of the pairwise distances between the selected nodes. We give the first constant factor approximation algorithm for this problem, achieving an approximation ratio of 2. Further, we perform extensive empirical analysis on real-world data to show the effectiveness of our algorithm.
4th linked data on the web workshop (LDOW2011)
Authors: Christian Bizer, Tom Heath, Tim Berners-Lee, Michael Hausenblas
Venue: WWW
Year: 2011

The Web has developed into a global information space consisting not just of linked documents, but also of Linked Data. In 2010, we have seen significant growth in the size of the Web of Data, as well as in the number of communities contributing to its creation. In addition to publishing and interlinking datasets, there is intensive work on developing Linked Data browsers, Linked Data crawlers, Web of Data search engines and other applications that consume Linked Data from the Web.
Automatically building probabilistic databases from the web
Authors: Lorenzo Blanco, Mirko Bronzi, Valter Crescenzi, Paolo Merialdo, Paolo Papotti
Venue: WWW
Year: 2011

A relevant number of web sites publish structured data about recognizable concepts (such as stock quotes, movies, restau- rants, etc.). There is a great chance to create applications that rely on a huge amount of data taken from the Web. We present an automatic and domain independent system that performs all the steps required to benefit from these data: it discovers data intensive web sites containing information about an entity of interest, extracts and integrate the published data, and finally performs a probabilistic analysis to characterize the impreciseness of the data and the accuracy of the sources. The results of the processing can be used to populate a probabilistic database.
Highly efficient algorithms for structural clustering of large websites
Authors: Lorenzo Blanco, Nilesh N. Dalvi, Ashwin Machanavajjhala
Venue: WWW
Year: 2011

In this paper, we present a highly scalable algorithm for structurally clustering webpages for extraction. We show that, using only the URLs of the webpages and simple content features, it is possible to cluster webpages effectively and efficiently. At the heart of our techniques is a principled framework, based on the principles of information theory, that allows us to effectively leverage the URLs, and combine them with content and structural properties. Using an extensive evaluation over several large full websites, we demonstrate the effectiveness of our techniques, at a scale unattainable by previous techniques.
Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks
Authors: Paolo Boldi, Marco Rosa, Massimo Santini, Sebastiano Vigna
Venue: WWW
Year: 2011

We continue the line of research on graph compression started with WebGraph, but we move our focus to the compression of social networks in a proper sense (e.g., LiveJournal): the approaches that have been used for a long time to compress web graphs rely on a specific ordering of the nodes (lexicographical URL ordering) whose extension to general social networks is not trivial. In this paper, we propose a solution that mixes clusterings and orders, and devise a new algorithm, called Layered Label Propagation, that builds on previous work on scalable clustering and can be used to reorder very large graphs (billions of nodes). Our implementation uses task decomposition to perform aggressively on multi-core architecture, making it possible to reorder graphs of more than 600 millions nodes in a few hours. Experiments performed on a wide array of web graphs and social networks show that combining the order produced by the proposed algorithm with the WebGraph compression framework provides a major increase in compression with respect to all currently known techniques, both on web graphs and on social networks. These improvements make it possible to analyse in main memory significantly larger graphs.
From actors, politicians, to CEOs: domain adaptation of relational extractors using a latent relational mapping
Authors: Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka
Venue: WWW
Year: 2011

We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.
Towards liquid service oriented architectures
Authors: Daniele Bonetta, Cesare Pautasso
Venue: WWW
Year: 2011

The advent of Cloud computing platforms, and the growing pervasiveness of Multicore processor architectures have revealed the inadequateness of traditional programming models based on sequential computations, opening up many challenges for research on parallel programming models for building distributed, service-oriented systems. More in detail, the dynamic nature of Cloud computing and its virtualized infrastructure pose new challenges in term of application design, deployment and dynamic reconfiguration. An application developed to be delivered as a service in the Cloud has to deal with poorly understood issues such as elasticity, infinite scalability and portability across heterogeneous virtualized environments. In this position paper we define the problem of providing a novel parallel programming model for building application services that can be transparently deployed on multicore and cloud execution environments. To this end, we introduce and motivate a research plan for the definition of a novel programming framework for Web service-based applications. Our vision called "Liquid Architecture" is based on a programming model inspired by core ideas tied to the REST architectural style coupled with a self-configuring runtime that allows transparent deployment of Web services on a broad range of heterogeneous platforms, from multicores to clouds.
Exploratory search in multi-domain information spaces with liquid query
Authors: Alessandro Bozzon, Marco Brambilla, Stefano Ceri, Piero Fraternali, Salvatore Vadacca
Venue: WWW
Year: 2011

Search Computing (SeCo) aims at building search applications that bridge the gap between general-purpose and vertical search engines. SeCo queries extract ranked information about several interconnected domains, such as "hotels", "restaurants" or "concerts", by interacting with Web data sources which are wrapped as search services; an example of query is: "Find a good Jazz concert close to the user's current location, together with close-by good restaurants and hotels". The SeCo system supports the deployment of search applications, by providing a generic software architecture and the tools for service and query registration, for query formulation and execution, and for result browsing. In this demo paper, we focus on the Liquid Query (LQ) interface which supports the iteration over query formulation, result visualization and query refinement, with commands for perusing the result set, changing the visualization of data based on their type (e.g., geographical or temporal) and interacting with the remote search services. It also supports an exploratory search approach, where the user starts by accessing one data source (e.g., an event listing for finding interesting concerts), then is assisted in progressively joining other correlated sources in an interactive exploration of the search space. The exploration paths can be chosen on the fly and the navigation history can be browsed back and forth for cross-checking the retrieved options.
Efficiently evaluating graph constraints in content-based publish/subscribe
Authors: Andrei Z. Broder, Shirshanka Das, Marcus Fontoura, Bhaskar Ghosh, Vanja Josifovski, Jayavel Shanmugasundaram, Sergei Vassilvitskii
Venue: WWW
Year: 2011

We introduce the problem of evaluating graph constraints in content-based publish/subscribe (pub/sub) systems. This problem formulation extends traditional content-based pub/sub systems in the following manner: publishers and subscribers are connected via a (logical) directed graph G with node and edge constraints, which limits the set of valid paths between them. Such graph constraints can be used to model a Web advertising exchange (where there may be restrictions on how advertising networks can connect advertisers and publishers) and content delivery problems in social networks (where there may be restrictions on how information can be shared via the social graph). In this context, we develop efficient algorithms for evaluating graph constraints over arbitrary directed graphs G. We also present experimental results that demonstrate the effectiveness and scalability of the proposed algorithms using a realistic dataset from Yahoo!'s Web advertising exchange.
Efficient diversification of search results using query logs
Authors: Gabriele Capannini, Franco Maria Nardini, Raffaele Perego, Fabrizio Silvestri
Venue: WWW
Year: 2011

We study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different "specializations" of queries in query logs to detect the submission of ambiguous/faceted queries, and manage them by diversifying the search results returned in order to cover the different possible interpretations of the query. We present an original formulation of the results diversification problem in terms of an objective function to be maximized that admits the finding of an optimal solution in linear time.
LivePulse: tapping social media for sentiments in real-time
Authors: Malú Castellanos, Riddhiman Ghosh, Yue Lu, Lei Zhang, Perla Ruiz, Mohamed Dekhil, Umeshwar Dayal, Meichun Hsu
Venue: WWW
Year: 2011

The rise of Twitter, blogs, review sites and social sites has motivated people to express their opinions publicly and more frequently than ever before. This has fueled the emerging field known as sentiment analysis whose goal is to translate the vagaries of human emotion into hard data. LivePulse is a tool that taps into the growing business interest in what is being said online with the particular characteristic of doing so in real-time. LivePulse integrates novel algorithms for sentiment analysis and a configurable dashboard with different kinds of dynamic charts that change as new data is ingested. It also provides support to drill down and visually explore the sentiment scores to understand how they were computed and what are the emotions expressed about a given aspect or topic. Our tool has been researched and prototyped at HP Labs in close interaction with internal and external customers whose valuable feedback has been crucial for improving the tool. This paper presents an overview of LivePulse's architecture and functionality, and illustrates how it would be demoed.
Joint WICOW/AIRWeb workshop on web quality (WebQuality 2011)
Authors: Carlos Castillo, Zoltán Gyöngyi, Adam Jatowt, Katsumi Tanaka
Venue: WWW
Year: 2011

In this paper we overview the Joint WICOW/AIRWeb Workshop on Web Quality (WebQuality 2011) that was held in conjunction with the 20th International World Wide Web Conference in Hyderabad, India.
Buy-it-now or take-a-chance: a simple sequential screening mechanism
Authors: L. Elisa Celis, Gregory Lewis, Markus M. Möbius, Hamid Nazerzadeh
Venue: WWW
Year: 2011

We present a simple auction mechanism which extends the second-price auction with reserve and is truthful in expectation. This mechanism is particularly effective in private value environments where the distribution of valuations are irregular. Bidders can "buy-it-now", or alternatively "take-a-chance" where the top d bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of valuations, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve.
Dynamics of bidding in a P2P lending service: effects of herding and predicting loan success
Authors: Simla Ceyhan, Xiaolin Shi, Jure Leskovec
Venue: WWW
Year: 2011

Online peer-to-peer (P2P) lending services are a new type of social platform that enables individuals borrow and lend money directly from one to another. In this paper, we study the dynamics of bidding behavior in a P2P loan auction website, Prosper.com. We investigate the change of various attributes of loan requesting listings over time, such as the interest rate and the number of bids. We observe that there is herding behavior during bidding, and for most of the listings, the numbers of bids they receive reach spikes at very similar time points. We explain these phenomena by showing that there are economic and social factors that lenders take into account when deciding to bid on a listing. We also observe that the profits the lenders make are tied with their bidding preferences. Finally, we build a model based on the temporal progression of the bidding, that reliably predicts the success of a loan request listing, as well as whether a loan will be paid back or not.
EntityTagger: automatically tagging entities with descriptive phrases
Authors: Kaushik Chakrabarti, Surajit Chaudhuri, Tao Cheng, Dong Xin
Venue: WWW
Year: 2011

We consider the problem of entity tagging: given one or more named entities from a specific domain, the goal is to automatically associate descriptive phrases, referred to as etags (entity tags), to each entity. Consider a product catalog containing product names and possibly short descriptions. For a product in the catalog, say Ricoh G600 Digital Camera, we want to associate etags such as "water resistant", "rugged" and "outdoor" to it, even though its name or description does not mention those phrases. Entity tagging can enable more effective search over entities. We propose to leverage signals in web documents to perform such tagging. We develop techniques to perform such tagging in a domain independent manner while ensuring high precision and high recall.