OpenRaiser/Intern-Atlas huggingface dataset:
Triplify: light-weight linked data publication from relational databases
Authors: Sören Auer, Sebastian Dietzold, Jens Lehmann, Sebastian Hellmann, David Aumueller
Venue: WWW
Year: 2009

In this paper we present Triplify - a simplistic but effective approach to publish Linked Data from relational databases. Triplify is based on mapping HTTP-URI requests onto relational database queries. Triplify transforms the resulting relations into RDF statements and publishes the data on the Web in various RDF serializations, in particular as Linked Data. The rationale for developing Triplify is that the largest part of information on the Web is already stored in structured form, often as data contained in relational databases, but usually published by Web applications only as HTML mixing structure, layout and content. In order to reveal the pure structured information behind the current Web, we have implemented Triplify as a light-weight software component, which can be easily integrated into and deployed by the numerous, widely installed Web applications. Our approach includes a method for publishing update logs to enable incremental crawling of linked data sources. Triplify is complemented by a library of configurations for common relational schemata and a REST-enabled data source registry. Triplify configurations containing mappings are provided for many popular Web applications, including osCommerce, WordPress, Drupal, Gallery, and phpBB. We will show that despite its light-weight architecture Triplify is usable to publish very large datasets, such as 160GB of geo data from the OpenStreetMap project.
Estimating the impressionrank of web pages
Authors: Ziv Bar-Yossef, Maxim Gurevich
Venue: WWW
Year: 2009

The ImpressionRank of a web page (or, more generally, of a web site) is the number of times users viewed the page while browsing search results. ImpressionRank captures the visibility of pages and sites in search engines and is thus an important measure, which is of interest to web site owners, competitors, market analysts, and end users.
Search shortcuts: driving users towards their goals
Authors: Ranieri Baraglia, Fidel Cacheda, Victor Carneiro, Vreixo Formoso, Raffaele Perego, Fabrizio Silvestri
Venue: WWW
Year: 2009

Giving suggestions to users of Web-based services is a common practice aimed at enhancing their navigation experience. Major Web Search Engines usually provide "Suggestions" under the form of queries that are, to some extent, related to the current query typed by the user, and the knowledge learned from the past usage of the system. In this work we introduce "Search Shortcuts" as "Successful" queries allowed, in the past, users to satisfy their information needs. Differently from conventional suggestion techniques, our search shortcuts allows to evaluate effectiveness by exploiting a simple train-and-test approach. We have applied several Collaborative Filtering algorithms to this problem, evaluating them on a real query log data. We generate the shortcuts from all user sessions belonging to the testing set, and measure the quality of the shortcuts suggested by considering the similarity between them and the navigational user behavior.
Smart Miner: a new framework for mining large scale web usage data
Authors: Murat Ali Bayir, Ismail Hakki Toroslu, Ahmet Cosar, Guven Fidan
Venue: WWW
Year: 2009

In this paper, we propose a novel framework called Smart-Miner for web usage mining problem which uses link information for producing accurate user sessions and frequent navigation patterns. Unlike the simple session concepts in the time and navigation based approaches, where sessions are sequences of web pages requested from the server or viewed in the browser, Smart Miner sessions are set of paths traversed in the web graph that corresponds to users' navigations among web pages. We have modeled session construction as a new graph problem and utilized a new algorithm, Smart-SRA, to solve this problem efficiently. For the pattern discovery phase, we have developed an efficient version of the Apriori-All technique which uses the structure of web graph to increase the performance. From the experiments that we have performed on both real and simulated data, we have observed that Smart-Miner produces at least 30% more accurate web usage patterns than other approaches including previous session construction methods. We have also studied the effect of having the referrer information in the web server logs to show that different versions of Smart-SRA produce similar results. Our another contribution is that we have implemented distributed version of the Smart Miner framework by employing Map/Reduce Paradigm. We conclude that we can efficiently process terabytes of web server logs belonging to multiple web sites by our scalable framework.
Learning consensus opinion: mining data from a labeling game
Authors: Paul N. Bennett, David Maxwell Chickering, Anton Mityagin
Venue: WWW
Year: 2009

We consider the problem of identifying the consensus ranking for the results of a query, given preferences among those results from a set of individual users. Once consensus rankings are identified for a set of queries, these rankings can serve for both evaluation and training of retrieval and learning systems. We present a novel approach to collecting the individual user preferences over image-search results: we use a collaborative game in which players are rewarded for agreeing on which image result is best for a query. Our approach is distinct from other labeling games because we are able to elicit directly the preferences of interest with respect to image queries extracted from query logs. As a source of relevance judgments, this data provides a useful complement to click data. Furthermore, the data is free of positional biases and is collected by the game without the risk of frustrating users with non-relevant results; this risk is prevalent in standard mechanisms for debiasing clicks. We describe data collected over 34 days from a deployed version of this game that amounts to about 18 million expressed preferences between pairs. Finally, we present several approaches to modeling this data in order to extract the consensus rankings from the preferences and better sort the search results for targeted queries.
Deriving music theme annotations from user tags
Authors: Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu
Venue: WWW
Year: 2009

Music theme annotations would be really beneficial for supporting retrieval, but are often neglected by users while annotating. Thus, in order to support users in tagging and to fill the gaps in the tag space, in this paper we develop algorithms for recommending theme annotations. Our methods exploit already existing user tags, the lyrics of music tracks, as well as combinations of both. We compare the results for our recommended theme annotations against genre and style recommendations – a much easier and already studied task. We evaluate the quality of our recommended tags against an expert ground truth data set. Our results are promising and provide interesting insights into possible extensions for music tagging systems to support music search.
Scrolling behaviour with single- and multi-column layout
Authors: Cameron Braganza, Kim Marriott, Peter Moulder, Michael Wybrow, Tim Dwyer
Venue: WWW
Year: 2009

The standard layout model used by web browsers is to lay text out in a vertical scroll using a single column. The horizontal-scroll layout model--in which text is laid out in columns whose height is set to that of the browser window and the viewer scrolls horizontally - seems well-suited to multi-column layout on electronic devices. We describe a study that examines how people read and, in particular, the strategies they use for scrolling with these two models when reading large textual documents on a standard computer monitor. We compare usability of the models and evaluate both user preferences and the effect of the model on performance. Also interesting is the description of the browser and its user interface which we used for the study.
Online expansion of rare queries for sponsored search
Authors: Andrei Z. Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Donald Metzler, Lance Riedel, Jeffrey Yuan
Venue: WWW
Year: 2009

Sponsored search systems are tasked with matching queries
Raise semantics at the user level for dynamic and interactive SOA-based portals
Authors: Jean-Sébastien Brunner, Patrick Gatellier
Venue: WWW
Year: 2009

In this paper, we describe the fully dynamic semantic portal we implemented, integrating Semantic Web technologies and Service Oriented Architecture (SOA). The goals of the portal are twofold: first it helps administrators to easily propose new features in the portal using semantics to ease the orchestration process; secondly it automatically generates a customized user interface for these scenarios. This user interface takes into account different devices and assists end-users in the use of the portal taking benefit of context awareness. All the added-value of this portal is based on a core semantics defined by an ontology. We present here the main features of this portal and how it was implemented using state-of-the-art technologies and frameworks.
A measurement-driven analysis of information propagation in the flickr social network
Authors: Meeyoung Cha, Alan Mislove, P. Krishna Gummadi
Venue: WWW
Year: 2009

Online social networking sites like MySpace, Facebook, and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to viral marketing techniques that attempt to spread content, products, and ideas on these sites. However, there is little data publicly available on viral propagation in the real world and few studies have characterized how information spreads over current online social networks.
Quicklink selection for navigational query results
Authors: Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
Venue: WWW
Year: 2009

Quicklinks for a website are navigational shortcuts displayed below the website homepage on a search results page, and that let the users directly jump to selected points inside the website. Since the real-estate on a search results page is constrained and valuable, picking the best set of quicklinks to maximize the benefits for a majority of the users becomes an important problem for search engines. Using user browsing trails obtained from browser toolbars, and a simple probabilistic model, we formulate the quicklink selection problem as a combinatorial optimizaton problem. We first demonstrate the hardness of the objective, and then propose an algorithm that is provably within a factor of (1 − 1/e) of the optimal. We also propose a different algorithm that works on trees and that can find the optimal solution; unlike the previous algorithm, this algorithm can incorporate natural constraints on the set of chosen quicklinks. The efficacy of our methods is demonstrated via empirical results on both a manually labeled set of websites and a set for which quicklink click-through rates for several webpages were obtained from a real-world search engine.
Retaining personal expression for social search
Authors: Praphul Chandra, Ajay Gupta
Venue: WWW
Year: 2009

Web is being extensively used for personal expression, which includes ratings, reviews, recommendations, blogs. This user created content, e.g. book review on Amazon.com, becomes the property of the website, and the user often does not have easy access to it. In some cases, user's feedback may get averaged with feedback from other users e.g. ratings of a video. We argue that the creator of such content needs to be able to retain (a link to) her created content. We introduce the concept of MEB which is a user controlled store of such retained links. A MEB allows a user to access/share all the reviews she has given on different websites. With this capability users can allow their friends to search through their feedback. Searching through one's social network allows harnessing the power of social networks where known relationships provide the context & trust necessary to interpret feedback.
A dynamic bayesian network click model for web search ranking
Authors: Olivier Chapelle, Ya Zhang
Venue: WWW
Year: 2009

As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.
Collaborative filtering for orkut communities: discovery of user latent behavior
Authors: WenYen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, Edward Y. Chang
Venue: WWW
Year: 2009

Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective community recommendations in order to meet more users. In this paper, we investigate two algorithms from very different domains and evaluate their effectiveness for personalized community recommendation. First is association rule mining (ARM), which discovers associations between sets of communities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommendations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492,104 users and 118,002 communities. Our empirical comparisons using the top-k recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities. However, for recommendation lists of up to 3 communities, ARM is still a bit better. We analyze examples of the latent information learned by LDA to explain this finding. To efficiently handle the large-scale data set, we parallelize LDA on distributed computers and demonstrate our parallel implementation's scalability with varying numbers of machines.
RuralCafe: web search in the rural developing world
Authors: Jay Chen, Lakshminarayanan Subramanian, Jinyang Li
Venue: WWW
Year: 2009

The majority of people in rural developing regions do not have access to the World Wide Web. Traditional network connectivity technologies have proven to be prohibitively expensive in these areas. The emergence of new long-range wireless technologies provide hope for connecting these rural regions to the Internet. However, the network connectivity provided by these new solutions are by nature intermittent due to high network usage rates, frequent power-cuts and the use of delay tolerant links. Typical applications, especially interactive applications like web search, do not tolerate intermittent connectivity. In this paper, we present the design and implementation of RuralCafe, a system intended to support efficient web search over intermittent networks. RuralCafe enables users to perform web search asynchronously and find what they are looking for in one round of intermittency as opposed to multiple rounds of search/downloads. RuralCafe does this by providing an expanded search query interface which allows a user to specify additional query terms to maximize the utility of the results returned by a search query. Given knowledge of the limited available network resources, RuralCafe performs optimizations to prefetch pages to best satisfy a search query based on a user's search preferences. In addition, RuralCafe does not require modifications to the web browser, and can provide single round search results tailored to various types of networks and economic constraints. We have implemented and evaluated the effectiveness of RuralCafe using queries from logs made to a large search engine, queries made by users in an intermittent setting, and live queries from a small testbed deployment. We have also deployed a prototype of RuralCafe in Kerala, India.
Discovering the staring people from social networks
Authors: Dewei Chen, Jie Tang, Juanzi Li, Lizhu Zhou
Venue: WWW
Year: 2009

In this paper, we study a novel problem of staring people discovery from social networks, which is concerned with finding people who are not only authoritative but also sociable in the social network. We formalize this problem as an optimization programming problem. Taking the co-author network as a case study, we define three objective functions and propose two methods to combine these objective functions. A genetic algorithm based method is further presented to solve this problem. Experimental results show that the proposed solution can effectively find the staring people from social networks.
Web image retrieval reranking with multi-view clustering
Authors: Mingmin Chi, Peiwu Zhang, Yingbin Zhao, Rui Feng, Xiangyang Xue
Venue: WWW
Year: 2009

General image retrieval is often carried out by a text-based search engine, such as Google Image Search. In this case, natural language queries are used as input to the search engine. Usually, the user queries are quite ambiguous and the returned results are not well-organized as the ranking often done by the popularity of an image. In order to address these problems, we propose to use both textual and visual contents of retrieved images to reRank web retrieved results. In particular, a machine learning technique, a multi-view clustering algorithm is proposed to reorganize the original results provided by the text-based search engine. Preliminary results validate the effectiveness of the proposed framework.
Compressed web indexes
Authors: Flavio Chierichetti, Ravi Kumar, Prabhakar Raghavan
Venue: WWW
Year: 2009

Web search engines use indexes to efficiently retrieve pages containing specified query terms, as well as pages linking to specified pages. The problem of compressed indexes that permit such fast retrieval has a long history. We consider the problem: assuming that the terms in (or links to) a page are generated from a probability distribution, how well compactly can we build such indexes that allow fast retrieval? Of particular interest is the case when the probability distribution is Zipfian (or a similar power law), since these are the distributions that arise on the web. We obtain sharp bounds on the space requirement of Boolean indexes for text documents that follow Zipf's law. In the process we develop a general technique that applies to any probability distribution, not necessarily a power law; this is the first analysis of compression in indexes under arbitrary distributions. Our bounds lead to quantitative versions of rules of thumb that are folklore in indexing. Our experiments on several document collections show that the distribution of terms appears to follow a double-Pareto law rather than Zipf's law. Despite widely varying sets of documents, the index sizes observed in the experiments conform well to our theoretical predictions. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
What makes conversations interesting?: themes, participants and consequences of conversations in online social media
Authors: Munmun De Choudhury, Hari Sundaram, Ajita John, Dorée D. Seligmann
Venue: WWW
Year: 2009

Rich media social networks promote not only creation and consumption of media, but also communication about the posted media item. What causes a conversation to be interesting, that prompts a user to participate in the discussion on a posted video? We conjecture that people participate in conversations when they find the conversation theme interesting, see comments by people whom they are familiar with, or observe an engaging dialogue between two or more people (absorbing back and forth exchange of comments). Importantly, a conversation that is interesting must be consequential - i.e. it must impact the social network itself.
The web of nations
Authors: Sukwon Chung, Dungjit Shiowattana, Pavel Dmitriev, Su Chan
Venue: WWW
Year: 2009

In this paper, we report on a large-scale study of structural differences among the national webs. The study is based on a web-scale crawl conducted in the summer 2008. More specifically, we study two graphs derived from this crawl, the nation graph, with nodes corresponding to nations and edges - to links among nations, and the host graph, with nodes corresponding to hosts and edges - to hyperlinks among pages on the hosts. Contrary to some of the previous work [2], our results show that webs of different nations are often very different from each other, both in terms of their internal structure, and in terms of their connectivity with other nations.
A trust management framework for service-oriented environments
Authors: William Conner, Arun Iyengar, Thomas A. Mikalsen, Isabelle Rouvellou, Klara Nahrstedt
Venue: WWW
Year: 2009

Many reputation management systems have been developed under the assumption that each entity in the system will use a variant of the same scoring function. Much of the previous work in reputation management has focused on providing robustness and improving performance for a given reputation scheme. In this paper, we present a reputation-based trust management framework that supports the synthesis of trust-related feedback from many different entities while also providing each entity with the flexibility to apply different scoring functions over the same feedback data for customized trust evaluations. We also propose a novel scheme to cache trust values based on recent client activity. To evaluate our approach, we implemented our trust management service and tested it on a realistic application scenario in both LAN and WAN distributed environments. Our results indicate that our trust management service can effectively support multiple scoring functions with low overhead and high availability.
Mapping the world's photos
Authors: David J. Crandall, Lars Backstrom, Daniel P. Huttenlocher, Jon M. Kleinberg
Venue: WWW
Year: 2009

We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.
Thumbs-up: a game for playing to rank search results
Authors: Ali Dasdan, Chris Drome, Santanu Kolay
Venue: WWW
Year: 2009

Human computation is an effective way to channel human effort spent playing games to solving computational problems that are easy for humans but difficult for computers to automate. We propose Thumbs-Up, a new game for human computation with the purpose of playing to rank search result. Our experience from users shows that Thumbs-Up is not only fun to play, but produces more relevant rankings than both a major search engine and optimal rank aggregation using the Kemeny rule.