OpenRaiser/Intern-Atlas huggingface dataset:
Information diffusion in social networks: observing and affecting what society cares about
Authors: Divyakant Agrawal, Ceren Budak, Amr El Abbadi
Venue: CIKM
Year: 2011

Information diffusion in social networks provide great opportunities for political and social change as well as societal education. Therefore understanding information diffusion in social networks is a critical research goal. This greater understanding can be achieved through data analysis, development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.
Timing when to buy
Authors: Rakesh Agrawal, Samuel Ieong, Raja Velu
Venue: CIKM
Year: 2011

Most e-commerce sites to-date have focused on helping consumers decide what to buy and where to buy. We study the complementary question of helping consumers decide when to buy, focusing on consumer durables. We introduce a utility-based model for evaluating different approaches to this question. We focus on how best to make use of forecasts in making recommendations, and propose three natural strategies. We establish a relationship between these strategies, and show that one of them is optimal. We conduct a large-scale experimental study to test the performance and robustness of these strategies. Across a wide range of conditions, the best strategy obtains 90% of the maximum possible gains.
Understanding the types of information humans associate with geographic objects
Authors: Ahmet Aker, Robert J. Gaizauskas
Venue: CIKM
Year: 2011

In this paper we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and the Bodiam Castle are two objects of the same type - castle. The question is whether specific information is requested for the object type castle and how this information differs for objects of other types, e.g. church, museum or lake. We aim to answer this question using an online survey. In the survey we showed 184 participants 200 images pertaining to urban and rural objects and asked them to write questions for which they would like to know the answers when seeing those objects. Our analysis of 7644 questions collected in the survey shows that humans have shared ideas of what to ask about geographical objects. When the object types resemble each other (e.g. church, temple) the requested information is similar for the objects of these types. Otherwise, the information is specific to an object type. Our results can guide tasks involving automatic generation of templates for image descriptions, and their assessment as well as image indexing and organization.
Reranking search results for sparse queries
Authors: Elif Aktolga, James Allan
Venue: CIKM
Year: 2011

It is well known that clickthrough data can be used to improve the effectiveness of search results: broadly speaking, a query's past clicks are a predictor of future clicks on documents. However, when a new or unusual query appears, or when a system is not as widely used as a mainstream web search system, there may be little to no click data available to improve the results. Existing methods to boost query performance for sparse queries extend the query-document click relationship to more documents or queries, but require substantial clickthrough data from other queries. In this work we describe a way to boost rarely-clicked queries in a system where limited clickthrough data is available for all queries. We describe a probabilistic approach for carrying out that estimation and use it to rerank retrieved documents. We utilize information from co-click queries, subset queries, and synonym queries to estimate the clickthrough for a sparse query. Our experiments on a query log from a medical informatics company demonstrate that when overall clickthrough data is sparse, reranking search results using clickthrough information from related queries significantly outperforms reranking that employs clickthrough information from the query alone.
XQuery optimization based on program slicing
Authors: Jesús Manuel Almendros-Jiménez, Josep Silva, Salvador Tamarit
Venue: CIKM
Year: 2011

XQuery has become the standard query language for XML. The efforts put on this language have produced mature and efficient implementations of XQuery processors. However, in practice the efficiency of XQuery programs is strongly dependent on the ability of the programmer to combine different queries which often affect several XML sources that in turn can be distributed in different branches of the organization. Therefore, techniques to reduce the amount of data loaded and also to reduce the intermediate structures computed by queries is a necessity. In this work we propose a novel technique that allows the programmer to automatically optimize a query in such a way that unnecessary intermediate computations are avoided, and, in addition, it identifies the paths in the source XML documents that are really required to resolve the query.
On relevance, time and query expansion
Authors: Giuseppe Amodeo, Giambattista Amati, Giorgio Gambosi
Venue: CIKM
Year: 2011

We present the results of our exploratory analysis on the relationship that exists between relevance and time. We observe how the amount of documents published in a given interval of time is related to the probability of relevance, and, using the time series analysis, we show the existence of a correlation between time and relevance. As an initial application of this analysis, we study query expansion exploiting the detection of publication time peaks over the Blog06 collection. We finally propose an effective approach for the query expansion in the blog search domain. Our approach is based on the documents publication trend being so completely independent of any external resource.
Hybrid models for future event prediction
Authors: Giuseppe Amodeo, Roi Blanco, Ulf Brefeld
Venue: CIKM
Year: 2011

We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication dates of the retrieved documents to capture trends and periodicity of the associated events. The periodicity of historic data is used to estimate a probabilistic model to predict future bursts. Finally, a hybrid model is obtained by intertwining the probabilistic and the time-series model. Our empirical results on the New York Times corpus show that autocorrelation functions of time-series suffice to classify queries accurately and that our hybrid models lead to more accurate future event predictions than baseline competitors.
DTMBIO 2011: international workshop on data and textmining in biomedical informatics
Authors: Sophia Ananiadou, Doheon Lee, Shamkant B. Navathe, Min Song
Venue: CIKM
Year: 2011

ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 11) organizers are pleased to announce that the fifth DTMBIO will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBIO is on biomedical informatics. DTMBIO delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research.
A framework for personalized and collaborative clustering of search results
Authors: David C. Anastasiu, Byron J. Gao, David Buttler
Venue: CIKM
Year: 2011

How to organize and present search results plays a critical role in the utility of search engines. Due to the unprecedented scale of the Web and diversity of search results, the common strategy of ranked lists has become increasingly inadequate, and clustering has been considered as a promising alternative. Clustering divides a long list of disparate search results into a few topic-coherent clusters, allowing the user to quickly locate relevant results by topic navigation. While many clustering algorithms have been proposed that innovate on the automatic clustering procedure, we introduce ClusteringWiki, the first prototype and framework for personalized clustering that allows direct user editing of the clustering results. Through a Wiki interface, the user can edit and annotate the membership, structure and labels of clusters for a personalized presentation. In addition, the edits and annotations can be shared among users as a mass-collaborative way of improving search result organization and search engine utility.
Detection of text quality flaws as a one-class classification problem
Authors: Maik Anderka, Benno Stein, Nedim Lipka
Venue: CIKM
Year: 2011

For Web applications that are based on user generated content the detection of text quality flaws is a key concern. Our research contributes to automatic quality flaw detection. In particular, we propose to cast the detection of text quality flaws as a one-class classification problem: we are given only positive examples (= texts containing a particular quality flaw) and decide whether or not an unseen text suffers from this flaw. We argue that common binary or multiclass classification approaches are ineffective in here, and we underpin our approach by a real-world application: we employ a dedicated one-class learning approach to determine whether a given Wikipedia article suffers from certain quality flaws. Since in the Wikipedia setting the acquisition of sensible test data is quite intricate, we analyze the effects of a biased sample selection. In addition, we illustrate the classifier effectiveness as a function of the flaw distribution in order to cope with the unknown (real-world) flaw-specific class imbalances. Altogether, provided test data with little noise, four from ten important quality flaws in Wikipedia can be detected with a precision close to 1.
Latent feature encoding using dyadic and relational data
Authors: Shin Ando
Venue: CIKM
Year: 2011

Learning from dyadic and relational data is a fundamental problem for IR and KDD applications in web and social media domain. Basic behaviors and characteristics of users and documents are typically described by a collection of dyads, i.e., pairs of entities. Discriminative features extracted from such data are essential in exploratory and discriminatory analyses. Relational properties of the entities reflect pair-wise similarities and their collective community structure which are also valuable for discriminative learning. A challenging aspect of learning from the relational data in many domains, is that the generative process of relational links appears noisy and is not well described by a stochastic model.
PDFMeat: managing publications on the semantic desktop
Authors: David Aumüller, Erhard Rahm
Venue: CIKM
Year: 2011

Researchers maintain bibliographies and extensive sets of PDF files of scholarly publications on their desktop. The lack of proper metadata of downloaded PDFs makes this task a tedious one. With PDFMeat we present a solution to automatically determine publication metadata for scholarly papers within the user's desktop environment and link the metadata to the files. PDFMeat effectively matches local full texts to an online repository. In an evaluation for more than 2.000 diverse PDF files it worked highly reliable and showed excellent accuracy of up to 98 percent. We demonstrate PDFMeat for different sets of papers, highlighting the semantic integration and use of the retrieved metadata within the file browser of the desktop environment.
OpinioNetIt: understanding the opinions-people network for politically controversial topics
Authors: Rawia Awadallah, Maya Ramanath, Gerhard Weikum
Venue: CIKM
Year: 2011

The wikileaks documents or the economic crises in Ireland and Portugal are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics. In this paper, we describe our system, named OpinioNetIt (pronounced similar to "opinionated"), which aims to automatically derive a map of the opinions-people network from news and other Web documents.
Ranking-based processing of SQL queries
Authors: Hany Azzam, Thomas Roelleke, Sirvan Yahyaei
Venue: CIKM
Year: 2011

A growing number of applications are built on top of search engines and issue complex structured queries. This paper contributes a customisable ranking-based processing of such queries, specifically SQL. Similar to how term-based statistics are exploited by term-based retrieval models, ranking-aware processing of SQL queries exploits tuple-based statistics that are derived from sources or, more precisely, derived from the relations specified in the SQL query. To implement this ranking-based processing, we leverage PSQL, a probabilistic variant of SQL, to facilitate probability estimation and the generalisation of document retrieval models to be used for tuple retrieval. The result is a general-purpose framework that can interpret any SQL query and then assign a probabilistic retrieval model to rank the results of that query. The evaluation on the IMDB and Monster benchmarks proves that the PSQL-based approach is applicable to (semi-)structured and unstructured data and structured queries.
Extracting cross references from life science databases for search result ranking
Authors: Anja Bachmann, René Schult, Matthias Lange, Myra Spiliopoulou
Venue: CIKM
Year: 2011

Scholars in life sciences have to process huge amounts of data in a disciplined and efficient way. These data are spread among thousands of databases which overlap in content but differ substantially with respect to interface, formats and data structure. Search engines have the potential of assisting in data retrieval from these structured sources but fall short of providing a relevance ranking of the results that reflects the needs of life science scholars. One such need is to acquire insights to cross-references among entities in the databases, whereby search hits with many cross-references are expected to be more informative than those with few cross-references. In this work, we investigate to what extend this expectation holds. We propose BioXREF, a method that extracts cross-references from multiple life science databases by combining targeted crawling, pointer chasing, sampling and information extraction. We study the retrieval quality of our method and the relationship between manually crafted relevance ranking and relevance ranking based on cross-references, and report on first, promising results.
Constructing seminal paper genealogy
Authors: Duck-Ho Bae, Se-Mi Hwang, Sang-Wook Kim, Christos Faloutsos
Venue: CIKM
Year: 2011

When a researcher starts with a new topic, it would be very useful if seminal papers in the topic and their relationships are provided in advance. We propose an approach to construct seminal paper genealogy and show the effectiveness and efficiency of our approach.
Discovering URLs through user feedback
Authors: Xiao Bai, Berkant Barla Cambazoglu, Flavio Paiva Junqueira
Venue: CIKM
Year: 2011

Search engines rely upon crawling to build their Web page collections. A Web crawler typically discovers new URLs by following the link structure induced by links on Web pages. As the number of documents on the Web is large, discovering newly created URLs may take arbitrarily long, and depending on how a given page is connected to others, such a crawler may miss the pages altogether. In this paper, we evaluate the benefits of integrating a passive URL discovery mechanism into a Web crawler. This mechanism is passive in the sense that it does not require the crawler to actively fetch documents from the Web to discover URLs. We focus here on a mechanism that uses toolbar data as a representative source for new URL discovery. We use the toolbar logs of Yahoo! to characterize the URLs that are accessed by users via their browsers, but not discovered by Yahoo! Web crawler. We show that a high fraction of URLs that appear in toolbar logs are not discovered by the crawler. We also reveal that a certain fraction of URLs are discovered by the crawler later than the time they are first accessed by users. One important conclusion of our work is that web search engines can highly benefit from user feedback in the form of toolbar logs for passive URL discovery.
Cloning for privacy protection in multiple independent data publications
Authors: Muzammil M. Baig, Jiuyong Li, Jixue Liu, Hua Wang
Venue: CIKM
Year: 2011

Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data owner. However, no method has been proposed for the anonymization of data of multiple independent data publications. A data owner publishes a dataset, which contains overlapping population with other datasets published by other independent data owners. In this paper we analyze the privacy risk in the such scenario and vulnerability of partitioned based anonymization methods. We show that no partitioned based anonymization methods can protect privacy in arbitrary data distributions, and identify a case that the privacy can be protected in the scenario. We propose a new generalization principle ε-cloning to protect privacy for multiple independent data publications. We also develop an effective algorithm to achieve the ε-cloning. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves good data utility.
Focusing on novelty: a crawling strategy to build diverse language models
Authors: Luciano Barbosa, Srinivas Bangalore
Venue: CIKM
Year: 2011

Word prediction performed by language models has an important role in many tasks as e.g. word sense disambiguation, speech recognition, hand-writing recognition, query spelling and query segmentation. Recent research has exploited the textual content of the Web to create language models. In this paper, we propose a new focused crawling strategy to collect Web pages that focuses on novelty in order to create diverse language models. In each crawling cycle, the crawler tries to ll the gaps present in the current language model built from previous cycles, by avoiding visiting pages whose vocabulary is already well represented in the model. It relies on an information theoretic measure to identify these gaps and then learns link patterns to pages in these regions in order to guide its visitation policy. To handle constantly evolving domains, a key feature of our crawler approach is its ability to adjust its focus as the crawl progresses. We evaluate our approach in two different scenarios in which our solution can be useful. First, we demonstrate that our approach produces more effective language models than the ones created by a baseline crawler in the context of a speech recognition task of broadcast news. In fact, in some cases, our crawler was able to obtain similar results to the baseline by crawling only 12.5% of the pages collected by the latter. Secondly, since in the news domain avoiding well-represented content might lead to novelty, i.e. up-to-date pages, we show that our diversity-based crawler can also be helpful to guide the crawler for the most recent content in the news. The results show that our approach was able to obtain on average 50% more up-to-date pages than the baseline crawler.
Large-scale array analytics: taming the data tsunami
Authors: Peter Baumann
Venue: CIKM
Year: 2011

Never before in history mankind has collected data at the rates we face today. Alone in 2002, an estimated 403 Petabyte of data has been acquired, equivalent to all printed information ever created before. Earth orbiting satellites, as well as ground, airborne, and underwater sensors, space observatories scan their environment at unprecedented resolutions, giving rise to "Big Science". The same holds for the life sciences where genomic data, high-resolution scans, and other modalities are collected in steadily increasing streams. Social network analysis, OLAP, and stock exchange trading represent further examples, the latter involving real-time correlation of thousands of ticker time series resulting in Terabytes of data to be analysed per single run. Summarized under Large-Scale Analytics we are witnessing an exploding demand for flexible access to massive volumes of scientific and business data sets. Arguably a large class of these massive data is represented by multi-dimensional arrays. Consequently, large arrays pose new challenges to data modelling, querying, optimization, and maintenance -- in short: we need Large-Scale Array Analytics. This tutorial introduces to the topic from a database perspective. Aspects addressed include modelling, query languages, query optimization and parallelization, and storage management. High emphasis will be devoted to applications in "Big Science", particularly geo, space, and life sciences; real-life use cases will be presented and discussed which stem from our 15 years of experience with the open-source rasdaman array DBMS and our work on geo raster service standardization. We will highlight requirements, achievements, open research issues, and avenues for future research. Discussion will make use of real-life examples, many of which Internet connected participants can replay hands-on.
Structured collaborative filtering
Authors: Alejandro Bellogín, Jun Wang, Pablo Castells
Venue: CIKM
Year: 2011

In a general collaborative filtering (CF) setting, a user profile contains a set of previously rated items and is used to represent the user's interest. Unfortunately, most CF approaches ignore the underlying structure of user profiles. In this paper, we argue that a certain class of interest is best represented jointly by several items, drawing an analogy to "phrases" in text retrieval, which are not equivalent to the separate meaning of their words. At an alternative stance, we also consider the situation where, analogously to word synonyms, two items might be substitutable when representing a class of interest. We propose an approach integrating these two notions as opposing poles on a continuum spectrum. Upon this, we model the underlying structure in user profiles, drawing an analogy with text retrieval. The approach gives rise to a novel structured Vector Space Model for CF. We show that item-based CF approaches are a special case of the proposed method.
Factorization-based lossless compression of inverted indices
Authors: George Beskales, Marcus Fontoura, Maxim Gurevich, Sergei Vassilvitskii, Vanja Josifovski
Venue: CIKM
Year: 2011

Many large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index represents a sparse term-document matrix, where non-zero elements indicate the strength of term-document associations. In this work, we present an approach for lossless compression of inverted indices. Our approach maps terms in a document corpus to a new term space in order to reduce the number of non-zero elements in the term-document matrix, resulting in a more compact inverted index. We formulate the problem of selecting a new term space as a matrix factorization problem, and prove that finding the optimal solution is an NP-hard problem. We develop a greedy algorithm for finding an approximate solution. A side effect of our approach is increasing the number of terms in the index, which may negatively affect query evaluation performance. To eliminate such effect, we develop a methodology for modifying query evaluation algorithms by exploiting specific properties of our compression approach.