OpenRaiser/Intern-Atlas huggingface dataset:
Sparse structured probabilistic projections for factorized latent spaces
Authors: Xinquan Qu, Xinlei Chen
Venue: CIKM
Year: 2011

Building a common representation for several related data sets is an important problem in multi-view learning. CCA and its extensions have shown that they are effective in finding the shared variation among all data sets. However, these models generally fail to exploit the common structure of the data when the views are with private information. Recently, methods explicitly modeling the information into shared part and private parts have been proposed, but they presume to know the prior knowledge about the latent space, which is usually impossible to obtain. In this paper, we propose a probabilistic model, which could simultaneously learn the structure of the latent space whilst factorize the information correctly, therefore the prior knowledge of the latent space is unnecessary. Furthermore, as a probabilistic model, our method is able to deal with missing data problem in a natural way. We show that our approach attains the performance of state-of-art methods on the task of human pose estimation when the motion capture view is completely missing, and significantly improves the inference accuracy with only a few observed data.
A nugget-based test collection construction paradigm
Authors: Shahzad Rajput, Virgiliu Pavlu, Peter B. Golbus, Javed A. Aslam
Venue: CIKM
Year: 2011

The problem of building test collections is central to the development of information retrieval systems such as search engines. Starting with a few relevant "nuggets" of information manually extracted from existing TREC corpora, we implement and test a methodology that finds and correctly assesses the vast majority of relevant documents found by TREC assessors - as well as up to four times more additional relevant documents. Our methodology produces highly accurate test collections that hold the promise of addressing the issues of scalability, reusability, and applicability.
Optimising ontology stream reasoning with truth maintenance system
Authors: Yuan Ren, Jeff Z. Pan
Venue: CIKM
Year: 2011

So far researchers in the Description Logics / Ontology communities mainly consider ontology reasoning services for static ontologies. The rapid development of the Semantic Web and its emerging data ask for reasoning technologies for dynamic knowledge streams. Existing work on stream reasoning is focused on lightweight languages such as RDF and RDFS. In this paper, we introduce the notion of Ontology Stream Management System (OSMS) and present a stream-reasoning approach based on Truth Maintenance System (TMS). We present optimised EL++ algorithm to reduce memory consumption. Our evaluations show that the optimisation improves TMS-enabled EL++ reasoning to deal with relatively large volumes of data and update efficiently.
On benchmarking data translation systems for semantic-web ontologies
Authors: Carlos R. Rivero, Inma Hernández, David Ruiz, Rafael Corchuelo
Venue: CIKM
Year: 2011

Data translation, also known as data exchange, is an integration task that aims at populating a target model using data from a source model. This task is gaining importance in the context of semantic-web ontologies due to the increasing interest in graph databases and semantic-web agents. Currently, there are a variety of semantic-web technologies that can be used to implement data translation systems. This makes it difficult to assess them from an empirical point of view. In this paper, we present a benchmark that provides a catalogue of seven data translation patterns that can be instantiated by means of seven parameters. This allows us to create a variety of synthetic, domain-independent scenarios one can use to test existing data translation systems. We also illustrate how to analyse three such systems using our benchmark. The main benefit of our benchmark is that it allows to compare data translation systems side by side within a homogeneous framework.
Search and mining entity-relationship data
Authors: Haggai Roitman, Ralf Schenkel, Marko Grobelnik
Venue: CIKM
Year: 2011

This paper summarizes the details of the first international workshop on search and mining entity-relationship data. This workshop will bridge between IR, DB, and KM researchers to seek novel solutions for search and data mining of rich entity-relationship data and their applications in various domains. We first provide an overview about the workshop. We then briefly discuss the workshop program.
Exploratory search over social-medical data
Authors: Haggai Roitman, Sivan Yogev, Yevgenia Tsimerman, Dae Won Kim, Yossi Mesika
Venue: CIKM
Year: 2011

In this demo we shall present the IBM Patient Empowerment System (PES), and more specifically, its social-medical discovery sub-system. Social and medical data are represented using entities and relationships and are explored using a combination of expressive, yet intuitive, query language, faceted search, and ER graph navigation. While this demonstration focuses on the healthcare domain, the underlining search technology is generic and can be utilized in many other domains. Therefore, this demo has two main contributions. First, we present a novel entity-relationship indexing and retrieval solution, and discuss its implementation challenges. Second, the demonstration depicts a practical entity-relationship discovery technology in a real domain setting within a real IBM system.
Exploiting user knowledge during retrieval of semantically annotated API operations
Authors: Michal Rój
Venue: CIKM
Year: 2011

Size and complexity of current application programming interfaces (APIs) result in a relatively high amount of effort needed to find operations of desired functionality. With semantic annotation of APIs, the searching process can be automated. This paper identifies and discusses four methods of offering the operation discovery functionality to users. In each method different kind of information is taken as input; each method can be useful for programmers with different background and experience.
Editing knowledge resources: the wiki way
Authors: Francesco Ronzano, Andrea Marchetti, Maurizio Tesconi
Venue: CIKM
Year: 2011

The creation, customization, and maintenance of knowledge resources are essential for fostering the full deployment of Language Technologies. The definition and refinement of knowledge resources are time- and resource-consuming activities. In this paper we explore how the Wiki paradigm for online collaborative content editing can be exploited to gather massive social contributions from common Web users in editing knowledge resources. We discuss the Wikyoto Knowledge Editor, also called Wikyoto. Wikyoto is a collaborative Web environment that enables users with no knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project.
The list Viterbi training algorithm and its application to keyword search over databases
Authors: Silvia Rota, Sonia Bergamaschi, Francesco Guerra
Venue: CIKM
Year: 2011

Hidden Markov Models (HMMs) are today employed in a varietyof applications, ranging from speech recognition to bioinformatics.In this paper, we present the List Viterbi training algorithm, aversion of the Expectation-Maximization (EM) algorithm based onthe List Viterbi algorithm instead of the commonly used forwardbackwardalgorithm. We developed the batch and online versionsof the algorithm, and we also describe an interesting application inthe context of keyword search over databases, where we exploit aHMM for matching keywords into database terms. In our experimentswe tested the online version of the training algorithm in asemi-supervised setting that allows us to take into account the feedbacksprovided by the users.
Re-ranking by local re-scoring for video indexing and retrieval
Authors: Bahjat Safadi, Georges Quénot
Venue: CIKM
Year: 2011

Poster session: information retrieval
Social ranking for spoken web search
Authors: Shrey Sahay, Nitendra Rajput, Niketan Pansare
Venue: CIKM
Year: 2011

Spoken Web is an alternative Web for low-literacy users in the developing world. People can create audio content over phone and share on the Spoken Web. This enables easy creation of locally relevant content. Even on the World Wide Web in developed regions, the recent increase in traffic is due to the locally relevant content created on social networking sites. This paper argues that content search and ranking in the new scenario needs a re-look. The generic model of using in-links for ranking such content is not an appropriate measure of the content relevance in such a collaborative Web 2.0 world. This paper aims to bring the social context in Spoken Web ranking. We formulate a relationship function between the query-creator and the content-creator and use this as one measure of the content relevance to the user. The relationship function uses the geographical location of the two people and their prior browsing preferences as parameters to determine the relationship between the two users. Further we also determine the trustability of the content based on the content creator's acceptance measure by the social network. We use these two features in addition to the term-frequency - inverse-term-frequency match to rank the search results in context of the social network of the query-creator and provide a more specific and socially relevant result to the user.
Click the search button and be happy: evaluating direct and immediate information access
Authors: Tetsuya Sakai, Makoto P. Kato, Young-In Song
Venue: CIKM
Year: 2011

We define Direct Information Access as a type of information access where there is no user operation such as clicking or scrolling between the user's click on the search button and the user's information acquisition; we define Immediate Information Access as a type of information access where the user can locate the relevant information within the system output very quickly. Hence, a Direct and Immediate Information Access (DIIA) system is expected to satisfy the user's information need very quickly with its very first response. We propose a nugget-based evaluation framework for DIIA, which takes nugget positions into account in order to evaluate the ability of a system to present important nuggets first and to minimise the amount of text the user has to read. To demonstrate the integrity, usefulness and limitations of our framework, we built a Japanese DIIA test collection with 60 queries and over 2,800 nuggets as well as an offset-based nugget match evaluation interface, and conducted experiments with manual and automatic runs. The results suggest our proposal is a useful complement to traditional ranked retrieval evaluation based on document relevance.
Large-scale information retrieval experimentation with terrier
Authors: Rodrygo L. T. Santos, Richard McCreadie, Vassilis Plachouras
Venue: CIKM
Year: 2011

This tutorial aims to provide a practical introduction to conducting large-scale information retrieval (IR) experiments, using Terrier (http://terrier.org) as an experimentation platform. Written in Java, Terrier provides an open-source, feature-rich, flexible, and robust environment for large-scale IR experimentation. This tutorial will cover the experimentation process end-to-end, from configuring Terrier to a particular experimental setting, to efficiently indexing a document corpus and retrieving from it, and to evaluating the outcome. Moreover, it will describe how to use and extend the platform to one's own needs, and will be illustrated by practical research-driven examples. As a half-day tutorial, it will be split into two major sessions, with each session comprising both background information and practical demonstrations. In the first session, we will provide an overview of several aspects of large-scale IR experimentation, spanning areas such as indexing, data structures, query languages, and advanced retrieval models, and how these are implemented within Terrier. In the second session, we will discuss how to extend Terrier to conduct one's own experiments in a large-scale setting, including how to facilitate the evaluation of non-standard IR tasks through crowdsourcing. The practical demonstrations will cover recent use cases identified from Terrier's online discussion forum, so as to provide attendees with concrete examples of what can be done within Terrier.
Semantic-ART: a framework for semantic annotation of regulatory text
Authors: Krishna Sapkota, Arantza Aldea, David A. Duce, Muhammad Younas, René Bañares-Alcántara
Venue: CIKM
Year: 2011

Converting regulatory texts to machine interpretable models can enhance the automation of compliance management (CM) processes. The process poses serious research challenges as the information to be extracted from the regulatory texts comes from different regulatory bodies and is in different formats. In this paper, we present the main problems that we have faced in this area and how we have tackled them. Our proposed framework, Semantic-ART, considers the use of semantic annotation (SA) techniques to extract the regulations automatically.
Fast metadata-driven multiresolution tensor decomposition
Authors: Claudio Schifanella, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2011

Tensors (multi-dimensional arrays) are widely used for representing high-order dimensional data, in applications ranging from social networks, sensor data, and Internet traffic. Multi-way data analysis techniques, in particular tensor decompositions, allow extraction of hidden correlations among multi-way data and thus are key components of many data analysis frameworks. Intuitively, these algorithms can be thought of as multi-way clustering schemes, which consider multiple facets of the data in identifying clusters, their weights, and contributions of each data element. Unfortunately, algorithms for fitting multi-way models are, in general, iterative and very time consuming. In this paper, we observe that, in many applications, there is a priori background knowledge (or metadata) about one or more domain dimensions. This metadata is often in the form of a hierarchy that clusters the elements of a given data facet (or mode). In this paper, we investigate whether such single-mode data hierarchies can be used to boost the efficiency of tensor decomposition process, without significant impact on the final decomposition quality. We consider each domain hierarchy as a guide to help provide higher- or lower-resolution views of the data in the tensor on demand and we rely on these metadata-induced multi-resolution tensor representations to develop a multiresolution approach to tensor decomposition. In this paper, we focus on an alternating least squares (ALS) based implementation of the PARAllel FACtors (PARAFAC) decomposition (which decomposes a tensor into a diagonal tensor and a set of factor matrices). Experiment results show that, when the available metadata is used as a rough guide, the proposed multiresolution method helps fit PARAFAC models with consistent (for both dense and sparse tensor representations, under different parameters settings) savings in execution time and memory consumption, while preserving the quality of the decomposition.
A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi
Authors: Julie Séguéla, Gilbert Saporta
Venue: CIKM
Year: 2011

In domains such as Marketing, Advertising or even Human Resources (sourcing), decision-makers have to choose the most suitable channels according to their objectives when starting a campaign. In this paper, three recommender systems providing channel ("user") ranking for a given campaign ("item") are introduced. This work refers exclusively to the new item problem, which is still a challenging topic in the literature. The first two systems are standard content-based recommendation approaches, with different rating estimation techniques (model-based vs heuristic-based). To overcome the lacks of previous approaches, we introduce a new hybrid system using a supervised similarity based on PLS components. Algorithms are compared in a case study: purpose is to predict the ranking of job boards (job search web sites) in terms of ROI (return on investment) per job posting. In this application, the semi-supervised hybrid system outperforms standard approaches.
A pairwise ranking based approach to learning with positive and unlabeled examples
Authors: Sundararajan Sellamanickam, Priyanka Garg, Sathiya Keerthi Selvaraj
Venue: CIKM
Year: 2011

A large fraction of binary classification problems arising in web applications are of the type where the positive class is well defined and compact while the negative class comprises everything else in the distribution for which the classifier is developed; it is hard to represent and sample from such a broad negative class. Classifiers based only on positive and unlabeled examples reduce human annotation effort significantly by removing the burden of choosing a representative set of negative examples. Various methods have been proposed in the literature for building such classifiers. Of these, the state of the art methods are Biased SVM and Elkan & Noto's methods. While these methods often work well in practice, they are computationally expensive since hyperparameter tuning is very important, particularly when the size of labeled positive examples set is small and class imbalance is high. In this paper we propose a pairwise ranking based approach to learn from positive and unlabeled examples (LPU) and we give a theoretical justification for it. We present a pairwise RankSVM (RSVM) based method for our approach. The method is simple, efficient, and its hyperparameters are easy to tune. A detailed experimental study using several benchmark datasets shows that the proposed method gives competitive classification performance compared to the mentioned state of the art methods, while training 3-10 times faster. We also propose an efficient AUC based feature selection technique in the LPU setting and demonstrate its usefulness on the datasets. To get an idea of the goodness of the LPU methods we compare them against supervised learning (SL) methods that also make use of negative examples in training. SL methods give a slightly better performance than LPU methods when there is a rich set of negative examples; however, they are inferior when the number of negative training examples is not large enough.
Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset
Authors: Sathiya Keerthi Selvaraj, Bigyan Bhar, Sundararajan Sellamanickam, Shirish K. Shevade
Venue: CIKM
Year: 2011

In the design of practical web page classification systems one often encounters a situation in which the labeled training set is created by choosing some examples from each class; but, the class proportions in this set are not the same as those in the test distribution to which the classifier will be actually applied. The problem is made worse when the amount of training data is also small. In this paper we explore and adapt binary SVM methods that make use of unlabeled data from the test distribution, viz., Transductive SVMs (TSVMs) and expectation regularization/constraint (ER/EC) methods to deal with this situation. We empirically show that when the labeled training data is small, TSVM designed using the class ratio tuned by minimizing the loss on the labeled set yields the best performance; its performance is good even when the deviation between the class ratios of the labeled training set and the test set is quite large. When the labeled training data is sufficiently large, an unsupervised Gaussian mixture model can be used to get a very good estimate of the class ratio in the test set; also, when this estimate is used, both TSVM and EC/ER give their best possible performance, with TSVM coming out superior. The ideas in the paper can be easily extended to multi-class SVMs and MaxEnt models.
Efficient phrase querying with flat position index
Authors: Dongdong Shan, Wayne Xin Zhao, Jing He, Rui Yan, Hongfei Yan, Xiaoming Li
Venue: CIKM
Year: 2011

A large proportion of search engine queries contain phrases,namely a sequence of adjacent words. In this paper, we propose to use flat position index (a.k.a schema-independent index) for phrase query evaluation. In the flat position index, the entire document collection is viewed as a huge sequence of tokens. Each token is represented by one flat position, which is a unique position offset from the beginning of the collection. Each indexed term is associated with a list of the flat positions about that term in the sequence. To recover DocID from flat positions efficiently, we propose a novel cache sensitive look-up table (CSLT), which is much faster than existing search algorithms. Experiments on TREC GOV2 data collection show that flat position index can reduce the index size and speed up phrase querying substantially, compared with traditional word-level index.
MTopS: scalable processing of continuous top-k multi-query workloads
Authors: Avani Shastri, Di Yang, Elke A. Rundensteiner, Matthew O. Ward
Venue: CIKM
Year: 2011

A continuous top-k query retrieves the k most preferred objects in a data stream according to a given preference function. These queries are important for a broad spectrum of applications ranging from web-based advertising to financial analysis. In various streaming applications, a large number of such continuous top-k queries need to be executed simultaneously against a common popular input stream. To efficiently handle such top-k query workload, we present a comprehensive framework, called MTopS.Within this MTopS framework, several computational components work collaboratively to first analyze the commonalities across the workload; organize the workload for maximized sharing opportunities; execute the workload queries simultaneously in a shared manner; and output query results whenever any input query requires. In particular, MTopS supports two proposed algorithms, MTopBand and MTopList, which both incrementally maintain the top-k objects over time for multiple queries. As the foundation, we first identify the minimal object set from the data stream that is both necessary and sufficient for accurately answering all top-k queries in the workload. Then, the MTopBand algorithm is presented to incrementally maintain such minimum object set and eliminate the need for any recomputation from scratch. To further optimize MTop-Band, we design the second algorithm, MTopList which organizes the progressive top-k results of workload queries in a compact structure. MTopList is shown to be memory optimal and also more efficient in terms of CPU time usage than MTopBand. Our experimental study, using real data streams from domains of stock trades and moving object monitoring, demonstrates that both the efficiency and scalability of our proposed techniques are clearly superior to the state-of-the-art solutions.
On selection of objective functions in multi-objective community detection
Authors: Chuan Shi, Philip S. Yu, Yanan Cai, Zhenyu Yan, Bin Wu
Venue: CIKM
Year: 2011

There is a surge of community detection of complex networks in recent years. Different from conventional single-objective community detection, this paper formulates community detection as a multi-objective optimization problem and proposes a general algorithm NSGA-Net based on evolutionary multi-objective optimization. Interested in the effect of optimization objectives on the performance of the multi-objective community detection, we further study the correlations (i.e., positively correlated, independent, or negatively correlated) of 11 objective functions that have been used or can potentially be used for community detection. Our experiments show that NSGA-Net optimizing over a pair of negatively correlated objectives usually performs better than the single-objective algorithm optimizing over either of the original objectives, and even better than other well-established community detection approaches.