OpenRaiser/Intern-Atlas huggingface dataset:
Frequent pattern-growth approach for document organization
Authors: Monika Akbar, Rafal A. Angryk
Venue: CIKM
Year: 2008

In this paper, we propose a document clustering mechanism that depends on the appearance of frequent senses in the documents rather than on the co-occurrence of frequent keywords. Instead of representing each document as a collection of keywords, we use a document-graph which reflects a conceptual hierarchy of keywords related to that document. We incorporate a graph mining approach with one of the well-known association rule mining procedures, FP-growth, to discover the frequent subgraphs among the document-graphs. The similarity of the documents is measured in terms of the number of frequent subgraphs appearing in the corresponding document-graphs. We believe that our novel approach allows us to cluster the documents based more on their senses rather than the actual keywords.
Towards an error-free Arabic stemming
Authors: Eiman Tamah Al-Shammari, Jessica Lin
Venue: CIKM
Year: 2008

Stemming is a computational process for reducing words to their roots (or stems). It can be classified as a recall-enhancing or precision-enhancing component.
Experiments with English-Persian text retrieval
Authors: Abolfazl AleAhmad, Hadi Amiri, Masoud Rahgozar, Farhad Oroumchian
Venue: CIKM
Year: 2008

As the number of non-English documents is increasing dramatically on the web nowadays, the study and design of information retrieval systems for these languages is very important. The Persian language is the official language of Iran, Afghanistan and Tajikistan and is also spoken in some other countries in the Middle East, so there are significant amount of Persian documents available on the web. In this study, we will present and compare our English-Persian cross language text retrieval experiments on Hamshahri text collection. Also, we will present Combinatorial Translation Probability (CTP) calculation method for query translation that estimates translation probabilities based on the collection itself.
Structural relevance: a common basis for the evaluation of structured document retrieval
Authors: Mir Sadek Ali, Mariano P. Consens, Gabriella Kazai, Mounia Lalmas
Venue: CIKM
Year: 2008

This paper presents a unified framework for the evaluation of a range of structured document retrieval (SDR) approaches and tasks. The framework is based on a model of tree retrieval, evaluated using a novel extension of the Structural elevance (SR) measure. The measure replaces the assumption of independence in traditional information retrieval (IR) with a notion of redundancy that takes into account the user navigation inside documents while seeking relevant information. Unlike existing metrics for SDR, our proposed framework does not require the computation of an ideal ranking which has, thus far, prevented the practical application of such measures. Instead, SR builds on a Markovian model of user navigation that can be estimated through the use of structural summaries. The results of this paper (supported by experimental validation using INEX data) show that SR defined over a tree retrieval model can provide a common basis for the evaluation of SDR approaches across various structured search tasks.
An effective algorithm for mining 3-clusters in vertically partitioned data
Authors: Faris Alqadah, Raj Bhatnagar
Venue: CIKM
Year: 2008

Conventional clustering algorithms group similar data points together along one dimension of a data table. Bi-clustering simultaneously clusters both dimensions of a data table. 3-clustering goes one step further and aims to concurrently cluster two data tables that share a common set of row labels, but whose column labels are distinct. Such clusters reveal the underlying connections between the elements of all three sets. We present a novel algorithm that discovers 3-clusters across vertically partitioned data. Our approach presents two new and important formulations: first we introduce the notion of a 3-cluster in partitioned data; and second we present a mathematical formulation that measures the quality of such clusters. Our algorithm discovers high quality, arbitrarily positioned, overlapping clusters, and is efficient in time. These results are exhibited in a comprehensive study on real datasets.
Detecting significant distinguishing sets among bi-clusters
Authors: Faris Alqadah, Raj Bhatnagar
Venue: CIKM
Year: 2008

A fundamental task of data analysis is comprehending what distinguishes clusters found within the data. We present the problem of mining distinguishing sets; which seeks to find sets of objects or attributes that induce the most incremental change between adjacent bi-clusters of a binary dataset. Viewing the lattice of bi-clusters formed within a data set as a weighted directed graph, we mine the most significant distinguishing sets by growing a maximal-cost spanning tree of the lattice.
Text mining for pharmacogenomics
Authors: Russ B. Altman
Venue: CIKM
Year: 2008

We are building the Pharmacogenetics & Pharmacogenomics Knowledgebase (PharmGKB, http://www.pharmgkb.org/) with the goal of cataloguing all knowledge about how genetic variation impacts drug response phenotypes. PharmGKB stores primary data (genotype and phenotype data) as well as more distilled knowledge in the form of pathway diagrams, annotated summaries of very important pharmacogenes (VIP genes), and annotated literature. The literature annotation efforts include both manual curation by trained curators and automatic information extraction. In this talk, I will discuss three projects relevant to our efforts in literature curation:
Clustering the topics using TF-IDF for model fusion
Authors: Muath Alzghool, Diana Zaiu Inkpen
Venue: CIKM
Year: 2008

Users tend to express their queries in various ways: sometimes they use more general terms, sometimes more specific terms. Information retrieval systems need to be able to accommodate this variety of user needs. Some retrieval models perform better when the queries are general, others perform better when the queries are more specific, and others when a combination is available. In this paper we are looking for a system that will perform well in all these cases, we present a new method for combining the results of different models in order to improve the performance on a difficult task: Information Retrieval from spontaneous speech. Our technique is based on clustering the training topics according to their tf-idf (term frequency-inverse document frequency) properties, and selecting the best models for each cluster. When the system runs on a test topic, the cluster of the topic needs to be determined and the combination of models of this cluster is used. We report improvements on the Malach collection used at CLEF-CLSR 2007.
Viability of in-house datamarting approaches for population genetics analysis of snp genotypes
Authors: Jorge Amigo, Christopher Phillips, Antonio Salas
Venue: CIKM
Year: 2008

Databases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical or population genetics applications. While many of these SNP repositories have implemented data retrieval tools for general purpose mining, these alone cannot cover the broad spectrum of needs of most medical and population genetics studies. To address this limitation, we propose building in-house customized data marts from the raw data provided by the largest public databases. In particular, for population genetics analysis based on genotypes we propose building a set of data processing scripts that would deal with raw data coming from the major SNP variation databases (e.g. HapMap, Perlegen) that can be stripped into single genotypes and then grouped into populations. This allows not only in-house standardization and normalization of the genotyping data retrieved from different repositories, but also the calculation of statistical indices from simple allele frequency estimates up to elaborate genetic differentiation tests within populations, together with the ability to combine population samples from different databases. This article describes the viability of implementing scripts for handling extensive datasets of SNP genotypes with low computational costs, and shows that the updating of these data marts is straightforward, permitting easy implementation of new external data and the computation of new statistical indices.
Data degradation: making private data less sensitive over time
Authors: Nicolas Anciaux, Luc Bouganim, Harold van Heerde, Philippe Pucheral, Peter M. G. Apers
Venue: CIKM
Year: 2008

Trail disclosure is the leakage of privacy sensitive data, resulting from negligence, attack or abusive scrutinization or usage of personal digital trails. To prevent trail disclosure, data degradation is proposed as an alternative to the limited retention principle. Data degradation is based on the assumption that long lasting purposes can often be satisfied with a less accurate, and therefore less sensi-tive, version of the data. Data will be progressively degraded such that it still serves application purposes, while decreasing accuracy and thus privacy sensitivity.
Collaborative partitioning with maximum user satisfaction
Authors: Fred S. Annexstein, Svetlana Strunjas
Venue: CIKM
Year: 2008

Our collaborative partitioning model posits a bicriteria objective in which we seek the best item clustering that satisfies the most users at the highest level of satisfaction. We consider two basic methods for determining user satisfaction. The first method is based on how well each user's preferences match a given partition, and the second method is based on average correlation scores taken over sufficiently large subpopulations of users. We show these problems are NP-Hard and develop a set of heuristic approaches for solving them. We provide lower bounds on the satisfaction level on random data, and error bounds in the planted partition model, which provide confidence levels for our heuristic methods. Finally, we present experiments on several real examples that demonstrate the effectiveness of our framework.
Intra-document structural frequency features for semi-supervised domain adaptation
Authors: Andrew Arnold, William W. Cohen
Venue: CIKM
Year: 2008

In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access to large amounts of labeled data from other related, but distinct source domains, and seemingly no way to transfer knowledge from one to the other. Experimentally, we focus on the problem of extracting protein mentions from academic publications in the field of biology, where the source domain data are abstracts labeled with protein mentions, and the target domain data are wholly unlabeled captions. We mine the large number of such full text articles freely available on the Internet in order to supplement the limited amount of annotated data available. By exploiting the explicit and implicit common structure of the different subsections of these documents, including the unlabeled full text, we are able to generate robust features that are insensitive to changes in marginal and conditional distributions of classes and data across domains. We supplement these domain-insensitive features with automatically obtained high-confidence positive and negative predictions on the target domain to learn extractors that generalize well from one section of a document to another. Finally, lacking labeled target testing data, we employ comparative user preference studies to evaluate the relative performance of the proposed methods with respect to existing baselines.
The effect of contextualization at different granularity levels in content-oriented xml retrieval
Authors: Paavo Arvola, Jaana Kekäläinen, Marko Junkkari
Venue: CIKM
Year: 2008

In the hierarchical XML structure, the ancestors form the context of an XML element. The process of taking element's context into account in element scoring is called contextualization. The aim of this paper is to separate different granularity levels and test the effect of contextualization on these levels.
Predicting protein-protein relationships from literature using collapsed variational latent dirichlet allocation
Authors: Tatsuya Asou, Koji Eguchi
Venue: CIKM
Year: 2008

This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model, and compared them from the viewpoints of log-likelihoods, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the other, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction.
EDSC: efficient density-based subspace clustering
Authors: Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
Venue: CIKM
Year: 2008

Subspace clustering mines clusters hidden in subspaces of high-dimensional data sets. Density-based approaches have been shown to successfully mine clusters of arbitrary shape even in the presence of noise in full space clustering. Exhaustive search of all density-based subspace clusters, however, results in infeasible runtimes for large high-dimensional data sets. This is due to the exponential number of possible subspace projections in addition to the high computational cost of density-based clustering.
Records retention in relational database systems
Authors: Ahmed A. Ataullah, Ashraf Aboulnaga, Frank Wm. Tompa
Venue: CIKM
Year: 2008

The recent introduction of several pieces of legislation mandating minimum and maximum retention periods for corporate records has prompted the Enterprise Content Management (ECM) community to develop various records retention solutions. Records retention is a significant subfield of records management, and legal records retention requirements apply over corporate records regardless of their shape or form. Unfortunately, the scope of existing solutions has been largely limited to proper identification, classification and retention of documents, and not of data more generally.
Estimating real-valued characteristics of criminals from their recorded crimes
Authors: Richard Bache, Fabio Crestani
Venue: CIKM
Year: 2008

Offender profiling concerns making inferences about a criminal from the crime(s) he has committed. Where descriptionsof the crimes are recorded electronically, text mining techniques provide a means by which recorded characteristics of the offenders can be linked with features of his crimes as revealed in the text. Past studies have used Language Modelling to identify characteristics that can be described by a categorical variable e.g. gender. Here we adapt the Language Modelling approach to allow estimation of numerical quantities such as age and distance travelled.
A light weighted damage tracking quarantine and recovery scheme for mission-critical database systems
Authors: Kun Bai, Peng Liu
Venue: CIKM
Year: 2008

As online applications gain popularity in today's E-Business world, surviving DBMS from an attack is becoming crucial because of the increasingly critical role that database servers are playing. Although a number of research projects have been done to tackle the emerging data corruption threats, existing mechanisms are still limited in meeting four highly desired requirements: near zero run time overhead, zero system down time. In this paper, we propose TRACE, a light weighted database Damage Tracking, Quarantine, and Recovery (DTQR) solution with negligible run time overhead.
Non-local evidence for expert finding
Authors: Krisztian Balog, Maarten de Rijke
Venue: CIKM
Year: 2008

The task addressed in this paper, finding experts in an enterprise setting, has gained in importance and interest over the past few years. Commonly, this task is approached as an association finding exercise between people and topics. Existing techniques use either documents (as a whole) or proximity-based techniques to represent candidate experts. Proximity-based techniques have shown clear precision-enhancing benefits. We complement both document and proximity-based approaches to expert finding by importing global evidence of expertise, i.e., evidence obtained using information that is not available in the immediate proximity of a candidate expert's name occurrence or even on the same page on which the name occurs. Examples include candidate priors, query models, as well as other documents a candidate expert is associated with.
Incorporation of corpus-specific semantic information into question answering context
Authors: Protima Banerjee, Hyoil Han
Venue: CIKM
Year: 2008

In today's environment of information overload, Question Answering (QA) is a critically important research area for the Semantic Web. In order for humans to make effective use of the expansive information sources available to us, we require automated tools to help us make sense of large amounts of data. Within this framework, Question Context plays an important role. We define Question Context to be an semantic structure that can be used to enrich queries so that the user's information need is better represented. This paper describes the theoretical foundations of a novel approach that uses statistical language modeling techniques to create Question Context and to then integrate it into the Information Retrieval stage of QA. We base our approach on two established language modeling methods - the Aspect Model, which is the basis of Probabilistic Latent Semantic Analysis (PLSA) and Relevance-Based Language Models. Our approach proposes an Aspect-Based Relevance Language Model as the Question Context Model, and our methodology incorporates corpus-specific semantic concepts into the QA process. Words from the most heavily relevant aspects are then incorporated into the query. We present some interesting preliminary qualitative results that show the potential usefulness of the Question Context Model to both the first (IR) and second (Intelligent Information Processing) stages of QA.
Boosting social annotations using propagation
Authors: Shenghua Bao, Bohai Yang, Ben Fei, Shengliang Xu, Zhong Su, Yong Yu
Venue: CIKM
Year: 2008

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Although social annotations are developing fast, they cover only a small proportion of Web pages on the World Wide Web. To alleviate the low coverage problem, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included: basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. Experimental results show that the proposed model is very effective in increasing coverage of annotations as well as preserving property of social annotations.
Local approximation of pagerank and reverse pagerank
Authors: Ziv Bar-Yossef, Li-Tal Mashiach
Venue: CIKM
Year: 2008

We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. This problem was originally studied by Chen, Gan, and Suel (CIKM 2004), who presented an algorithm for tackling it. We prove that local approximation of PageRank, even to within modest approximation factors, is infeasible in the worst-case, as it requires probing the link server for Ω(n) nodes, where n is the size of the graph. The difficulty emanates from nodes of high in-degree and/or from slow convergence of the PageRank random walk.
A new method for indexing genomes using on-disk suffix trees
Authors: Marina Barsky, Ulrike Stege, Alex Thomo, Chris Upton
Venue: CIKM
Year: 2008

We propose a new method to build persistent suffix trees for indexing the genomic data. Our algorithm DiGeST (Disk-Based Genomic Suffix Tree) improves significantly over previous work in reducing the random access to the input string and performing only two passes over disk data. DiGeST is based on the two-phase multi-way merge sort paradigm using a concise binary representation of the DNA alphabet. Furthermore, our method scales to larger genomic data than managed before.
Building self-organized image retrieval network
Authors: Stanislav Barton, Vlastislav Dohnal, Jan Sedmidubský, Pavel Zezula
Venue: CIKM
Year: 2008

We propose a self-organized content-based Image Retrieval Network (IRN) that is inspired by a Metric Social Network (MSN) search system. The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries. Thus a shared database where each peer is fully in charge of its data, is created. The self-organization of the network is obtained by exploiting the social-network approach of the MSN - the connections between peers in the network are created as social-network relationships formed on the basis of a query-answer principle. The knowledge of answers to previous queries is used to fast navigate to peers, possibly containing the best answers to new queries. Additionally, the network uses a randomized mechanism to explore new and unvisited parts of the network. In this way, the self-adaptability and robustness of the system are achieved. The proposed concepts are verified using a real network consisting of 2,000 peers containing descriptive features of 10 million images from the Flickr Photo Sharing system.
Data weaving: scaling up the state-of-the-art in data clustering
Authors: Ron Bekkerman, Martin Scholz
Venue: CIKM
Year: 2008

The enormous amount and dimensionality of data processed by modern data mining tools require effective, scalable unsupervised learning techniques. Unfortunately, the majority of previously proposed clustering algorithms are either effective or scalable. This paper is concerned with information-theoretic clustering (ITC) that has historically been considered the state-of-the-art in clustering multi-dimensional data. Most existing ITC methods are computationally expensive and not easily scalable. Those few ITC methods that scale well (using, e.g., parallelization) are often outperformed by the others, of an inherently sequential nature. First, we justify this observation theoretically. We then propose data weaving - a novel method for parallelizing sequential clustering algorithms. Data weaving is intrinsically multi-modal - it allows simultaneous clustering of a few types of data (modalities). Finally, we use data weaving to parallelize multi-modal ITC, which results in proposing a powerful DataLoom algorithm. In our experimentation with small datasets, DataLoom shows practically identical performance compared to expensive sequential alternatives. On large datasets, however, DataLoom demonstrates significant gains over other parallel clustering methods. To illustrate the scalability, we simultaneously clustered rows and columns of a contingency table with over 120 billion entries.