OpenRaiser/Intern-Atlas huggingface dataset:
LiquidXML: adaptive XML content redistribution
Authors: Jesús Camacho-Rodríguez, Asterios Katsifodimos, Ioana Manolescu, Alexandra Roatis
Venue: CIKM
Year: 2010

We propose to demonstrate LiquidXML, a platform for managing large corpora of XML documents in large-scale P2P networks. All LiquidXML peers may publish XML documents to be shared with all the network peers. The challenge then is to efficiently (re-)distribute the published content in the network, possibly in overlapping, redundant fragments, to support efficient processing of queries at each peer. The novelty of LiquidXML relies in its adaptive method of choosing which data fragments are stored where, to improve performance. The "liquid" aspect of XML management is twofold: XML data flows from many sources towards many consumers, and its distribution in the network continuously adapts to improve query performance.
An effective approach for mining mobile user habits
Authors: Huanhuan Cao, Tengfei Bao, Qiang Yang, Enhong Chen, Jilei Tian
Venue: CIKM
Year: 2010

The user interaction with the mobile device plays an important role in user habit understanding. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.
On the relationship between novelty and popularity of user-generated content
Authors: David Carmel, Haggai Roitman, Elad Yom-Tov
Venue: CIKM
Year: 2010

This work deals with the task of predicting the popularity of user-generated content. We demonstrate how the novelty of newly published content plays an important role in affecting its popularity. We study three dimensions of novelty: contemporaneous novelty, self novelty, and discussion novelty. We demonstrate the contribution of the new novelty measures to estimating blog-post popularity by predicting the number of comments expected for a fresh post. We further demonstrate how novelty based measures can be utilized for predicting the citation volume of academic papers.
ANITA: a narrative interpretation of taxonomies for their adaptation to text collections
Authors: Mario Cataldi, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2010

Taxonomies embody formalized knowledge and define aggregations between concepts/categories in a given domain, facilitating the organization of the data and making the contents easily accessible to the users. Since taxonomies have significant roles in the data annotation, search and navigation, they are often carefully engineered. However, especially in very dynamic content, they do not necessarily reflect the content knowledge. Thus, in this paper, we propose A Narrative Interpretation of Taxonomies for their Adaptation (ANITA) for re-structuring existing taxonomies to varying application contexts and we evaluate the proposed scheme by user studies that show that the proposed algorithm is able to adapt the taxonomy in a new compact and understandable structure from a human point of view.
Feature subspace transformations for enhancing k-means clustering
Authors: Anirban Chatterjee, Sanjukta Bhowmick, Padma Raghavan
Venue: CIKM
Year: 2010

Unsupervised classification typically concerns identifying clusters of similar entities in an unlabeled dataset. Popular methods include clustering based on (i) distance-based metrics between the entities in the feature space (K-Means), and (ii) combinatorial properties in a weighted graph representation of the dataset (Multilevel K-Means).
Estimating accuracy for text classification tasks on large unlabeled data
Authors: Snigdha Chaturvedi, Tanveer A. Faruquie, L. Venkata Subramaniam, Mukesh K. Mohania
Venue: CIKM
Year: 2010

Rule based systems for processing text data encode the knowledge of a human expert into a rule base to take decisions based on interactions of the input data and the rule base. Similarly, supervised learning based systems can learn patterns present in a given dataset to make decisions on similar and other related data. Performances of both these classes of models are largely dependent on the training examples seen by them, based on which the learning was performed. Even though trained models might fit well on training data, the accuracies they yield on a new test data may be considerably different. Computing the accuracy of the learnt models on new unlabeled datasets is a challenging problem requiring costly labeling, and which is still likely to only cover a subset of the new data because of the large sizes of datasets involved. In this paper, we present a method to estimate the accuracy of a given model on a new dataset without manually labeling the data. We verify our method on large datasets for two shallow text processing tasks: document classification and postal address segmentation, and using both supervised machine learning methods and human generated rule based models.
A probabilistic topic-connection model for automatic image annotation
Authors: Xin Chen, Xiaohua Hu, Zhongna Zhou, Caimei Lu, Gail Rosen, Tingting He, E. K. Park
Venue: CIKM
Year: 2010

The explosive increase of image data on Internet has made it an important, yet very challenging task to index and automatically annotate image data. To achieve that end, sophisticated algorithms and models have been proposed to study the correlation between image content and corresponding text description. Despite the success of previous works, however, researchers are still facing two major difficulties that may undermine their effort of providing reliable and accurate annotations for images. The first difficulty is lacking of comprehensive benchmark image dataset with high quality text descriptions. The second difficulty is lacking of effective way to represent the image content and make it associate with the text descriptions. In our paper, we aim to deal with both problems. To deal with the first problem, we utilize Wikipedia as external knowledge source and enrich the ontology structure of ImageNet database with comprehensive and highly-reliable text descriptions from Wikipedia articles. To address the second problem, we develop a Probabilistic Topic-Connection (PTC) model to represent the connection between latent semantic topic in text description and latent patterns from image feature space. We compare the performance of our model with the currently popular Correspondence LDA (Corr-LDA) model under the same automatic image annotation scenario using cross-validation. Experimental results demonstrate that our model is able to well represent the connection between latent semantic topics and latent patterns in image feature space, thus facilitates knowledge organization and understanding of both image and text descriptions.
An efficient algorithm for mining time interval-based patterns in large database
Authors: Yi-Cheng Chen, Ji-Chiang Jiang, Wen-Chih Peng, Suh-Yin Lee
Venue: CIKM
Year: 2010

Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.
Energy-efficient top-k query processing in wireless sensor networks
Authors: Baichen Chen, Weifa Liang, Rui Zhou, Jeffrey Xu Yu
Venue: CIKM
Year: 2010

Technological advances have enabled the deployment of large-scale sensor networks for environmental monitoring and surveillance purposes. The large volume of data generated by sensors needs to be processed to respond to the users queries. However, efficient processing of queries in sensor networks poses great challenges due to the unique characteristics imposed on sensor networks including slow processing capability, limited storage, and energy-limited batteries, etc. Among various queries, top-k query is one of the fundamental operators in many applications of wireless sensor networks for phenomenon monitoring. In this paper we focus on evaluating top-k queries in an energy-efficient manner such that the network lifetime is maximized. To achieve that, we devise a scalable, filter-based localized evaluation algorithm for top-k query evaluation, which is able to filter out as many unlikely top-k results as possible within the network from transmission. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm on real datasets. The experimental results show that the proposed algorithm outperforms existing algorithms significantly in network lifetime prolongation.
WS-GraphMatching: a web service tool for graph matching
Authors: Qiong Cheng, Mitsunori Ogihara, Jinpeng Wei, Alexander Zelikovsky
Venue: CIKM
Year: 2010

Some emerging applications deal with graph data and relie on graph matching and mining. The service-oriented graph matching and mining tool has been required. In this demo we present the web service tool WS-GraphMatching which supports the efficient and visualized matching of polytrees, series-parallel graphs, and arbitrary graphs with bounded feedback vertex set. Its embedded matching algorithms take in account the similarity of vertex-to-vertex and graph structures, allowing path contraction, vertex deletion, and vertex insertions. It provides one-to-one matching queries as well as queries in batch modes including one-to-many matching mode and many-to-many matching mode. It can be used for predicting unknown structured information, comparing and finding conserved patterns, and resolving ambiguous identification of vertices.
Mixture model label propagation
Authors: Mingmin Chi, Xisheng He, Shipeng Yu
Venue: CIKM
Year: 2010

Usually, we can use a classification or clustering machine learning algorithm to manage knowledge and information retrieval. If we have a small size of known information with a large scale of unknown data, a semi-supervised learning (SSL) algorithm is often preferred. Under the cluster or manifold assumption, usually, the larger amount of unlabeled data are used for learning, the bigger gains of the SSL approaches are achieved. In the paper, we adopt the graph-based SSL algorithm to solve the problem. However the graph-based SSL algorithms are unable to be learnt with large-scale unlabeled samples and originally can only work in a transductive setting. In the paper, we propose a scalable graph-based SSL algorithm to attack the problems aforementioned by Gaussian mixture model label propagation. Experiments conducted on the real dataset illustrate the effectiveness of the proposed algorithm.
Open user schema guided evaluation of streaming RDF queries
Authors: Zhihong Chong, Guilin Qi, Hu Shu, Jiajia Bao, Weiwei Ni, Aoying Zhou
Venue: CIKM
Year: 2010

Performance and scalability are two issues that are becoming increasingly pressing as RDF data model is applied to real-world applications. Because neither vertical nor flat structures of RDF storage can handle frequent schema updates and meanwhile avoid possible long-chain joins, there is no clear winner between these two typical structures. In this paper, we propose an alternative storage schema called open user schema. The open user schema consists of flat tables automatically extracted from RDF query streams. A query is divided into two parts and conquered,respectively, on the flat tables in the open user schema and on the vertical table stored in a backend storage. At the core of this divide and conquer architecture with open user schema, an efficient isomorphism decision algorithm is given to guide a query to related flat tables in the open user schema. Our proposal in essence departs from existing methods in that it can accommodate schema updates without possible long-chain joins. We implement our approach and provide empirical evaluations to demonstrate both efficiency and effectiveness of our approach in evaluating complex RDF queries.
Evaluation of top-k queries in peer-to-peer networks using threshold algorithms
Authors: Ioannis Chrysakis, Constantinos Chalkidis, Dimitris Plexousakis
Venue: CIKM
Year: 2010

In p2p networks, top-k query processing can provide a lot of advantages both in time and bandwidth consumption. Several algorithms have been proposed for the evaluation of top-k queries. A large percentage of them follow the Threshold Approach. We focus on the main adaptations of threshold algorithms fulfilling the requirements of modern p2p applications. We introduce two algorithms optimized for ranking queries in p2p networks and present their characteristics. In the setting of a simulation of large p2p networks, we evaluate the algorithms' performance. Our experiments demonstrate that in some cases a threshold algorithm can improve top-k query processing, while in others it is far more costly. The results show that distributed query processing can be more effective than a simple threshold algorithm in a p2p network.
Index structures for efficiently searching natural language text
Authors: Pirooz Chubak, Davood Rafiei
Venue: CIKM
Year: 2010

Many existing indexes on text work at the document granularity and are not effective in answering the class of queries where the desired answer is only a term or a phrase. In this paper, we study some of the index structures that are capable of answering the class of queries referred to here as wild card queries and perform an analysis of their performance. Our experimental results on a large class of queries from different sources (including query logs and parse trees) and with various datasets reveal some of the performance barriers of these indexes. We then present Word Permuterm Index (WPI) which is an adaptation of the permuterm index for natural language text applications and show that this index supports a wide range of wild card queries, is quick to construct and is highly scalable. Our experimental resultS comparing WPI to alternative methods on a wide range oF wild card queries show a few orders of magnitude performancE improvements for WPI while the memory usage is kept the same for all compared systems.
WikiPop: personalized event detection system based on Wikipedia page view statistics
Authors: Marek Ciglan, Kjetil Nørvåg
Venue: CIKM
Year: 2010

In this paper, we describe WikiPop service, a system designed to detect significant increase of popularity of topics related to users' interests. We exploit Wikipedia page view statistics to identify concepts with significant increase of the interest from the public. Daily, there are thousands of articles with increased popularity; thus, a personalization is in order to provide the user only with results related to his/her interest. The WikiPop system allows a user to define a context by stating a set of Wikipedia articles describing topics of interest. The system is then able to search, for the given date, for popular topics related to the user defined context.
A framework for evaluating database keyword search strategies
Authors: Joel Coffman, Alfred C. Weaver
Venue: CIKM
Year: 2010

With regard to keyword search systems for structured data, research during the past decade has largely focused on performance. Researchers have validated their work using ad hoc experiments that may not reflect real-world workloads. We illustrate the wide deviation in existing evaluations and present an evaluation framework designed to validate the next decade of research in this field. Our comparison of 9 state-of-the-art keyword search systems contradicts the retrieval effectiveness purported by existing evaluations and reinforces the need for standardized evaluation. Our results also suggest that there remains considerable room for improvement in this field. We found that many techniques cannot scale to even moderately-sized datasets that contain roughly a million tuples. Given that existing databases are considerably larger than this threshold, our results motivate the creation of new algorithms and indexing techniques that scale to meet both current and future workloads.
Multi task learning on multiple related networks
Authors: Prakash Mandayam Comar, Pang-Ning Tan, Anil Kumar Jain
Venue: CIKM
Year: 2010

With the rapid proliferation of online social networks, the need for newer class of learning algorithm to simultaneously deal with multiple related networks has become increasingly important. This paper proposes an approach for multi-task learning in multiple related networks, where in we perform different tasks such as classification on one network and clustering on the other. We show that the framework can be extended to incorporate prior information about the correspondences between the clusters and classes in different networks. We have performed experiments on real-world data sets to demonstrate the effectiveness of the proposed framework.
Overview of the 2nd international workshop on search and mining user-generated contents
Authors: José Carlos Cortizo, Francisco M. Carrero, Iván Cantador, José A. Troyano, Paolo Rosso
Venue: CIKM
Year: 2010

This overview introduces the aim of the SMUC 2010 workshop, as well as the list of papers presented in the workshop.
Examining the information retrieval process from an inductive perspective
Authors: Ronan Cummins, Mounia Lalmas, Colm O&apos, Riordan
Venue: CIKM
Year: 2010

Term-weighting functions derived from various models of retrieval aim to model human notions of relevance more accurately. However, there is a lack of analysis of the sources of evidence from which important features of these term weighting schemes originate. In general, features pertaining to these term-weighting schemes can be collected from (1) the document, (2) the entire collection and (3) the query. In this work, we perform an empirical analysis to determine the increase in effectiveness as information from these three different sources becomes more accurate.
Taxonomic clustering of web service for efficient discovery
Authors: Sourish Dasgupta, Satish Bhat, Yugyung Lee
Venue: CIKM
Year: 2010

The World Wide Web (WWW) has become a major platform for hosting, discovering, and composing web services. Web service clustering is a technique for efficiently facilitating web service discovery. Most web service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper we propose a taxonomic clustering algorithm for grouping functionally similar web services. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.
TAER: time-aware entity retrieval-exploiting the past to find relevant entities in news articles
Authors: Gianluca Demartini, Malik Muhammad Saad Missen, Roi Blanco, Hugo Zaragoza
Venue: CIKM
Year: 2010

Retrieving entities instead of just documents has become an important task for search engines. In this paper we study entity retrieval for news applications, and in particular the importance of the news trail history (i.e., past related articles) in determining the relevant entities in current articles. This is an important problem in applications that display retrieved entities to the user, together with the news article.
A hierarchical approach to reachability query answering in very large graph databases
Authors: Saikat K. Dey, Hasan M. Jamil
Venue: CIKM
Year: 2010

The cost of reachability query computation using traditional algorithms such as depth first search or transitive closure has been found to be prohibitive and unacceptable in massive graphs such as biological interaction networks, or pathways. Contemporary solutions mainly take two distinct approaches - precompute reachability in the form of transitive closure (trade space for time) or use state space search (trade time for space). A middle ground among the two approaches has recently gained popularity. It precomputes part of the reachability information as a complex index so that most queries can be answered within a reasonable time. In this approach, the main cost now is creation of the index, and response generation using it as well as the space needed to materialize the structure. Most contemporary solutions favor a combination of these costs to be efficient for a class of applications. In this paper, we propose a hierarchical index based on graph segmentation to reduce index size without sacrificing query efficiency. We present experimental evidence to show that our approach can achieve significant space savings, and improve efficiency. We also show that our index need not be rebuilt for a large class of updates, a feature missing in all other contemporary systems.
Faceted search and browsing of audio content on spoken web
Authors: Mamadou Diao, Sougata Mukherjea, Nitendra Rajput, Kundan Srivastava
Venue: CIKM
Year: 2010

Spoken Web is a web of VoiceSites that can be accessed by a phone. The content in a VoiceSite is audio. Therefore Spoken Web provides an alternate to the World Wide Web (WWW) in developing regions where low Internet penetration and low literacy are barriers to accessing the conventional WWW. Searching of audio content in Spoken Web through an audio query-result interface presents two key challenges: indexing of audio content is not accurate, and the presentation of results in audio is sequential, and therefore cumbersome. In this paper, we apply the concepts of faceted search and browsing to the SpokenWeb search problem. We use the concepts of facets to index the meta-data associated with the audio content. We provide a mechanism to rank the facets based on the search results. We develop an interactive query interface that enables easy browsing of search results through the top ranked facets. To our knowledge, this is the first system to use the concepts of facets in audio search, and the first solution that provides an audio search for the rural population.