OpenRaiser/Intern-Atlas huggingface dataset:
Mining and ranking streams of news stories using cross-stream sequential patterns
Authors: Robert Gwadera, Fabio Crestani
Venue: CIKM
Year: 2009

We present a new method for mining and ranking streams of news stories using cross-stream sequential patterns and content similarity. In particular, we focus on stories reporting the same event across the streams within a given time window, where an event is defined as a specific thing that happens at a specific time and place. For every discovered cluster of stories reporting the same event we create an itemset-sequence consisting of stream identifiers of the stories in the cluster, where the sequence is ordered according to the timestamps of the stories. Furthermore, we record exact timestamps and content similarities between the respective stories. Given such a collection of itemset-sequences we use it for two tasks: (I) to discover recurrent temporal publishing patterns between the news streams in terms of frequent sequential patterns and content similarity and (II) to rank the streams of news stories with respect to timeliness of reporting important events and content authority. We demonstrate the applicability of the presented method on a multi-stream of news stories was gathered from RSS feeds of major world news agencies.
Evaluating top-k queries over incomplete data streams
Authors: Parisa Haghani, Sebastian Michel, Karl Aberer
Venue: CIKM
Year: 2009

We study the problem of continuous monitoring of top-k queries over multiple non-synchronized streams. Assuming a sliding window model, this general problem has been a well addressed research topic in recent years. Most approaches, however, assume synchronized streams where all attributes of an object are known simultaneously to the query processing engine. In many streaming scenarios though, different attributes of an item are reported in separate non-synchronized streams which do not allow for exact score calculations. We present how the traditional notion of object dominance changes in this case such that the k dominance set still includes all and only those objects which have a chance of being among the top-k results in their life time. Based on this, we propose an exact algorithm which builds on generating multiple instances of the same object in a way that enables efficient object pruning. We show that even with object pruning the necessary storage for exact evaluation of top-k queries is linear in the size of the sliding window. As data should reside in main memory to provide fast answers in an online fashion and cope with high stream rates, storing all this data may not be possible with limited resources. We present an approximate algorithm which leverages correlation statistics of pairs of streams to evict more objects while maintaining accuracy. We evaluate the efficiency of our proposed algorithms with extensive experiments.
A framework for semantic link discovery over relational data
Authors: Oktie Hassanzadeh, Anastasios Kementsietsidis, Lipyeow Lim, Renée J. Miller, Min Wang
Venue: CIKM
Year: 2009

Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted the paramount importance of establishing semantic links among web data sources. Currently, LOD sources provide billions of RDF triples, but only millions of links between data sources. Many of these data sources are published using tools that operate over relational data stored in a standard RDBMS. In this paper, we present a framework for discovery of semantic links from relational data. Our framework is based on declarative specification of linkage requirements by a user. We illustrate the use of our framework using several link discovery algorithms on a real world scenario. Our framework allows data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.
Detecting topic evolution in scientific literature: how can citations help?
Authors: Qi He, Bi Chen, Jian Pei, Baojun Qiu, Prasenjit Mitra, C. Lee Giles
Venue: CIKM
Year: 2009

Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.
Graph-based transfer learning
Authors: Jingrui He, Yan Liu, Richard D. Lawrence
Venue: CIKM
Year: 2009

Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. In this paper, we propose a graph-based transfer learning framework. It propagates the label information from the source domain to the target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bi-partite graph. Our framework is semi-supervised and non-parametric in nature and thus more flexible. We also develop an iterative algorithm so that our framework is scalable to large-scale applications. It enjoys the theoretical property of convergence. Compared with existing transfer learning methods, the proposed framework propagates the label information to both the features irrelevant to the source domain and the unlabeled examples in the target omain via the common features in a principled way. Experimental results on 3 real data sets demonstrate the effectiveness of our algorithm.
Cluster based rank query over multidimensional data streams
Authors: Dengcheng He, Yongluan Zhou, Lidan Shou, Gang Chen
Venue: CIKM
Year: 2009

Many data stream monitoring applications involve rank queries and hence a number of efficient evaluation algorithms are proposed recently. Most of these techniques assume that rank queries are executed directly over the whole data space. However, we observe that many applications often require to perform clustering over the data streams before rank queries are run on each cluster. To address the problem, we propose a novel algorithm for integral clustering and ranking processing and we refer to such integrated queries as cluster-based rank queries. The algorithm includes two phases, namely the online phase which maintains the required data structures and statistics, and the query phase which uses these data structures to process queries. Extensive experiments indicate that the proposed algorithm is efficient in both space consumption and query processing.
The impact of document structure on keyphrase extraction
Authors: Katja Hofmann, Manos Tsagkias, Edgar Meij, Maarten de Rijke
Venue: CIKM
Year: 2009

Keyphrases are short phrases that reflect the main topic of a document. Because manually annotating documents with keyphrases is a time-consuming process, several automatic approaches have been developed. Typically, candidate phrases are extracted using features such as position or frequency in the document text. Document structure may contain useful information about which parts or phrases of a document are important, but has rarely been considered as a source of information for keyphrase extraction.
Consistent on-line classification of dbs workload events
Authors: Marc Holze, Claas Gaidies, Norbert Ritter
Venue: CIKM
Year: 2009

An important goal of self-managing databases is the autonomic adaptation of the database configuration to evolving workloads. However, the diversity of SQL statements in real-world workloads typically causes the required analysis overhead to be prohibitive for a continuous workload analysis. The workload classification presented in this paper reduces the workload analysis overhead by grouping similar workload events into classes. Our approach employs clustering techniques based upon a general distance function for DBS workload events. To be applicable for a continuous workload analysis, our workload classification specifically addresses a stream-based, lightweight operation, a controllable loss of quality, and self-management.
Effective anonymization of query logs
Authors: Yuan Hong, Xiaoyun He, Jaideep Vaidya, Nabil R. Adam, Vijayalakshmi Atluri
Venue: CIKM
Year: 2009

User search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to protect the identity of users. Publishing such inadequately anonymized data can cause severe breach of privacy. While significant effort has been expended on coming up with anonymity models and techniques for microdata, there is little corresponding work for query log data. Query logs are different in several important aspects, such as the diversity of queries and the causes of privacy breach. This necessitates the need to design privacy models and techniques specific to this environment. This paper takes a first cut at tackling this challenge. Our main contribution is to define effective anonymization models for query log data along with proposing techniques to achieve such anonymization. We analyze the inherent utility and privacy tradeoff, and experimentally validate the performance of our techniques.
Intention-focused active reranking for image object retrieval
Authors: Jen-Hao Hsiao, Ming-Syan Chen
Venue: CIKM
Year: 2009

We consider the problem of ranking refinement for image object retrieval, whose goal is to improve an existing ranking function by a small number of labeled instances. To retrieve the relevant image object, one state-of-the-art approach is to use the relevance feedback: it first ranks the images in database based on a given ranking function (i.e., base ranker), and then rerank the initial result by further introducing user's feedback information. The key challenge of combining the information from the base ranker and user's feedback comes from the fact that the base ranker tends to give an imperfect result and the information obtained from user's feedback tends to be very noisy. This paper describes an Intention-Focused Active Reranking, an approach for automatically finding the right information to re-estimate the query model. Three novel strategies are proposed to boost the performance of the base ranker: (1) an active selection criterion, which obtains a small number of feedback images that are the most informative to the base ranker for user labeling; (2) the user intention verification, which captures the user's intention in object level to alleviate the query drift problem; (3) a discriminative query model re-estimation, which augments the generative approach with a model of the discriminative information conveyed by positive and negative feedback information. Experiments on a real world data set demonstrate the effectiveness of the proposed approach and furthermore it significantly outperforms the baseline visual bag-of-words retrieval.
Data mining in bioinformatics: challenges and opportunities
Authors: Xiaohua Hu
Venue: CIKM
Year: 2009

In this talk I will discuss some data mining techniques and methods in the bioinformatics domain, what are the main challenges and what are the opportunities. I will cover some of the issues related to biomedical literature mining, bioinformatics data integration and biological network analysis and simulation. In biomedical literature mining, I will discuss the effective information retrieval and large-scale information extraction from biomedical literatures. I will also share my view of the semantic-based approach for data integration for bioinformatics domain. In the end, I will talk about the various approaches for biological network analysis and simulation.
RSS watchdog: an instant event monitor on real online news streams
Authors: Chih-Lin Hu, Chung-Kuang Chou
Venue: CIKM
Year: 2009

This paper introduces the RSS Watchdog system, which is capable of news clustering and instant event monitoring over multiple real and online RSS news streams. We briefly mention software architecture design, technical implementation, and prototype demonstration. In addition, the results of real case studies are presented to notice the RSS Watchdog's functionality
Modeling context-dependent information
Authors: Jie Hu, Mengchi Liu
Venue: CIKM
Year: 2009

Object properties are often based on their contexts, and contexts can be nested to form complex context-dependent information. Existing data models cannot naturally and directly represent such context-dependent information. In this paper, we propose a novel mechanism called context constructor in an object-oriented framework to solve this problem.
A unified relevance model for opinion retrieval
Authors: Xuanjing Huang, W. Bruce Croft
Venue: CIKM
Year: 2009

Representing the information need is the greatest challenge for opinion retrieval. Typical queries for opinion retrieval are composed of either just content words, or content words with a small number of cue "opinion" words. Both are inadequate for retrieving opinionated documents. In this paper, we develop a general formal framework--the opinion relevance model--to represent an information need for opinion retrieval. We explore a series of methods to automatically identify the most appropriate opinion words for query expansion, including using query independent sentiment resources. We also propose a relevance feedback-based approach to extract opinion words. Both query-independent and query-dependent methods can also be integrated into a more effective mixture relevance model. Finally, opinion retrieval experiments are presented for the Blog06 and COAE08 text collections. The results show that, significant improvements can always be obtained by this opinion relevance model whether sentiment resources are available or not.
Analyzing and evaluating query reformulation strategies in web search logs
Authors: Jeff Huang, Efthimis N. Efthimiadis
Venue: CIKM
Year: 2009

Users frequently modify a previous search query in hope of retrieving better results. These modifications are called query reformulations or query refinements. Existing research has studied how web search engines can propose reformulations, but has given less attention to how people perform query reformulations. In this paper, we aim to better understand how web searchers refine queries and form a theoretical foundation for query reformulation. We study users' reformulation strategies in the context of the AOL query logs. We create a taxonomy of query refinement strategies and build a high precision rule-based classifier to detect each type of reformulation. Effectiveness of reformulations is measured using user click behavior. Most reformulation strategies result in some benefit to the user. Certain strategies like add/remove words, word substitution, acronym expansion, and spelling correction are more likely to cause clicks, especially on higher ranked results. In contrast, users often click the same result as their previous query or select no results when forming acronyms and reordering words. Perhaps the most surprising finding is that some reformulations are better suited to helping users when the current results are already fruitful, while other reformulations are more effective when the results are lacking. Our findings inform the design of applications that can assist searchers; examples are described in this paper.
Smoothing document language model with local word graph
Authors: Yunping Huang, Le Sun, Jian-Yun Nie
Venue: CIKM
Year: 2009

Smoothing document model with word graph is a new and effective method in information retrieval. Word graph can naturally incorporate the dependency between the words; random walk algorithm based on the graph can be used to estimate the weight of each vertex. In this paper, we present a new way to construct a local word graph for smoothing document model, which exploits the document's k nearest neighbors: the vertices represent the words in the document and its k nearest neighbors, and the weights of the edges are estimated through word co-occurrence in the local document set. We argue that word graph is a key factor to the performance in graph-based smoothing method. By using the local document set, we can obtain a document specific word graph, and achieve better retrieval performance. Experimental results on three TREC collections show that our proposed approach is effective.
Clustering object moving patterns for prediction-based object tracking sensor networks
Authors: Chih-Chieh Hung, Wen-Chih Peng
Venue: CIKM
Year: 2009

Prior works have shown that probabilistic suffix trees (PST) could predict accurately the moving behaviors of objects for prediction-based object tracking sensor networks. However, maintaining PSTs for objects incurs a considerable amount of storage spaces for resource-constrained sensor nodes. In this paper, we derive a distance function between two PSTs and propose an algorithm to determine the similarity between them. By the distance between PSTs, we propose a clustering algorithm to partition objects with similar moving behaviors into groups. Furthermore, for each group, one PST is selected to predict movements of objects within one group. Experimental results show that our proposed approaches not only effectively reduce the storage cost but also provide good prediction accuracy.
MKEM: a multi-level knowledge emergence model for mining undiscovered public knowledge
Authors: Ali Zeeshan Ijaz, Min Song, Doheon Lee
Venue: CIKM
Year: 2009

Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypothesesand expand knowledge. In this paper, we propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model.
Real-word spelling correction using Google web 1Tn-gram data set
Authors: Aminul Islam, Diana Inkpen
Venue: CIKM
Year: 2009

We present a method for correcting real-word spelling errors using the Google Web 1T n-gram data set and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Our method is focused mainly on how to improve the correction recall (the fraction of errors corrected) while keeping the correction precision (the fraction of suggestions that are correct) as high as possible. Evaluation results on a standard data set show that our method performs very well.
Interactive relevance feedback with graded relevance and sentence extraction: simulated user experiments
Authors: Kalervo Järvelin
Venue: CIKM
Year: 2009

Research on relevance feedback (RFB) in information retrieval (IR) has given mixed results. Success in RFB seems to depend on the searcher's willingness to provide feedback and ability to identify relevant documents or query keys. The paper is based on simulating many user scenarios regarding the amount and quality of RFB. In addition, we experiment with query-biased sentence extraction for query reformulation. The baselines are initial no-feedback queries and queries based on pseudo-relevance feedback. The core question is: under which conditions would RFB based on sentence extraction be successful? The answer depends on user's behavior, implementation of feedback query formulation, and the evaluation methods. A small amount of feedback from a short browsing window seems to improve the final ranking the most. Longer browsing allows more feedback and better queries but also consumes the available relevant documents.
Linear predictive coding representation of correlated mutation for protein sequence alignment
Authors: Chan-seok Jeong, Dongsup Kim
Venue: CIKM
Year: 2009

Although both conservation and correlated mutation (CM) are important information reflecting the different sorts of context in multiple sequence alignment, most of alignment methods use sequence profiles that only represent conservation. There is no general way to represent correlated mutation and incorporate it with sequence alignment yet. We develop a novel method, CM profile, to represent correlated mutation as the spectral feature derived by using linear predictive coding where correlated mutations among different positions are represented by a fixed number of values. We combine CM profile with conventional sequence profile to improve alignment quality. For distantly related protein pairs, using CM profile improves the profile-profile alignment with or without predicted secondary structure. Especially, at superfamily level, combining CM profile with sequence profile improves profile-profile alignment by 9.5% while predicted secondary structure does by 6.0%. More significantly, using both of them improves profile-profile alignment by 13.9%. We also exemplify the effectiveness of CM profile by demonstrating that the resulting alignment preserves share coevolution and contacts. Because of the generality of CM profile, it can be used for other bioinformatics applications in the same way of using sequence profile.
ExSearch: a novel vertical search engine for online barter business
Authors: Lei Ji, Jun Yan, Ning Liu, Wen Zhang, Weiguo Fan, Zheng Chen
Venue: CIKM
Year: 2009

E-Commerce has shown its exponentially-growing business value in the past decade. However, in contrast to the successful examples in online sales, such as Amazon1 and eBay2, the online barter business is still underexplored due to the lack of corresponding information aggregation service. In this paper, we design and implement a novel vertical search engine, called ExSearch, to aggregate online barter information for developing the barter market. Different from classical general purpose Web search engines, ExSearch adopts a focused crawler to gather related information from various websites. We propose to automatically extract the barter information from free-text Web pages such that the unstructured information is represented in structured databases. In addition, we utilize the data mining techniques such as regression to fulfill the missing information, which cannot be extracted from the Web pages. Finally, we validate and rank the search results according to user queries. Experimental results show that each component module in our proposed ExSearch system is efficient and effective. The volunteer users are satisfied by and interested in this novel vertical search engine.