OpenRaiser/Intern-Atlas huggingface dataset:
Incorporating query difference for learning retrieval functions in world wide web search
Authors: Hongyuan Zha, Zhaohui Zheng, Haoying Fu, Gordon Sun
Venue: CIKM
Year: 2006

We discuss information retrieval methods that aim at serving a diverse stream of user queries such as those submitted to commercial search engines. We propose methods that emphasize the importance of taking into consideration of query difference in learning effective retrieval functions. We formulate the problem as a multi-task learning problem using a risk minimization framework. In particular, we show how to calibrate the empirical risk to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we also propose an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. We work out the details for both L1 and L2 regularization cases, and provide convergence analysis for the alternating optimization method for the special case when the retrieval functions belong to a reproducing kernel Hilbert space. We illustrate the effectiveness of the proposed methods using modeling data extracted from a commercial search engine. We also point out how the current framework can be extended in future research.
Finding highly correlated pairs efficiently with powerful pruning
Authors: Jian Zhang, Joan Feigenbaum
Venue: CIKM
Year: 2006

We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attributes) whose (Pearson) correlation coefficients are greater than the threshold. Correlation analysis is an important step in many statistical and knowledge-discovery tasks. Normally, the number of highly correlated pairs is quite small compared to the total number of pairs. Identifying highly correlated pairs in a naive way by computing the correlation coefficients for all the pairs is wasteful. With massive data sets, where the total number of pairs may exceed the main-memory capacity, the computational cost of the naive method is prohibitive. In their KDD'04 paper [15], Hui Xiong et al. address this problem by proposing the TAPER algorithm. The algorithm goes through the data set in two passes. It uses the first pass to generate a set of candidate pairs whose correlation coefficients are then computed directly in the second pass. The efficiency of the algorithm depends greatly on the selectivity (pruning power) of its candidate-generating stage.In this work, we adopt the general framework of the TAPER algorithm but propose a different candidate-generation method. For a pair of items, TAPER's candidate-generation method considers only the frequencies (supports) of individual items. Our method also considers the frequency (support) of the pair but does not explicitly count this frequency (support). We give a simple randomized algorithm whose false-negative probability is negligible. The space and time complexities of generating the candidate set in our algorithm are asymptotically the same as TAPER's. We conduct experiments on synthesized and real data. The results show that our algorithm produces a greatly reduced candidate set - one that can be several orders of magnitude smaller than that generated by TAPER. Because of this, our algorithm uses much less memory and can be faster. The former is critical for dealing with massive data.
Mining coherent patterns from heterogeneous microarray data
Authors: Xiang Zhang, Wei Wang
Venue: CIKM
Year: 2006

Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naïve solution for it.
Re-ranking search results using query logs
Authors: Ziming Zhuang, Silviu Cucerzan
Venue: CIKM
Year: 2006

This work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get high ranks for an underspecified query due to their freshness and to the large number of pages that match the query, despite the fact that a large number of users have searched for parts of their content recently. We propose a novel method, Q-Rank, to effectively refine the ranking of search results for any given query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking system of a large-scale commercial Web search engine, being able to improve the relevance of search results for 82% of the queries.
Towards interactive indexing for large Chinese calligraphic character databases
Authors: Yi Zhuang, Yueting Zhuang, Qing Li, Lei Chen
Venue: CIKM
Year: 2006

In this paper, based on a novel shape-similarity-based retrieval method, we propose an interactive partial-distance-map (PDM)- based high-dimensional indexing scheme to speed up the retrieval performance of the large Chinese calligraphic character databases. Specifically, we use the approximate minimal bounding hyper- sphere of query character to search the PDM and utilize the users' relevance feedback to refine the search process. We conduct comprehensive experiments to testify the efficiency and effectiveness of the proposed method.
Automatic computation of semantic proximity using taxonomic knowledge
Authors: Cai-Nicolas Ziegler, Kai Simon, Georg Lausen
Venue: CIKM
Year: 2006

Taxonomic measures of semantic proximity allow us to compute the relatedness of two concepts. These metrics are versatile instruments required for diverse applications, e.g., the Semantic Web, linguistics, and also text mining. However, most approaches are only geared towards hand-crafted taxonomic dictionaries such as WordNet, which only feature a limited fraction of real-world concepts. More specific concepts, and particularly instances of concepts, i.e., names of artists, locations, brand names, etc., are not covered.The contributions of this paper are two fold. First, we introduce a framework based on Google and the Open Directory Project (ODP), enabling us to derive the semantic proximity between arbitrary concepts and instances. Second, we introduce a new taxonomy-driven proximity metric tailored for our framework. Studies with human subjects corroborate our hypothesis that our new metric outperforms benchmark semantic proximity metrics and comes close to human judgement.
Bayesian adaptive user profiling with explicit & implicit feedback
Authors: Philip Zigoris, Yi Zhang
Venue: CIKM
Year: 2006

Research in information retrieval is now moving into a personalized scenario where a retrieval or filtering system maintains a separate user profile for each user. In this framework, information delivered to the user can be automatically personalized and catered to individual user's information needs. However, a practical concern for such a personalized system is the "cold start problem": any user new to the system must endure poor initial performance until sufficient feedback from that user is provided.To solve this problem, we use both explicit and implicit feedback to build a user's profile and use Bayesian hierarchical methods to borrow information from existing users. We analyze the usefulness of implicit feedback and the adaptive performance of the model on two data sets gathered from user studies where users' interaction with a document, or implicit feedback, were recorded along with explicit feedback. Our results are two-fold: first, we demonstrate that the Bayesian modeling approach effectively trades off between shared and user-specific information, alleviating poor initial performance for each user. Second, we find that implicit feedback has very limited unstable predictive value by itself and only marginal value when combined with explicit feedback.
An automatic approach to construct domain-specific web portals
Authors: Ismail Sengör Altingövde, Rifat Ozcan, Suleyman Cetintas, Hakan Yilmaz, Özgür Ulusoy
Venue: CIKM
Year: 2007

We describe the architecture of an automatic domain-specific Web portal construction system. The system has three major components: i) a focused crawler that collects the domain-specific pages on the Web, ii) an information extraction engine that extracts useful fields from these Web pages, and iii) a query engine that allows both typical keyword based queries on the pages and advanced queries on the extracted data fields. We present a prototype system that works for the course homepages domain on the Web. A user study with the prototype system shows that our approach produces high quality results and achieves better precision figures than the typical keyword based search.
Hierarchical two-tier ensemble learning: a new paradigm for network intrusion detection
Authors: Morteza Analoui, Behrouz Minaei-Bidgoli, Mohammad Hossein Rezvani
Venue: CIKM
Year: 2007

Intrusion detection is a mechanism of providing security to computer networks. Almost all of traditional intelligent intrusion detection systems (IDSs) use a single approach to distinguish normal behavior patterns from attack signatures. Moreover these systems have a high false alarm rate and high cost. The combination of multiple classifiers usually exhibits lower false alarm and overall error rate than individual decisions. On the other hand, the combination of classifiers trained on different feature sets could provide better performances than each single classifier. In this paper, a hierarchical two-level combiner is proposed to detect network intrusions using multiple well-known and efficient base classifiers. The proposed combiner exploits the different recognition capabilities provided by the independent feature representations in the first level as well as the agreement among the classifiers in the second level.
Very efficient mining of distance-based outliers
Authors: Fabrizio Angiulli, Fabio Fassetti
Venue: CIKM
Year: 2007

In this work a novel algorithm, named DOLPHIN, for detecting distance-based outliers is presented.
Detecting distance-based outliers in streams of data
Authors: Fabrizio Angiulli, Fabio Fassetti
Venue: CIKM
Year: 2007

In this work a method for detecting distance-based outliers in data streams is presented. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. Two algorithms are presented. The first one exactly answers outlier queries, but has larger space requirements. The second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. Several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions.
Inferring document relevance from incomplete information
Authors: Javed A. Aslam, Emine Yilmaz
Venue: CIKM
Year: 2007

Recent work has shown that average precision can be accurately estimated from a small random sample of judged documents. Unfortunately, such "random pools" cannot be used to evaluate retrieval measures in any standard way. In this work, we show that given such estimates of average precision, one can accurately infer the relevances of the remaining unjudged documents, thus obtaining a fully judged pool that can be used in standard ways for system evaluation of all kinds. Using TREC data, we demonstrate that our inferred judged pools are well correlated with assessor judgments, and we further demonstrate that our inferred pools can be used to accurately infer precision recall curves and all commonly used measures of retrieval performance.
Language models, probability of relevance and relevance likelihood
Authors: Richard Bache, Mark Baillie, Fabio Crestani
Venue: CIKM
Year: 2007

This paper proposes a measure of relevance likelihood derived specifically for language models. Such a measure may be used to guide a user on how far to browse through the list of retrieved items or for pseudo-relevance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will contain fragments of this ideal document. Thus, in deriving this measure we propose a novel way of capturing relevance in Language Modelling.
Efficient interactive query expansion with complete search
Authors: Hannah Bast, Debapriyo Majumdar, Ingmar Weber
Venue: CIKM
Year: 2007

We present an efficient realization of the following interactive search engine \nfeature: as the user is typing the query, words that are related to the last \nquery word and that would lead to good hits are suggested, as well as selected \nsuch hits. The realization has three parts: (i) building clusters of related \nterms, (ii) adding this information as artificial words to the index such that \n(iii) the described feature reduces to an instance of prefix search and \ncompletion. An efficient solution for the latter is provided by the \nCompleteSearch engine, with which we have integrated the proposed feature. For \nbuilding the clusters of related terms we propose a variant of latent semantic \nindexing that, unlike standard approaches, is completely transparent to the \nuser. By experiments on two large test-collections, we demonstrate that the \nfeature is provided at only a slight increase in query processing time and \nindex size.
Cufres: clustering using fuzzy representative eventsselection for the fault recognition problem intelecommunication networks
Authors: Jacques-H. Bellec, M. Tahar Kechadi
Venue: CIKM
Year: 2007

In this paper we introduce an efficient clustering algorithm embedded in a novel approach for solving the problem of faults identification in large telecommunication networks. Our algorithm is especially designed for the event correlation problem taking into account comprehensive information about the system behaviour. Although alarms are usually useful for identifying faults in such systems, their large number overloads the current management systems, making it extremely difficult to provide an answer within a reasonable response time. The alarm flow presents some interesting characteristics like alarm storm and alarm cascade. For instance, a single fault may result in a large number of alarms, and it is often very difficult to isolate the true cause of the fault. One way of overcoming this problem is to analyze, interpret and reduce the number of these alarms before trying to localize the faults. In this paper, we present a new original algorithm, and compare it with some available clustering algorithms by experimenting them with some samples of both simulated and real data from Ericsson's network.
Efficient evaluation of high-selective xml twig patterns with parent child edges in tree-unaware rdbms
Authors: Sourav S. Bhowmick, Erwin Leonardi, Hongmei Sun
Venue: CIKM
Year: 2007

Recent study showed that native twig join algorithms and tree-aware relational framework significantly outperform tree-unaware approaches in evaluating structural relationships in xml twig queries. In this paper, we present an efficient strategy to evaluate high-selective twig queries containing only parent-child relationships in a tree-unaware relational environment. Our scheme is built on top of our Sucxent++ system. We show that by exploiting the encoding scheme of Sucxent++, we can devise efficient strategy for evaluating such twig queries. Extensive performance studies on various data sets and queries show that our approach performs better than a representative tree-unaware approach (Global-Order) and a state-of-theart native twig join algorithm (TJFast) on all benchmark queries with the highest observed gain factors being 243 and 95, respectively. Additionally, our approach reduces significantly the performance gap between tree-aware and tree-unaware approaches and even outperforms a tree-aware approach (MonetDB/XQuery) for certain high-selective twig queries. We also report our insights to the plan choices a relational optimizer made during twig query evaluation by visually characterizing its behavior over the relational selectivity space. 2 1
Conceptual modeling by analogy and metaphor
Authors: Karin Koogan Breitman, Simone Diniz Junqueira Barbosa, Marco A. Casanova, António L. Furtado
Venue: CIKM
Year: 2007

Metaphor is not merely a rhetorical device, characteristic of language alone, but rather a fundamental feature of the human conceptual system. A metaphor is understood by finding an analogy mapping between two domains. This paper argues that analogy mappings facilitate conceptual modeling by allowing the designer to reinterpret fragments of familiar conceptual models in other contexts. The contributions of the paper are expressed within the tradition of the Entity-Relation model.
Communities in graphs and hypergraphs
Authors: Michael Brinkmeier, Jeremias Werner, Sven Recknagel
Venue: CIKM
Year: 2007

Abstract. In this paper we define a type of cohesive subgroups – called communities – in hypergraphs, based on the edge connectivity of subhy-pergraphs. We describe a simple algorithm for the construction of these sets and show, based on examples from image segmentation and infor-mation retrieval, that these groups may be useful for the analysis and accessibility of large graphs and hypergraphs. 1
Self-correcting queries for xml
Authors: Tali Brodianskiy, Sara Cohen
Venue: CIKM
Year: 2007

It has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insufficiently familiar with the labels appearing the documents, or may not be intimately aware of the hierarchical structure of the documents. This difficulty may be compounded by the fact that errors in query formulation lead to an empty answer, and not to some sort of compilation error.
Leveraging context in user-centric entity detection systems
Authors: Vadim von Brzeski, Utku Irmak, Reiner Kraft
Venue: CIKM
Year: 2007

A user-centric entity detection system is one in which the primary consumer of the detected entities is a person who can perform actions on the detected entities (e.g. perform a search, view a map, shop, etc.). We contrast this with machine-centric detection systems where the primary consumer of the detected entities is a machine. Machine-centric detection systems typically focus on the quantity of detected entities, measured by precision and recall metrics, with the goal of correctly identifying every single entity in a document.
Index compression is good, especially for random access
Authors: Stefan Büttcher, Charles L. A. Clarke
Venue: CIKM
Year: 2007

Index compression techniques are known to substantially decrease the storage requirements of a text retrieval system. As a side-effect, they may increase its retrieval performance by reducing disk I/O overhead. Despite this advantage, developers sometimes choose to store index data in uncompressed form, in order to not obstruct random access into each index term's postings list.
Extending query translation to cross-language query expansion with markov chain models
Authors: Guihong Cao, Jianfeng Gao, Jian-Yun Nie, Jing Bai
Venue: CIKM
Year: 2007

Dictionary-based approaches to query translation have been widely used in Cross-Language Information Retrieval (CLIR) experiments. However, translation has been not only limited by the coverage of the dictionary, but also affected by translation ambiguities. In this paper we propose a novel method of query translation that combines other types of term relation to complement the dictionary-based translation. This allows extending the literal query translation to related words, which produce a beneficial effect of query expansion in CLIR. In this paper, we model query translation by Markov Chains (MC), where query translation is viewed as a process of expanding query terms to their semantically similar terms in a different language. In MC, terms and their relationships are modeled as a directed graph, and query translation is performed as a random walk in the graph, which propagates probabilities to related terms. This framework allows us to incorporating different types of term relation, either between two languages or within the source or target languages. In addition, the iterative training process of MC allows us to attribute higher probabilities to the target terms more related to the original query, thus offers a solution to the translation ambiguity problem. We evaluated our method on three CLIR benchmark collections, and obtained significant improvements over traditional dictionary-based approaches.
Document retrieval for question answering: a quantitative evaluation of text preprocessing
Authors: Gracinda Carvalho, David Martins de Matos, Vítor Rocio
Venue: CIKM
Year: 2007

Question Answering (QA) has been an area of interest for researchers, in part motivated by the international QA evaluation forums, namely the Text REtrieval Conference (TREC), and more recently, the Cross Language Evaluation Forum (CLEF) through QA@CLEF, that since 2004 includes the Portuguese language. In these forums, a collection of written documents is provided, as well as a set of questions, which are to be answered by the participating systems. Each system is evaluated by its capacity to answer the questions, as a whole, and there are relatively few results published that focus on the performance of its different components and their influence on the overall system performance. That is the case of the Information Retrieval (IR) component, which is broadly used in QA systems.
Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports
Authors: Jean-Pierre Chevallet, Joo-Hwee Lim, Diem Thi Hoang Le
Venue: CIKM
Year: 2007

Conceptual Indexing is a way to produce only one index for many multilingual documents. Inter-Media conceptual indexing promotes the use of common concepts between two media in order to use a single index for several media. In this paper we explore such an advance indexing point of view. We show the benefit of an automatic conceptual indexing for texts and its extension for text and image documents. Tests are conducted on the multilingual image and text medical document corpus of the CLEF initiative, where we obtain best results on text in 2005 and 2006, and show promising results on images, and best results for the combination of image and text.