OpenRaiser/Intern-Atlas huggingface dataset:
Web unit mining: finding and classifying subgraphs of web pages
Authors: Aixin Sun, Ee-Peng Lim
Venue: CIKM
Year: 2003

In web classification, most researchers assume that the objects to classify are individual web pages from one or more web sites. In practice, the assumption is too restrictive since a web page itself may not always correspond to a concept instance of some semantic concept (or category) given to the classification task. In this paper, we want to relax this assumption and allow a concept instance to be represented by a subgraph of web pages or a set of web pages. We identify several new issues to be addressed when the assumption is removed, and formulate the web unit mining problem. We also propose an iterative web unit mining (iWUM) method that first finds subgraphs of web pages using some knowledge about web site structure. From these web subgraphs, web units are constructed and classified into semantic concepts (or categories) in an iterative manner. Our experiments using the WebKB dataset showed that iWUM improves the overall classification performance and works very well on the more structured parts of a web site.
The power-method: a comprehensive estimation technique for multi-dimensional queries
Authors: Yufei Tao, Christos Faloutsos, Dimitris Papadias
Venue: CIKM
Year: 2003

Existing estimation approaches for multi-dimensional databases often rely on the assumption that data distribution in a small region is uniform, which seldom holds in practice. Moreover, their applicability is limited to specific estimation tasks under certain distance metric. This paper develops the Power-method, a comprehensive technique applicable to a wide range of query optimization problems under various metrics. The Power-method eliminates the local uniformity assumption and is accurate even in scenarios where existing approaches completely fail. Furthermore, it performs estimation by evaluating only one simple formula with minimal computational overhead. Extensive experiments confirm that the Power-method outperforms previous techniques in terms of accuracy and applicability to various optimization scenarios.
Visual structures for image browsing
Authors: Ricardo da Silva Torres, Celmar G. Silva, Claudia Bauzer Medeiros, Heloisa Vieira da Rocha
Venue: CIKM
Year: 2003

Content-Based Image Retrieval (CBIR) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. Database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. Comparatively little work has been done on designing user interfaces for CBIR systems. This, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. This paper describes an initial effort to fill this gap, combining recent research in CBIR and Information Visualization, studied from a Human-Computer Interface perspective. It presents two visualization techniques based on Spiral and Concentric Rings implemented in a CBIR system to explore query results. The approach is centered on keeping user focus on both the query image, and the most similar retrieved images. Experiments conducted so far suggest that the proposed visualization strategies improves system usability.
Managing IFC for civil engineering projects
Authors: Renaud Vanlande, Christophe Cruz, Christophe Nicolle
Venue: CIKM
Year: 2003

The "Industrial Foundation Classes" (IFC) are an ISO norm to define all components of a building in a civil engineering project. IFC files are textual files whose size can reach 100 megabytes. Several IFC files can coexist on the same civil engineering project. Due to their size, their handling and sharing is a complex task. In this paper, we present an approach to automatically identify business objects in the IFC files and simplify their visualization and manipulation on the Internet. We construct an IFC Viewer which transforms the IFC file into a XML IFC tree manipulated through the 3D visualization of the building. The IFC Viewer composed a web-based platform called ACTIVe3D BUILD SERVER. This platform lets geographically dispersed project participants-from architects to electricians-directly use and exchange project documents in a centralized virtual environment during the life cycle of a civil engineering project.
An approach for implicitly detecting information needs
Authors: Ryen White, Joemon M. Jose, Ian Ruthven
Venue: CIKM
Year: 2003

Searchers can have problems devising queries that accurately express their, often dynamic, information needs. In this paper we describe an adaptive approach that uses unobtrusive monitoring of interaction to help alleviate such problems and support searchers in their seeking. The approach we propose implicitly selects terms to better represent information needs, gathers evidence on potential changes in these needs, and uses this evidence to tailor the result presentation accordingly. A user evaluation of an interface implementing our approach, presented in [7], shows it can select terms that approximate current information needs and provide evidence to track changes in these needs.
Tracking changes in user interests with a few relevance judgments
Authors: Dwi H. Widyantoro, Thomas R. Ioerger, John Yen
Venue: CIKM
Year: 2003

Keeping track of changes in user interests from a document stream with a few relevance judgments is not an easy task. To tackle this problem, we propose a novel method that integrates (1) pseudo-relevance feedback mechanism, (2) assumption about the persistence of user interests and (3) incremental method for data clustering. This approach has been empirically evaluated using Reuters-21578 corpus in a setting for information filtering. The experiment results reveal that it significantly improves the performances of existing user-interest-tracking systems without requiring additional, actual relevance judgments.
Finding more useful information faster from web search results
Authors: Yi-fang Brook Wu, Latha Shankar, Xin Chen
Venue: CIKM
Year: 2003

In this paper, we propose a prototype system for automatic generation of concept hierarchies to be used as an overview of search results. The system sends a user's query to five search engines and receives a returned list of relevant web pages. The system then extracts query-oriented concept terms from snippets that come with the returned hits. Concept terms are organized into a concept hierarchy using a co-occurrence-based classification technique. Finally, concepts in returned documents are dynamically highlighted according to terms in the selected concept branch that lead to the chosen document. The user study shows that concept hierarchies do provide easy navigation and browsing of web returned documents. The results also show that users can find a document of interest no matter how low it is ranked in the retrieved list.
Margin-based local regression for adaptive filtering
Authors: Yiming Yang, Bryan Kisiel
Venue: CIKM
Year: 2003

Adaptive information filtering is an open challenge in information retrieval. One of the tough issues is the optimization of decision thresholds over time, based on partial relevance feedback on the system-retrieved documents in chronological order. We developed a new approach, namely margin-based local regression, that automatically adjusts the thresholds based on a sliding window over the truly positive examples for which the system predicted "yes" with respect to a particular class, and a second sliding window over the other documents being processed by the system. Using the means of the scores of the documents in the two windows, we monitor the temporal drifting of the margin that is a function of both the current classification model and the threshold calibration strategy, and that suggests the bounds for the optimal threshold at a given time. Examining this approach together with a Rocchio-style classifier on the TREC 2001 and TREC 2002 benchmark data sets in adaptive filtering, we obtained significant improvements in performance (measured using Fβ=0.5) over the baseline system that did not adapt the threshold over time, and the best result ever reported on the TREC 2002 benchmark corpus for adaptive filtering evaluations. These empirical results suggest that it is important to use both system-accepted and system-rejected documents to optimize thresholds instead of just using system-accepted documents alone, as well as to make the thresholding function temporally sensitive to the shifting centroids of on-topic and off-topic documents.
On the complexity of schema inference from web pages in the presence of nullable data attributes
Authors: Guizhen Yang, I. V. Ramakrishnan, Michael Kifer
Venue: CIKM
Year: 2003

An increasingly large number of Web pages are machine-generated by filling in templates with data stored in backend databases. These templates can be viewed as the implicit schemas of those Web pages. The ability to infer the implicit schema from a collection of Web pages is important for scalable data extraction, since the inferred schema can be used to automatically identify schema attributes that are "encoded" in Web pages.However, the task of inferring a "good" schema is complicated due to the existence of nullable (missing) data attributes. Usually if an attribute contains a null value, then it will be omitted in the generated Web page, giving rise to different variations and permutations of layout structures in Web pages that are generated from the same template.In this paper we investigate the complexity of schema inference from Web pages in the presence of nullable data attributes. We introduce the notion of unambiguity as a quality measure for inferred schemas and prove that the problem of inferring "good" (unambiguous) schemas is NP-complete. Our complexity results imply that ambiguity resolution is one of the root causes of the computational difficulty underlying schema inference from Web pages.
Efficient data access to multi-channel broadcast programs
Authors: Wai Gen Yee, Shamkant B. Navathe
Venue: CIKM
Year: 2003

This paper studies fast access to data that are broadcast on multiple channels. Broadcast is a useful data dissemination technique because of its scalability, but is lacking when it comes to response time. Increasing the number of available broadcast channels is a logical way of increasing throughput. Little work, however, has considered the access structures necessary for making effective use of the additional channels. We propose various indexing schemes for a multi-channel broadcast program. We demonstrate the effectiveness of our techniques in decreasing response time and tuning time via extensive experiments over a wide range of parameters.
Text classification from positive and unlabeled documents
Authors: Hwanjo Yu, ChengXiang Zhai, Jiawei Han
Venue: CIKM
Year: 2003

Most existing studies of text classification assume that the training data are completely labeled. In reality, however, many information retrieval problems can be more accurately described as learning a binary classifier from a set of incompletely labeled examples, where we typically have a small number of labeled positive examples and a very large number of unlabeled examples. In this paper, we study such a problem of performing Text Classification WithOut labeled Negative data TC-WON). In this paper, we explore an efficient extension of the standard Support Vector Machine (SVM) approach, called SVMC (Support Vector Mapping Convergence) [17]for the TC-WON tasks. Our analyses show that when the positive training data is not too under-sampled, SVMC significantly outperforms other methods because SVMC basically exploits the natural "gap" between positive and negative documents in the feature space, which eventually corresponds to improving the generalization performance. In the text domain there are likely to exist many gaps in the feature space because a document is usually mapped to a sparse and high dimensional feature space. However, as the number of positive training data decreases, the boundary of SVMC starts overfitting at some point and end up generating very poor results.This is because when the positive training data is too few, the boundary over-iterates and trespasses the natural gaps between positive and negative class in the feature space and thus ends up fitting tightly around the few positive training data.
Learning cross-document structural relationships using boosting
Authors: Zhu Zhang, Jahna Otterbacher, Dragomir R. Radev
Venue: CIKM
Year: 2003

Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.
Protein structure alignment using geometrical features
Authors: S. Alireza Aghili, Divyakant Agrawal, Amr El Abbadi
Venue: CIKM
Year: 2004

A novel approach for similarity search on protein structure databases is proposed which incorporates the three dimensional coordinates of the main atoms of each amino acid and extracts a geometrical signature along with the direction of the given amino acid. As a result, each protein is presented by a series of feature vectors representing local geometry, shape, direction, and secondary structure assignment of its amino acid constituents. Furthermore, a residue-to-residue distance matrix is calculated and is incorporated into a local alignment dynamic programming algorithm to find the similar portions of two given proteins and finally a sequence alignment step is used as the last filtration step. The optimal superimposition of the detected similar regions is used to assess the quality of the results. The proposed algorithm is fast and accurate and hence could be used for the analysis of large protein structure similarity.
Key problems in integrating structured and unstructured information
Authors: D. Calvin Andrus, David Bernholz, James T. Scoggins, Erik Thomsen
Venue: CIKM
Year: 2004

This forum will host a discussion among the panelists - and with the audience - on key trends in information technology, as well as on industry and government problems in search of technology solutions. The panel will focus on the core problems inherent in integrating structured and unstructured textual information, moving from application-centric to user-centric software, and merging knowledge management with data management.
SWAM: a family of access methods for similarity-search in peer-to-peer data networks
Authors: Farnoush Banaei Kashani, Cyrus Shahabi
Venue: CIKM
Year: 2004

Peer-to-peer Data Networks (PDNs) are large-scale, self-organizing, distributed query processing systems. Familiar examples of PDN are peer-to-peer file-sharing networks, which support exact-match search queries to locate user-requested files. In this paper, we formalize the more general problem of <i>similarity-search</i> in PDNs, and propose a <i>family</i> of distributed access methods, termed <i>Small-World Access Methods (SWAM)</i>, for efficient execution of various similarity-search queries, namely exact-match, range, and k-nearest-neighbor queries. Unlike its predecessors, i.e., LH* and DHTs, SWAM does not control the assignment of data objects to PDN nodes; each node autonomously stores its own data. Besides, SWAM supports all similarity-search queries on multiple attributes. SWAM guarantees that the query object will be found (if it exists in the network) in average time logarithmically proportional to the network size. Moreover, once the query object is found, all the similar objects would be in its proximate network neighborhood and hence enabling efficient range and k-nearest-neighbor queries.
Improving document representations using relevance feedback: the RFA algorithm
Authors: Razvan Stefan Bot, Yi-fang Brook Wu
Venue: CIKM
Year: 2004

In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.
ClusterMap: labeling clusters in large datasets via visualization
Authors: Keke Chen, Ling Liu
Venue: CIKM
Year: 2004

With the rapid increase of data in many areas, clustering on large datasets has become an important problem in data analysis. Since cluster is a highly iterative process, cluster on large datasets prefers short iteration on a relatively small representative set. Thus, a two-phase framework sampling/summarization - iterative cluster analysis is often applied in practice. Since the clustering result only labels the small representative set, there are problems with extending the result to the entire large dataset, which are almost ignored by the traditional clustering research. This extending is often named as labeling process. Labeling irregular shaped clusters, distinguishing outliers and extending cluster boundary are the main problems in this stage. We address these problems and propose a visualization-based approach to dealing with them precisely. This approach partially involves human into the process of defining and refining the structure ClusterMap. Based on this structure, the ClusterMap algorithm scans the large dataset to adapt the boundary extension and generate the cluster labels for the entire dataset. Experimental result shows that ClusterMap can preserve cluster quality considerably with low computational cost, compared to the distance-comparison-based labeling algorithms.
EXPedite: a system for encoded XML processing
Authors: Yi Chen, George A. Mihaila, Susan B. Davidson, Sriram Padmanabhan
Venue: CIKM
Year: 2004

As XML becomes an increasingly popular format for information exchange, the efficient processing of broadcast XML data on a constrained device (for example, a cell phone or a PDA) becomes a critical task. In this paper we present the EXPedite system: a new model of data processing in an information exchange environment, which "migrates" the power of the data-sending server to receivers for efficient processing. It consists of a simple and general encoding scheme for servers, and streaming query processing algorithms on encoded XML stream for data receivers with constrained computing abilities. Experiments show the impressive performance of EXPedite.
An extended logic programming based multi-agent system formalization in mobile environments
Authors: Jianwen Chen, Yan Zhang
Venue: CIKM
Year: 2004

In this paper, we propose an extended logic programming based formalization for a multi-agent system in mobile environments. Such a system consists of a number of agents connected via wire or wireless communication channels. Our formalization is knowledge oriented and has declarative semantics inherited from extended logic programming. This model can be used to study the details of knowledge transaction in mobile environments.
Node ranking in labeled directed graphs
Authors: Krishna Prasad Chitrapura, Srinivas R. Kashyap
Venue: CIKM
Year: 2004

Our work is motivated by the problem of ranking hyper-linked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nodes of this graph with respect to any of the original labels. Ranking documents for a given query in a hyper-linked document set and ranking of authors/articles for a given topic in a citation database are some typical applications of our method. We outline the structural conditions that the graph must satisfy for our ranking to be different from the traditional <i>PageRank</i>.
Computing consistent query answers using conflict hypergraphs
Authors: Jan Chomicki, Jerzy Marcinkowski, Slawomir Staworko
Venue: CIKM
Year: 2004

A consistent query answer in a possibly inconsistent database is an answer which is true in every (minimal) repair of the database. We present here a practical framework for computing consistent query answers for large, possibly inconsistent relational databases. We consider relational algebra queries without projection, and denial constraints. Because our framework handles union queries, we can effectively (and efficiently) extract indefinite disjunctive information from an inconsistent database. We describe a number of novel optimization techniques applicable in this context and summarize experimental results that validate our approach.
On lossy time decompositions of time stamped documents
Authors: Parvathi Chundi, Daniel J. Rosenkrantz
Venue: CIKM
Year: 2004

Constructing time decompositions of time stamped documents is an important first step in extracting temporal information from a document set. Efficient algorithms are described for computing optimal lossy decompositions for a given document set, where the loss of information is constrained to be within a specified bound. A novel and efficient algorithm is proposed for computing information loss values required to construct optimal lossy decompositions. Experimental results are reported comparing optimal lossy decompositions and equal length decompositions in terms of a number of parameters such as information loss. In particular, our results show that optimal lossy decompositions outperform equal length decompositions by preserving more of the information content of the underlying document set. The results also demonstrate that permitting even small amounts of variability in the length of the subintervals of a decomposition results in capturing more of the temporal information content of a document set when compared to equal length decompositions. This paper builds upon our earlier work on time decompositions where the problem of computing optimal lossy decomposition of the time period associated with a document set was first formulated.
Mining gene expression datasets using density-based clustering
Authors: Seokkyung Chung, Jongeun Jun, Dennis McLeod
Venue: CIKM
Year: 2004

Given the recent advancement of microarray technologies, we present a density-based clustering approach for the purpose of co-expressed gene clusters identi cation. Underlying hypothesis is that a set of co-expressed gene clusters can be used to unreveal a common biological function. By addressing the strengths and limitations of previous densitybased clustering approaches, we present anovel clustering algorithm that utilizes a neighborhood de ned by k-nearest neighbors. Experimental results indicate that the proposed method identi es biologically meaningful and co-expressed gene clusters.
Intelligent agent for automated manufacturing rule generation
Authors: Alan L. Clark, Dimitar P. Filev
Venue: CIKM
Year: 2004

Article Share on Intelligent agent for automated manufacturing rule generation Authors: Alan Clark Ford Motor Company Ford Motor CompanyView Profile , Dimitar Filev Ford Motor Company Ford Motor CompanyView Profile Authors Info & Claims CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge managementNovember 2004Pages 495–500https://doi.org/10.1145/1031171.1031266Published:13 November 2004Publication History 3citation491DownloadsMetricsTotal Citations3Total Downloads491Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access