OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating contents-link coupled web page clustering for web search results
Authors: Yitong Wang, Masaru Kitsuregawa
Venue: CIKM
Year: 2002

Clustering is currently one of the most crucial techniques for dealing (e.g. resources locating, information interpreting) with massive amount of heterogeneous information on the web. Unlike clustering in other fields, web page clustering separates unrelated pages and clusters related pages (to a specific topic) into semantically meaningful groups, which is useful for discrimination, summarization, organization and navigation of unstructured web pages. We have proposed a contents-link coupled clustering algorithm that clusters web pages by combining contents and link analysis. In this paper, we particularly study the effects of out-links (from the web pages), in-links (to the web page) and terms on the final clustering results as well as how to effectively combine these three parts to improve the quality of clustering results. We apply it to cluster web search results. Preliminary experiments and evaluations are conducted on various topics. As the experimental results show, the proposed clustering algorithm is effective and promising.
Strategies for minimising errors in hierarchical web categorisation
Authors: Wahyu Wibowo, Hugh E. Williams
Venue: CIKM
Year: 2002

On the Web, browsing and searching categories is a popular method of finding documents. Two well-known category-based search systems are the Yahoo!~and DMOZ hierarchies, which are maintained by experts who assign documents to categories. However, manual categorisation by experts is costly, subjective, and not scalable with the increasing volumes of data that must be processed. Several methods have been investigated for effective automatic text categorisation. These include selection of categorisation methods, selection of pre-categorised training samples, use of hierachies, and selection of document fragments or features. In this paper, we further investigate categorisation into Web hierarchies and the role of hierarchical information in improving categorisation effectiveness. We introduce new strategies to reduce errors in hierarchical categorisation. In particular, we propose novel techniques that shift the assignment into higher level categories when lower level assignment is uncertain. Our results show that absolute error rates can be reduced by over 2%.
Efficient query monitoring using adaptive multiple key hashing
Authors: Kun-Lung Wu, Philip S. Yu
Venue: CIKM
Year: 2002

Monitoring continual queries or subscriptions is to determine the subset of all queries or subscriptions whose predicates match a given event. Predicates contain not only equality but also non-equality clauses. Event matching is usually accomplished by first identifying a "small" candidate set of subscriptions for an event and then determining the matched subscriptions from the candidate set. Prior work has focused on using equality clauses to identify the candidate set. However, we found that completely ignoring non-equality clauses can result in a much larger candidate set. In this paper, we present and evaluate an adaptive multiple key hashing (AMKH) method to judiciously include an effective subset of non-equality clauses in candidate set identification. Each subscription is mapped to a data point in a multidimensional space based on its predicate clauses. AMKH is then used to maintain subscriptions and perform event matching. AMKH further provides a controlling mechanism to limit the hash range of a non-equality clause, hence reducing the size of the candidate set. Simulations are conducted to study the performance of AMKH. The results show that (1) a small number of non-equality clauses can be effectively included by AMKH and (2) the attributes whose overall non-equality predicate clauses are most selective should be chosen for inclusion by AMKH.
An agent-based approach to knowledge management
Authors: Bin Yu, Munindar P. Singh
Venue: CIKM
Year: 2002

Traditional approaches to knowledge management are essentially limited to document management. However, much knowledge in organizations or communities resides in an informal social network and may be accessed only by asking the right people. This paper describes MARS, a multiagent referral system for knowledge management. MARS assigns a software agent to each user. The agents facilitate their users' interactions and help manage their personal social networks. Moreover, the agents cooperate with one another by giving and taking referrals to help their users find the right parties to contact for a specific knowledge need.
Removing redundancy and inconsistency in memory-based collaborative filtering
Authors: Kai Yu, Xiaowei Xu, Anton Schwaighofer, Volker Tresp, Hans-Peter Kriegel
Venue: CIKM
Year: 2002

The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (CF) algorithm. Our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the CF algorithm. In our approach, we consider instance selection as the problem of selecting informative data that increase the We begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. We evaluate the empirical performance of our approach PF on two real-world data sets and attain very promisingpositive experimental results. The dData size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.
Knowledge and information management: Is it possible to do interesting and important research, get funded, be useful and appreciated?
Authors: Maria Zemankova
Venue: CIKM
Year: 2002

The survey of the CIKM Call for Papers for the period 1998 - 2002 demonstrates that the CIKM organizers very accurately "identify challenging problems facing the development of future knowledge and information systems [in] applied and theoretical research" [1998] and also play an important role fostering "bridging traditionally separated areas such as databases and information retrieval, or those that apply techniques from one area to another" [2001, 2002]. The presented CIKM papers also indicate that researchers work on interesting problems. This talk will discuss some additional research topics for future consideration by the CIKM community.In most cases, to achieve important results, research needs to be well supported. If you are not getting the funding you need, this talk may provide some pointers where you can look for finding. If you are well supported, I will try to convince you that you can be instrumental in improving the funding scenario for everybody, by mentoring the junior members of the CIKM community, by forming collaborative (international, interdisciplinary) teams and by letting the funders know what you find conducive to your research and what you consider a hindrance. Regardless if you are well funded or not, it is most helpful if you are active in identifying new research directions and also assist in evaluating the priorities.The most frequent problem for inadequate funding is lack of funds. However, researchers can help! This can be achieved in many different ways: thinking about long-term applications of fundamental research to societal needs; working with communities that can directly benefit from research; sharing the research results not only with the research colleagues, but also wider constituencies - at their appropriate levels; and informing your funders about your spectacular achievements, i.e., providing good reasons for increasing the research funding.CIKM strives to bring together research communities that traditionally do not work together. Providing a forum for interdisciplinary research is laudable and very important, as very often interdisciplinary or international research is not "appreciated". This talk will discuss how we could gradually change the discipline- and country-based "appraisal" cultures. We will also attempt to answer the question: "What is the ultimate appreciation?" (Nobel Prize? ...successful .com?... ???).
Trajectory queries and octagons in moving object databases
Authors: Hongjun Zhu, Jianwen Su, Oscar H. Ibarra
Venue: CIKM
Year: 2002

An important class of queries in moving object databases involves trajectories. We propose to divide trajectory predicates into topological and non-topological parts; extend the 9 intersection model of Egenhofer-Franzosa to a 3-step evaluation strategy for trajectory queries: a filter step, a refinement step, and a tracing step.The filter and refinement steps are similar to region searches. As in spatial databases, approximations of trajectories are typically used in evaluating trajectory queries. In earlier studies, minimum bounding boxes (mbrs) are used to approximate trajectory segments which allow index structures to be built, e.g., TB-trees and R*-trees. The use of mbrs hinders the efficiency since mbrs are very coarse approximations especially for trajectory segments. To overcome this problem, we propose a new type of approximations, "minimum bounding octagon prism" mbop. We extend R*-tree to a new index structure "Octagon-Prism tree" (OP-tree) for mbops of trajectory segments. We conducted experiments to evaluate efficiency of OP-trees in performing region searches and trajectory queries. The results show that OP-trees improve region searches significantly over synthetic trajectory data sets to TB-trees and R*-trees and can significantly reduce the evaluation cost of trajectory queries compared to TB-trees.
Hierarchical graph indexing
Authors: James Abello, Yannis Kotidis
Venue: CIKM
Year: 2003

Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.
Building XML statistics for the hidden web
Authors: Ashraf Aboulnaga, Jeffrey F. Naughton
Venue: CIKM
Year: 2003

There is currently a lot of interest in developing Internet query processors that can pose elaborate queries on XML data on the Web. Such query processors can query data sources that have static XML files, but they should also be able to query &quot;hidden Web&quot; data sources that export an XML view of data stored in a database. To optimize queries that involve these hidden Web data sources, we need to have XML statistics that can be used to estimate the selectivity of queries posed to these sources. Since we can only access the data at a hidden Web data source by issuing queries, we need to develop on-line XML statistics that are built by observing queries to a hidden Web data source and their result sizes.
Bootstrapping for hierarchical document classification
Authors: Giordano Adami, Paolo Avesani, Diego Sona
Venue: CIKM
Year: 2003

Managing the hierarchical organization of data is starting to play a key role in the knowledge management community due to the great amount of human resources needed to create and maintain these organized repositories of information. Machine learning community has in part addressed this problem by developing hierarchical supervised classifiers that help maintainers to categorize new resources within given hierarchies. Although such learning models succeed in exploiting relational knowledge, they are highly demanding in terms of labeled examples, because the number of categories is related to the dimension of the corresponding hierarchy. Hence, the creation of new directories or the modification of existing ones require strong investments.This paper proposes a semi-automatic process (interleaved with human suggestions) whose aim is to minimize (simplify) the work required to the administrators when creating, modifying, and maintaining directories. Within this process, bootstrapping a taxonomy with examples represents a critical factor for the effective exploitation of any supervised learning model. For this reason we propose a method for the bootstrapping process that makes a first hypothesis of categorization for a set of unlabeled documents, with respect to a given empty hierarchy of concepts. Based on a revision of Self-Organizing Maps, namely TaxSOM, the proposed model performs an unsupervised classification, exploiting the a-priori knowledge encoded in a taxonomy structure both at the terminological and topological level. The ultimate goal of TaxSOM is to create the premise for successfully training a supervised classifier.
Lessons from the implementation of an adaptive parts acquisition ePortal
Authors: Rafael Alonso, Jeffrey A. Bloom, Hua Li
Venue: CIKM
Year: 2003

In recent work we have developed a novel approach to the design and implementation of an online portal (ePortal) to help application engineers find replacements for electronic parts that have become obsolete (and hence will no longer be produced). Our approach makes use of machine learning techniques to improve the performance of a database search function. However, the purpose of this note is not to describe in detail the application nor our technical solution - that has been done elsewhere (see [1,2]). Rather, it is our intention to present some of the lessons learned from our project. Below, we provide a brief introduction to the technical approach, concentrate on several of the most salient lessons, and conclude with a description of the current state of the project.
Multi-resolution disambiguation of term occurrences
Authors: Einat Amitay, Rani Nelken, Wayne Niblack, Ron Sivan, Aya Soffer
Venue: CIKM
Year: 2003

We describe a system for extracting mentions of terms such as company and product names, in a large and noisy corpus of documents, such as the World Wide Web. Since natural language terms are highly ambiguous, a significant challenge in this task is disambiguating which occurrences of each term are truly related to the right meaning, and which are not. We describe our approach for disambiguation, and show that it achieves very high accuracy with only limited training. This serves as a necessary first step for applications that strive to do analytics on term mentions.
A unified model for metasearch, pooling, and system evaluation
Authors: Javed A. Aslam, Virgiliu Pavlu, Robert Savell
Venue: CIKM
Year: 2003

We present a unified model which, given the ranked lists of documents returned by multiple retrieval systems in response to a given query, simultaneously solves the problems of (1) fusing the ranked lists of documents in order to obtain a high-quality combined list (metasearch); (2) generating document collections likely to contain large fractions of relevant documents (pooling); and (3) accurately evaluating the underlying retrieval systems with small numbers of relevance judgments (efficient system assessment). Our approach is based on the Hedge algorithm for on-line learning. In effect, our proposed system "learns" which documents are likely to be relevant from a sequence of on-line relevance judgments. In experiments using TREC data, our methodology is shown to outperform standard methods for metasearch, pooling, and system evaluation, often remarkably so.
Addressing the lack of direct translation resources for cross-language retrieval
Authors: Lisa Ballesteros, Mark Sanderson
Venue: CIKM
Year: 2003

Most cross language information retrieval research concentrates on language pairs for which direct, rich, and often multiple translation resources already exist. However, for most language pairs, translation via an intermediate language is necessary. Two distinct methods for dealing with the additional ambiguity introduced by the extra translation step have been proposed and individually, shown to improve retrieval effectiveness. Two previous works indicated that in combination, the methods were ineffective. This paper provides strong empirical evidence that the methods can be combined to produce consistent and often significant improvements in retrieval effectiveness. The improvement is shown across a number of different intermediate languages and test collections.
Approximate searches: k-neighbors + precision
Authors: Sid-Ahmed Berrani, Laurent Amsaleg, Patrick Gros
Venue: CIKM
Year: 2003

It is known that all multi-dimensional index structures fail to accelerate content-based similarity searches when the feature vectors describing images are high-dimensional. It is possible to circumvent this problem by relying on approximate search-schemes trading-off result quality for reduced query execution time. Most approximate schemes, however, provide none or only complex control on the precision of the searches, especially when retrieving the k nearest neighbors (NNs) of query points.In contrast, this paper describes an approximate search scheme for high-dimensional databases where the precision of the search can be probabilistically controlled when retrieving the k NNs of query points. It allows a fine and intuitive control over this precision by setting at run time the maximum probability for a vector that would be in the exact answer set to be missed in the approximate set of answers eventually returned. This paper also presents a performance study of the implementation using real datasets showing its reliability and efficiency. It shows, for example, that our method is 6.72 times faster than the sequential scan when it handles more than 5 106 24-dimensional vectors, even when the probability of missing one of the true nearest neighbors is below 0.01.
HyperThesis: the gRNA spell on the curse of bioinformatics applications integration
Authors: Sourav S. Bhowmick, Vivek Vedagiri, Amey V. Laud
Venue: CIKM
Year: 2003

In this paper, we describe a graphical workflow management system called HyperThesis to address the challenges of integrating bioinformatics applications. HyperThesis is an integral component of the Genomics Research Network Architecture (gRNA). The gRNA was designed and developed to address the challenges of developing new bioinformatics applications. Specifically, HyperThesis makes constructing workflows (pipelines of execution of applications) in the gRNA fast and intuitive for biologists and bio-programmers alike. It provides a large repository of interconnectable, parameterized workflow components for processing and relating diverse biological data and software programs. It also enables us to add new workflow components as new algorithms develop in ones area of interest. HyperThesis has been fully implemented using Java.
The virGIS WFS-based spatial mediation system
Authors: Omar Boucelma, Jean-Yves Garinet, Zoé Lacroix
Venue: CIKM
Year: 2003

The proliferation of spatial data on the Internet is beginning to allow a much wider access to data currently available in various Geographic Information Systems (GIS). In order to provide effective spatial database integration, we need to provide flexible and powerful GIS data integration solutions. Indeed, GIS are highly heterogeneous: not only they differ by their data representations, but they also offer radically different query languages. A GIS mediation approach should provide an integrated view of the data supplied by all sources, and a geographical query language to access and manipulate integrated data.
iTopN: incremental extraction of the N most visible objects
Authors: Linas Bukauskas, Leo Mark, Edward Omiecinski, Michael H. Böhlen
Venue: CIKM
Year: 2003

The visual exploration of large databases calls for a tight coupling of database and visualization systems. Current visualization systems typically fetch all the data and organize it in a scene tree, which is then used to render the visible data. For immersive data explorations, where an observer navigates in a potentially huge data space and explores selected data regions this approach is inadequate. A scalable approach is to make the database system observer-aware and exchange the data that is visible and most relevant to the observer.In this paper we present iTopN an incremental algorithm for extracting the most visible objects relative to the current position of the observer. We implement iTopN and compare it to an improved version of the R-tree that extends LRU with the caching of the top levels of the R-tree (LW-LRU). Our experiments show that iTopN is orders of magnitude faster than LW-LRU given the same amount of memory. Our experiments also show that for LW-LRU to perform as fast as iTopN it needs three times as much memory.
Combining link-based and content-based methods for web document classification
Authors: Pável Calado, Marco Cristo, Edleno Silva de Moura, Nivio Ziviani, Berthier A. Ribeiro-Neto, Marcos André Gonçalves
Venue: CIKM
Year: 2003

This paper studies how link information can be used to improve classification results for Web collections. We evaluate four different measures of subject similarity, derived from the Web link structure, and determine how accurate they are in predicting document categories. Using a Bayesian network model, we combine these measures with the results obtained by traditional content-based classifiers. Experiments on a Web directory show that best results are achieved when links from pages outside the directory are considered. Link information alone is able to obtain gains of up to 46 points in F1, when compared to a traditional content-based classifier. The combination with content-based methods can further improve the results, but too much noise may be introduced, since the text of Web pages is a much less reliable source of information. This work provides an important insight on which measures derived from links are more appropriate to compare Web documents and how these measures can be combined with content-based algorithms to improve the effectiveness of Web classification.
Misuse detection for information retrieval systems
Authors: Rebecca Cathey, Ling Ma, Nazli Goharian, David A. Grossman
Venue: CIKM
Year: 2003

We present a novel approach to detect misuse within an information retrieval system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. Our approach is based on building and maintaining a profile of the behavior of the system user through tracking, or monitoring of user activity within the information retrieval system. Any new activity of the user is compared to the user profile to detect a potential misuse for the authorized user. We propose four different methods to detect misuse in information retrieval systems. Our experimental results on $2$ GB collection favorably demonstrate the validity of our approach.