OpenRaiser/Intern-Atlas huggingface dataset:
Yahoo! As an Ontology: Using Yahoo! Categories to Describe Documents
Authors: Yannis Labrou, Timothy W. Finin
Venue: CIKM
Year: 1999

We suggest that one (or a collection) of names of Yahoo! (or any other WWW indexer's) categories can be used to describe the content of a document. Such categories offer a standardized and universal way for referring to or describing the nature of real world objects, activities, documents and so on, and may be used (we suggest) to semantically characterize the content of documents. WWW indices, like Yahoo! provide a huge hierarchy of categories (topics) that touch every aspect of human endeavors. Such topics can be used as descriptors, similarly to the way librarians use for example, the Library of Congress cataloging system to annotate and categorize books.
Adaptive Information Filtering: Detecting Changes in Text Streams
Authors: Carsten Lanquillon, Ingrid Renz
Venue: CIKM
Year: 1999

The task of information filtering is to classify documents from a stream as either relevant or non-relevant according to a particular user interest with the objective to reduce information load. When using an information filter in an environment that is changing with time, methods for adapting the filter should be considered in order to retain classification accuracy. We favor a methodology that attempts to detect changes and adapts the information filter only if inevitable in order to minimize the amount of user feedback for providing new training data. Yet, detecting changes may require costly user feedback as well. This paper describes two methods for detecting changes without user feedback. The first method is based on evaluating an expected error rate, while the second one observes the fraction of classification decisions made with a confidence below a given threshold. Further, a heuristics for automatically determining this threshold is suggested and the performance of this approach is experimentally explored as a function of the threshold parameter. Some empirical results show that both methods work well in a simulated change scenario with real world data.
Indexing and Retrieval of Scientific Literature
Authors: Steve Lawrence, Kurt Bollacker, C. Lee Giles
Venue: CIKM
Year: 1999

The web has greatly improved access to scientific literature. However, scientific articles on the web are largely disorganized, with research articles being spread across archive sites, institution sites, journal sites, and researcher homepages. No index covers all of the available literature, and the major web search engines typically do not index the content of Postscript/PDF documents at all. This paper discusses the creation of digital libraries of scientific literature on the web, including the efficient location of articles, full-text indexing of the articles, autonomous citation indexing, information extraction, display of query-sensitive summaries and citation context, hubs and authorities computation, similar document detection, user profiling, distributed error correction, graph analysis, and detection of overlapping documents. The software for the system is available at no cost for non-commercial use.
A Self-Organized File Cabinet
Authors: Dawn J. Lawrie, Daniela Rus
Venue: CIKM
Year: 1999

The self-organizing file cabinet is an information retrieval system associated with a user's physical file cabinet. It enhances a physical file cabinet with electronic information about the papers in it. It can remember, organize, update, and help the user find documents contained in the physical file cabinet. The system consists of a module for extracting electronic information about the papers stored in the file cabinet, a module for representing and storing this information in multiple views, and a module that allows a user to interact with this information. The focus of this paper is on the design and evaluation of the self-organized file cabinet.
SemQL: A Semantic Query Language for Multidatabase Systems
Authors: Jeong-Oog Lee, Doo-Kwon Baik
Venue: CIKM
Year: 1999

An essential prerequisite to achieving interoperability in multidatabase systems is to be able to identify semantically equivalent or related data items in component databases. Another problem in multidatabase systems is allowing users to handle information from different databases that refer to the same realworld entity. In this paper, we provide semantic networks so that multidatabase systems can detect and resolve semantic heterogeneities among component databases. And we provide a semantic query language, SemQL, to capture the concepts about what users want. It enables users to issue queries to a large number of autonomous databases without prior knowledge of their schemas.
Semantic Caching via Query Matching for Web Sources
Authors: Dongwon Lee, Wesley W. Chu
Venue: CIKM
Year: 1999

A semantic caching scheme suitable for wrappers wrapping web sources is presented. Since the web sources have typically weaker querying capabilities than conventional databases, existing semantic caching schemes cannot be applied directly. A seamlessly integrated query translation and capability mapping between the wrappers and web sources in semantic caching is described. In addition, an analysis on the match types between the user's input query and cached queries is presented. Semantic knowledge acquired from the data can be used to avoid unnecessary access to the web sources by transforming the cache miss to the cache hit. A polynomial time algorithm based on the proposed query matching technique is presented to find the best matched query in the cache. Experimental results reveal the effectiveness of the proposed semantic caching scheme.
Information Integration with Attributio Support for Corporate Profiles
Authors: Thomas Lee, Melanie Chams, Robert Nado, Michael D. Siegel, Stuart E. Madnick
Venue: CIKM
Year: 1999

The proliferation of electronically available data within large organizations as well as publicly available data (e.g. over the World Wide Web) poses challenges for users who wish to efficiently interact with and integrate multiple heterogeneous sources. This paper presents CI3, a corporate information integrator, which applies XML as a tool to facilitate data mediation and integration amongst heterogeneous sources in the context of financial analysts creating corporate profiles. Sources include Lotus Notes, relational databases, and the World Wide Web. CI3 applies a unified XML data model to automate integration. By preserving metadata about the source of each datum in the integrated result set, CI3 supports source attribution. Users may trace the attribution metadata from the result back to the underlying sources and leverage their expertise in interpreting the data and, if necessary, use their judgment in assessing the authenticity and veracity of results. We present a functional overview of CI3, its system architecture including the XML data model, and the integration procedures. We conclude by reflecting on lessons learned.
Text Classification Using ESC-based Stochastic Decision Lists
Authors: Hang Li, Kenji Yamanishi
Venue: CIKM
Year: 1999

We propose a new method of text classification using stochastic decision lists. A stochastic decision list is an ordered sequence of IF-THEN rules, and our method can be viewed as a rule-based method for text classification having advantages of readability and refinability of acquired knowledge. Our method is unique in that decision lists are automatically constructed on the basis of the principle of minimizing Extended Stochastic Complexity (ESC), and with it we are able to construct decision lists that have fewer errors in classification. The accuracy of classification achieved with our method appears better than or comparable to those of existing rule-based methods.
Automatically Extracting Structure and Data from Business Reports
Authors: Stephen W. Liddle, Douglas M. Campbell, Chad Crawford
Venue: CIKM
Year: 1999

A considerable amount of clean semistructured data is internally available to companies in the form of business reports. However, business reports are untapped for data mining, data warehousing, and querying because they are not in relational form. Business reports have a regular structure that can be reconstructed. We present algorithms that automatically infer the regular structure underlying business reports and automatically generate wrappers to extract relational data.
Page Access Scheduling in Join Processing
Authors: Andrew Lim, Jennifer Lai-Pheng Kwan, Wee-Chong Oon
Venue: CIKM
Year: 1999

The join relational operation is one of the most expensive among database operations. In this study, we consider the problem of scheduling page accesses in join processing. This raises two interesting problems: 1) determining a page access sequence that uses the minimum number of buffer pages without any page reaccesses, and 2) determining a page access sequence that minimizes the number of page reaccesses for a given buffer size. We use a graph model to represent the pages from the relations that contain tuples to be joined, and present new heuristics for the two problems based on the sort-merge join and the simple TID algorithm. Our experimental results show that the new heuristics perform well.
ZBroker: A Query Routing Broker for Z39.50 Databases
Authors: Yong Lin, Jian Xu, Ee-Peng Lim, Wee Keong Ng
Venue: CIKM
Year: 1999

A query routing broker is a software agent that determines from a large set of accessing information sources the ones most relevant to a user's information need. As the number of information sources on the Internet increases dramatically, future users will have to rely on query routing brokers to decide a small number of information sources to query without incurring too much query processing overheads. In this paper, we describe a query routing broker known as ZBroker developed for bibliographic database servers that support the Z39.50 protocol. ZBroker samples the content of each bibliographic database by using training queries and their results, and summarizes the bibliographic database content into a knowledge base. We present the design and implementation of ZBroker and describe its Web-based user interface.
Task-Oriented World Wide Web Retrieval by Document Type Classification
Authors: Katsushi Matsuda, Toshikazu Fukushima
Venue: CIKM
Year: 1999

This paper proposes a novel approach to accurately searching Web pages for relevant information in problem solving by specifying a Web document category instead of the user's task. Accessing information from World Wide Web pages as an approach to problem solving has become commonplace. However, such a search is difficult with current search services, since these services only provide keyword-based search methods that are equivalent to narrowing down the target references according to domains. However, problem solving usually involves both a domain and a task. Accordingly, our approach is based on problem solving tasks. To specify a user's problem solving task, we introduce the concept of document types that directly relate to the problem solving tasks; with this approach, users can easily designate problem solving tasks. We implemented PageTypeSearch system based on our approach. Classifier of PageTypeSearch classifies Web pages into the document types by comparing their pages with typical structural characteristics of the types. We compare PageTypeSearch using the document typeindices with a conventional keyword-based search system in experiments. The average precision of the document type-based search is 88.9%, while the average precision of the keyword-based search is 31.2%. Moreover, the number of irrelevant references gathered by our system is about one-thirteenth that of traditional keyword-based search systems. Our approach has practical advantages for problem solving by introducing the viewpoint of tasks to achieve higher performance.
A Unified Environment for Fusion of Information Retrieval Approaches
Authors: M. Catherine McCabe, Abdur Chowdhury, David A. Grossman, Ophir Frieder
Venue: CIKM
Year: 1999

Prior work has shown that combining results of various retrieval approaches and query representations can improve search effectiveness. Today, many meta-search engines exist which combine the results of various search engines in the hopes of improving overall effectiveness. However, the combination of results from different search engines masks variations in parsers, and other indexing techniques (stemming, stop words, etc.) This makes it difficult to assess the utility of the fusion technique. We have implemented the two most prevalent retrieval strategies: probabilistic and vector space using the same parser and the same relational retrieval engine. First, we identified a model that enables the fusion of an arbitrary number of sources. Next, we tested various linear combinations of these two methods as well as various thresholds for identifying retrieved documents. Our results show some improvement of effectiveness, but they also provide us for a baseline from which we can continue with other retrieval strategies and test the effect of fusing these strategies.
A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse
Authors: Amin Y. Noaman, Ken Barker
Venue: CIKM
Year: 1999

Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a “horizontal” fragmentation algorithm for the fact relation.
Incremental and Interactive Sequence Mining
Authors: Srinivasan Parthasarathy, Mohammed Javeed Zaki, Mitsunori Ogihara, Sandhya Dwarkadas
Venue: CIKM
Year: 1999

The discovery of frequent sequences in temporal databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new database. In this paper, we propose novel techniques for maintaining sequences in the presence of a) database updates, and b) user interaction (e.g. modifying mining parameters). This is a very challenging task, since such updates can invalidate existing sequences or introduce new ones. In both the above scenarios, we avoid re-executing the algorithm on the entire dataset, thereby reducing execution time. Experimental results confirm that our approach results in execution time improvements of up to several orders of magnitude in practice.
Ready for Prime Time: Pre-Generation of Web Pages in TIScover
Authors: Birgit Pröll, Heinrich Starck, Werner Retschitzegger, Harald Sighart
Venue: CIKM
Year: 1999

In large data- and access-intensive web sites, efficient and reliable access is hard to achieve. This situation gets even worse for web sites providing precise structured query facilities and requiring topicality of the presented information even in face of a highly dynamic content. The achievement of these partly conflicting goals is strongly influenced by the approach chosen for page generation, ranging from composing a web page upon a user's request to its generation in advance. The official Austrian web-based tourism information and booking system TIScover tries to reconcile these goals by employing a hybrid approach of page generation. In TIScover, web pages are not only generated on request in order to support precise structured queries on the content managed by a database system. Rather, the whole web site is also pre-generated out of the extremely dynamic content and synchronized with the database on the basis of metadata. Thus, topicality of information is guaranteed, while ensuring efficient and reliable access. This paper discusses the hybrid approach as realized in TIScover, focussing in particular on the concepts used for pre-generation.1
Graph-Based Object-Oriented Approach for Structural and Behavioral Representation of Multimedia Data
Authors: Ivan Radev, Niki Pissinou, Kia Makki, E. K. Park
Venue: CIKM
Year: 1999

The management of multimedia information poses special requirements for multimedia information systems. Both representation and retrieval of the complex and multifaceted multimedia data are not easily handled with the flat relational model and require new data models. In the last several years, object-oriented and graph-based data models are actively pursued approaches for handling the multimedia information. In this paper the characteristics of the novel graph-based object-oriented data model are presented. This model represents the structural and behavioral aspects of data that form multimedia information systems. It also provides for handling the continuously changing user requirements and the complexity of the schema and data representation in multimedia information systems using the schema versioning approach and perspective version abstraction.
Towards Data Warehouse Design
Authors: Franck Ravat, Olivier Teste, Gilles Zurfluh
Venue: CIKM
Year: 1999

This paper focuses on data warehouse modelling. The conceptual model we defined, is based on object concepts extended with specific concepts like generic classes, temporal classes and archive classes. The temporal classes are used to store the detailed evolutions and the archive classes store the summarised data evolutions. We also provide a flexible concept allowing the administrator to define historised parts and non-historised parts into the warehouse schema. Moreover, we introduce constraints which configure the data warehouse behaviour and these various parts. To validate our propositions, we describe a prototype dedicated to the data warehouse design.
Extracting Semi-Structured Data Through Examples
Authors: Berthier A. Ribeiro-Neto, Alberto H. F. Laender, Altigran Soares da Silva
Venue: CIKM
Year: 1999

In this paper, we describe an innovative approach to extracting semi-structured data from Web sources. The idea is to collect a couple of example objects from the user and to use this information to extract new objects from new pages or texts. To perform the extraction of new objects, we introduce a bottom-up extration strategy and, through experimentation, demonstrate that it works quite effectively with distinct Web sources, even if only a few examples are provided by the user.
Quality of Service Transferred to Information Retrieval: The Adaptive Information Retrieval System
Authors: Claudia Rolker, Ralf Kramer
Venue: CIKM
Year: 1999

Users often quit an information retrieval system (IR system) very frustrated, because they cannot find the information matching their information needs. We have identified the following two main reasons: too high expectations and wrong use of the system. Our approach which addresses both issues is based on the transfer of the concept of Quality of Service to IR systems: The user first negotiates the retrieval success rates with the IR system, so he knows what to expect from the system in advance. Second, by dynamic adaptation to the retrieval context, the IR system tries to improve the user's queries and thereby tries to exploit the underlying information source as best as possible.
Self Maintenance of Multiple Views in Data Warehousing
Authors: Sunil Samtani, Vijay Kumar, Mukesh K. Mohania
Venue: CIKM
Year: 1999

Materialized views (MV) at the data warehouse (DW) can be kept up to date in response to changes in data sources without accessing data sources for additional information.
A Practitioners' View of Techniques Used in Data Warehousing for Sifting Through Data to Provide Information
Authors: James T. Scoggins
Venue: CIKM
Year: 1999

Over the past 10 years data warehousing evolved from providing 'nice to know' data to 'need to know' information. The decision support systems providing summarized reports to executives have advanced to integrated information factories providing vital information to the desktops of knowledge workers. Data warehousing has benefited through the use advanced techniques of sifting through data to produce information. This discussion will cover data mining techniques used in specific business cases as well as attempt to describe problems that still exist (and could be researched) in the business intelligence arena.
Discovering Quasi-Equivalence Relationships from Database Systems
Authors: Mei-Ling Shyu, Shu-Ching Chen, Rangasami L. Kashyap
Venue: CIKM
Year: 1999

Association rule mining has recently attracted strong attention and proven to be a highly successful technique for extracting useful information from very large databases. In this paper, we explore a generalized affinity-based association mining which discovers quasi-equivalent media objects in a distributed information-providing environment consisting of a network of heterogeneous databases which could be relational databases, hierarchical databases, object-oriented databases, multimedia databases, etc. Online databases, consisting of millions of media objects, have been used in business management, government administration, scientific and engineering data management, and many other applications owing to the recent advances in high-speed communication networks and large-capacity storage devices. Because of the navigational characteristic, queries in such an information-providing environment tend to traverse equivalent media objects residing in different databases for the related data records. As the number of databases increases, query processing efficiency depends heavily on the capability to discover the equivalence relationships of the media objects from the network of databases. Theoretical terms along with an empirical study of real databases are presented.
An Adaptive View Element Framework for Multi-Dimensional Data Management
Authors: John R. Smith, Chung-Sheng Li
Venue: CIKM
Year: 1999

We present an adaptive wavelet view element framework for managing different types of multi-dimensional data in storage and retrieval applications. We consider the problems of multi-dimensional data compression, multi-resolution subregion access, selective materialization, progressive retrieval and similarity searching. The framework uses wavelets to partition the multi-dimensional data into view elements that form the building blocks for synthesizing views of the data. The view elements are organized and managed using different view element graphs. The graphs are used to guide cost-based view element selection algorithms for optimizing compression, access, retrieval and search performance.
A General Language Model for Information Retrieval
Authors: Fei Song, W. Bruce Croft
Venue: CIKM
Year: 1999

Statistical language modeling has been successfully used for speech recognition, part-of-speech tagging, and syntactic parsing. Recently, it has also been applied to information retrieval. According to this new paradigm, each document is viewed as a language sample, and a query as a generation process. The retrieved documents are ranked based on the probabilities of producing a query from the corresponding language models of these documents. In this paper, we will present a new language model for information retrieval, which is based on a range of data smoothing techniques, including the Good-Turning estimate, curve-fitting functions, and model combinations. Our model is conceptually simple and intuitive, and can be easily extended to incorporate probabilities of phrases such as word pairs and word triples. The experiments with the Wall Street Journal and TREC4 data sets showed that the performance of our model is comparable to that of INQUERY and better than that of another language model for information retrieval. In particular, word pairs are shown to be useful in improving the retrieval performance.