OpenRaiser/Intern-Atlas huggingface dataset:
An evaluation of the incorporation of a semantic network into a multidimensional retrieval engine
Authors: Jinho Lee, David A. Grossman, Ratko Orlandic
Venue: CIKM
Year: 2003

This paper describes a new method for incorporating a hierarchical category dimension into an Information Retrieval framework. The approach is to use the synonym sets and the hyponym ("is-a") relations defined within Wordnet in order to derive a conceptual hierarchical category dimension. The hierarchical nature of a category dimension not only provides an overview of a set of documents but also facilitates the effectiveness and the efficiency of searching documents. An evaluation is performed on two different types of models and the multidimensional approach shows a significant reduction in the number of page accesses over a large document collection.
Efficient multi-way text categorization via generalized discriminant analysis
Authors: Tao Li, Shenghuo Zhu, Mitsunori Ogihara
Venue: CIKM
Year: 2003

Text categorization is an important research area and has been receiving much attention due to the growth of the on-line information and of Internet. Automated text categorization is generally cast as a multi-class classification problem. Much of previous work focused on binary document classification problems. Support vector machines (SVMs) excel in binary classification, but the elegant theory behind large-margin hyperplane cannot be easily extended to multi-class text classification. In addition, the training time and scaling are also important concerns. On the other hand, other techniques naturally extensible to handle multi-class classification are generally not as accurate as SVM. This paper presents a simple and efficient solution to multi-class text categorization. Classification problems are first formulated as optimization via discriminant analysis. Text categorization is then cast as the problem of finding coordinate transformations that reflects the inherent similarity from the data. While most of the previous approaches decompose a multiclass classification problem into multiple independent binary classification tasks, the proposed approach enables direct multi-class classification. By using Generalized Singular Value Decomposition (GSVD), a coordinate transformation that reflects the inherent class structure indicated by the generalized singular values is identified. Extensive experiments demonstrate the efficiency and effectiveness of the proposed approach.
Content-based retrieval in hybrid peer-to-peer networks
Authors: Jie Lu, James P. Callan
Venue: CIKM
Year: 2003

Hybrid peer-to-peer architectures use special nodes to provide directory services for regions of the network ("regional directory services"). Hybrid peer-to-peer architectures are a potentially powerful model for developing large-scale networks of complex digital libraries, but peer-to-peer networks have so far tended to use very simple methods of resource selection and document retrieval. In this paper, we study the application of content-based resource selection and document retrieval to hybrid peer-to-peer networks. The directory nodes that provide regional directory services construct and use the content models of neighboring nodes to determine how to route query messages through the network. The leaf nodes that provide information use content-based retrieval to decide which documents to retrieve for queries. The experimental results demonstrate that using content-based retrieval in hybrid peer-to-peer networks is both more accurate and more efficient for some digital library environments than more common alternatives such as Gnutella 0.6.
Extracting unstructured data from template generated web documents
Authors: Ling Ma, Nazli Goharian, Abdur Chowdhury, Misun Chung
Venue: CIKM
Year: 2003

We propose a novel approach that identifies web page templates and extracts the unstructured data. Extracting only the body of the page and eliminating the template increases the retrieval precision for the queries that generate irrelevant results. We believe that by reducing the number of irrelevant results; the users are encouraged to go back to a given site to search. Our experimental results on several different web sites and on the whole cnnfn collection demonstrate the feasibility of our approach.
Adding numbers to text classification
Authors: Sofus A. Macskassy, Haym Hirsh
Venue: CIKM
Year: 2003

Many real-world problems involve a combination of both text- and numerical-valued features. For example, in email classification, it is possible to use instance representations that consider not only the text of each message, but also numerical-valued features such as the length of the message or the time of day at which it was sent. Text-classification methods have thus far not easily incorporated numerical features. In earlier work we described an approach for converting numerical features into bags of tokens so that text classification methods can be applied to numerical classification problems, and showed that the resulting learning methods are competitive with traditional numerical classification methods. In this paper we use this as a way to learn on problems that involve a combination of text and numbers. We show that the results outperform competing methods. Further, we show that selecting a best classification method using text-only features and then adding numerical features to the problem (as might happen if numerical features are only later added to a pre existing text-classification problem) gives performance that rivals a more time-consuming approach of evaluating all classification methods using the full set of both text and numerical features.
Lattice-based tagging using support vector machines
Authors: James Mayfield, Paul McNamee, Christine D. Piatko, Claudia Pearce
Venue: CIKM
Year: 2003

Tagging algorithms have become increasingly important for identifying lexical and semantic features of unstructured text. We describe an approach to lattice-based tagging that estimates joint transition and emission probabilities using support vector machines. The technique offers several advantages over alternative methods, including the ability to accommodate non-local features, support for hundreds of thousands of features, and language-neutrality. We demonstrate the technique on two tagging applications: named entity recognition and part-of-speech tagging.
Similarity among melodies for music information retrieval
Authors: Takao Miura, Isamu Shioya
Venue: CIKM
Year: 2003

Here we discuss how to look for similar melody in music databases by giving monophonic melody in sheet. In this work, we utilize text expression (or sheet music) to describe music and introduce pitch spectrum of melodies. By this feature, we concisely distinguish music from tempo, transposition or other arbitrary expressions. We show the usefulness by experimental results.
Relevant query feedback in statistical language modeling
Authors: Ramesh Nallapati, W. Bruce Croft, James Allan
Venue: CIKM
Year: 2003

In traditional relevance feedback, researchers have explored relevant document feedback, wherein, the query representation is updated based on a set of relevant documents returned by the user. In this work, we investigate relevant query feedback, in which we update a document's representation based on a set of relevant queries. We propose four statistical models to incorporate relevant query feedback.To validate our models, we considered anchor text of incoming links to a given document as feedback queries and performed experiments on the home-page retrieval task of TREC 2001. Our results show that three of our four models outperform the query-likelihood baseline by at least 35% in MRR score on a test set.
XML parsing: a threat to database performance
Authors: Matthias Nicola, Jasmi John
Venue: CIKM
Year: 2003

XML parsing is generally known to have poor performance characteristics relative to transactional database processing. Yet, its potentially fatal impact on overall database performance is being underestimated. We report real-word database applications where XML parsing performance is a key obstacle to a successful XML deployment. There is a considerable share of XML database applications which are prone to fail at an early and simple road block: XML parsing. We analyze XML parsing performance and quantify the extra overhead of DTD and schema validation. Comparison with relational database performance shows that the desired response times and transaction rates over XML data can not be achieved without major improvements in XML parsing technology. Thus, we identify research topics which are most promising for XML parser performance in database systems.
Ontologies for semantically interoperable systems
Authors: Leo Obrst
Venue: CIKM
Year: 2003

In this paper, we discuss the use of ontologies for semantic interoperability and integration. We argue that information technology has evolved into a world of largely loosely coupled systems and as such, needs increasingly more explicit, machine-interpretable semantics. Ontologies in the form of logical domain theories and their knowledge bases offer the richest representations of machine-interpretable semantics for systems and databases in the loosely coupled world, thus ensuring greater semantic interoperability and integration. Finally, we discuss how ontologies support semantic interoperability in the real, commercial and governmental world.
Dimensionality reduction using magnitude and shape approximations
Authors: Ümit Y. Ogras, Hakan Ferhatosmanoglu
Venue: CIKM
Year: 2003

High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique, and use this lower dimensional synopsis to provide fast, approximate answers to the queries. In this paper, we develop a novel dimensionality reduction technique based on partitioning the high dimensional vector space into orthogonal subspaces. First, we find a relation between the Euclidian distance of two n-dimensional vectors and the Euclidian distances of their projections on the orthogonal subspaces. Then, based on this relation we develop a method to approximate the Euclidian distance using novel inner product approximation. This process allows us to incorporate the shape information of the vectors to this approximation. While the inner product approximation is symmetric, i.e., captures only the magnitude information of the data, the proposed method takes both the magnitude and shape information of the original vectors into account through partitioning. In the experiments, we demonstrate the effectiveness of our technique by comparing it with commonly used methods.
Light-weight xPath processing of XML stream with deterministic automata
Authors: Makoto Onizuka
Venue: CIKM
Year: 2003

Several applications based on XML stream processing have recently emerged, such as those for air traffic control and the selective dissemination of information (SDI). Their common need is to process a large number of XPath expressions in continuous XML streams at high throughput.This paper proposes four techniques for XPath expression processing based on Deterministic Finite Automata (DFA) for two purposes: to improve the memory usage efficiency of the automata and to support the processing of branching XPath expressions. The first technique, called n-DFA, clusters the given XPath expressions into n clusters to reduce the number of DFA states. The second, called shared NFA state table, lets the Non-Deterministic Finite Automata (NFA) state set be shared among the DFA states. Our experiments show that memory usage in an 8-DFA can, with the shared NFA state table, be reduced to 1/40th that of the original 1-DFA. The optimized NFA conversion and general XPath expression processing algorithm techniques contribute to the processing of branching XPath expressions efficiently; overall performance is better than is possible with earlier approaches.
Ontology-driven peer profiling in peer-to-peer enabled semantic web
Authors: Olena Parkhomenko, Yugyung Lee, E. K. Park
Venue: CIKM
Year: 2003

Peer-to-peer (P2P) systems and Semantic Web are two novel technologies that face a lot of shortcomings if considered as isolated paradigms. We present an approach that utilizes ontologies to set up a peer profile containing all the data, necessary for peer-to-peer interoperability. Using this profile can help eliminate some major issues persistent in current P2P networks, such as security, resource aggregation, group management. We also consider applications of peer profiling for Semantic Web built on P2P networks, such as an improved semantic search for resources, not explicitly published on the Web, but available in a P2P system. We develop the ontology-based peer profile in RDF format and demonstrate its manifold benefits for peer communication and knowledge discovery in both P2P networks and Semantic Web.
Techniques for efficient fragment detection in web pages
Authors: Lakshmish Ramaswamy, Arun Iyengar, Ling Liu, Fred Douglis
Venue: CIKM
Year: 2003

The existing approaches to fragment-based publishing, delivery and caching of web pages assume that the web pages are manually fragmented at their respective web sites. However manual fragmentation of web pages is expensive, error prone, and not scalable. This paper proposes a novel scheme to automatically detect and flag possible fragments in a web site. Our approach is basedonananalysisofthewebpagesdynamicallygeneratedat given web sites with respect to their information sharing behavior, personalization characteristics and change patterns. Categories and Subject Descriptors: H.3.3 [Information Systems- Information storage and retrieval]: Information search and retrieval
Boosting support vector machines for text classification through parameter-free threshold relaxation
Authors: James G. Shanahan, Norbert Roma
Venue: CIKM
Year: 2003

Support vector machine (SVM) learning algorithms focus on finding the hyperplane that maximizes the margin (the distance from the separating hyperplane to the nearest examples) since this criterion provides a good upper bound of the generalization error. When applied to text classification, these learning algorithms lead to SVMs with excellent precision but poor recall. Various relaxation approaches have been proposed to counter this problem including: asymmetric SVM learning algorithms (soft SVMs with asymmetric misclassification costs); uneven margin based learning; and thresholding. A review of these approaches is presented here. In addition, in this paper, we describe a new threshold relaxation algorithm. This approach builds on previous thresholding work based upon the beta-gamma algorithm. The proposed thresholding strategy is parameter free, relying on a process of retrofitting and cross validation to set algorithm parameters empirically, whereas our previous approach required the specification of two parameters (beta and gamma). The proposed approach is more efficient, does not require the specification of any parameters, and similarly to the parameter-based approach, boosts the performance of baseline SVMs by at least 20% for standard information retrieval measures.
Index construction for linear categorisation
Authors: Vaughan R. Shanks, Hugh E. Williams
Venue: CIKM
Year: 2003

Categorisation is a useful method for organising documents into subcollections that can be browsed or searched to more accurately and quickly meet information needs. On the Web, category-based portals such as Yahoo! and DMOZ are extremely popular: DMOZ is maintained by over 56,000 volunteers, is used as the basis of the popular Google directory, and is perhaps used by millions of users each day. Support Vector Machines (SVM) is a machine-learning algorithm which has been shown to be highly effective for automatic text categorisation. However, a problem with iterative training techniques such as SVM is that during their learning or training phase, they require the entire training collection to be held in main-memory; this is infeasible for large training collections such as DMOZ or large news wire feeds. In this paper, we show how inverted indexes can be used for scalable training in categorisation, and propose novel heuristics for a fast, accurate, and memory efficient approach. Our results show that an index can be constructed on a desktop workstation with little effect on categorisation accu-racy compared to a memory-based approach. We conclude that our techniques permit automatic categorisation using very large train-ing collections, vocabularies, and numbers of categories.
A visual interface technique for exploring OLAP data with coordinated dimension hierarchies
Authors: Mark Sifer
Venue: CIKM
Year: 2003

Multi-dimensional data occurs in many domains while a wide variety of text based and visual interfaces for querying such data exists. But many of these interfaces are not applicable to OLAP, as they do not support use of dimension hierarchies for selection and aggregation. We introduce an interface technique which supports visual querying of OLAP data, that has been implemented in the SGViewer tool. It is based on a data graph rather than a data cube representation of the data. Our interface presents each dimension hierarchy in a zoomable panel which supports selection and aggregation at multiple levels. Users explore data and query it by making selections in several dimension views. Three view coordinations are identified; progressive, global and result only. Our main contribution, the progressive view coordination provides better support for query refinement than existing interfaces, by helping users decide the next query step with intermediate result overviews, and by helping users change a previous selection decision with retained selection context views. Our interface technique is demonstrated with a web log dataset of visits organised into time, download, visitor and referrer address dimensions.
High dimensional reverse nearest neighbor queries
Authors: Amit Singh, Hakan Ferhatosmanoglu, Ali Saman Tosun
Venue: CIKM
Year: 2003

Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. However most of the above applications inherently involve high dimensions and high dimensional RNN problem is still unexplored. In this paper, we propose an approximate solution to answer RNN queries in high dimensions. Our approach is based on the strong correlation in practice between k-NN and RNN. It works in two phases. In the first phase the k-NN of a query point is found and in the next phase they are further analyzed using a novel type of query Boolean Range Query (BRQ). Experimental results show that BRQ is much more efficient than both NN and range queries, and can be effectively used to answer RNN queries. Performance is further improved by running multiple BRQ simultaneously. The proposed approach can also be used to answer other variants of RNN queries such as RNN of order k, bichromatic RNN, and Matching Query which has many applications of its own. Our technique can efficiently answer NN, RNN, and its variants with approximately same number of I/O as running a NN query.
Securely sharing neuroimagery
Authors: Kenneth P. Smith, Vipin Swarup, Sushil Jajodia, Donald B. Faatz, Todd Cornett, Jeffrey Hoyt
Venue: CIKM
Year: 2003

Shared scientific data, such as neuroimagery, offers great benefits to science. However, data owners must exercise custodial responsibilities which can conflict with the unhindered sharing of their data. Given simple choices of sharing widely or not at all, the result will frequently be no sharing. We hypothesize that neuroimagery sharing will be enhanced if data owners are provided with well-defined intermediate levels of data visibility. In this paper, we describe a broadly applicable data sharing model, Structured Sharing Communities (SSC), in which data becomes incrementally visible to communities structured as a complete partial-order; the associated properties of Privacy and Fairness regulate access to shared data. Within SSC, a customized policy space is defined capturing the sharing relationships among specific collaborators.
Exploiting syntactic structure of queries in a language modeling approach to IR
Authors: Munirathnam Srikanth, Rohini K. Srihari
Venue: CIKM
Year: 2003

Natural Language Processing (NLP) techniques have been explored to enhance the performance of Information Retrieval (IR) methods with varied results. Most efforts in using NLP techniques have been to identify better index terms for representing documents. This use in the indexing phase of IR has implicit effect on retrieval performance. However, the explicit use of NLP techniques during the retrieval or information seeking phase has been restricted to interactive or dialogue systems. Recent advances in IR are based on using Statistical Language Models (SLM) to represent documents and ranking them based on their model generating a given user query. This paper presents a novel method for using NLP techniques on user queries, specifically, a syntactic parse of a query, in the statistical language modeling approach to IR. In the proposed method, named Concept Language Models, a query is viewed as a sequence of concepts and a concept as a sequence terms. The paper presents different approximations to estimate the concept and term probabilities and compute the query likelihood estimate for documents. Some empirical results on TREC test collections comparing Concept Language Models with smoothed N-gram language models are presented.
Raindrop: a uniform and layered algebraic framework for XQueries on XML streams
Authors: Hong Su, Jinhui Jian, Elke A. Rundensteiner
Venue: CIKM
Year: 2003

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data. While the automata model is naturally suited for pattern matching on tokenized XML streams, the algebraic model in contrast is a well-established technique for set-oriented processing of self-contained tuples. However, neither automata nor algebraic models are well-equipped to handle both computation paradigms.
Information extraction from biomedical literature: methodology, evaluation and an application
Authors: L. Venkata Subramaniam, Sougata Mukherjea, Pankaj Kankar, Biplav Srivastava, Vishal S. Batra, Pasumarti V. Kamesam, Ravi Kothari
Venue: CIKM
Year: 2003

Journals and conference proceedings represent the dominant mechanisms of reporting new biomedical results. The unstructured nature of such publications makes it difficult to utilize data mining or automated knowledge discovery techniques. Annotation (or markup) of these unstructured documents represents the first step in making these documents machine analyzable. In this paper we first present a system called BioAnnotator for identifying and annotating biological terms in documents. BioAnnotator uses domain based dictionary look-up for recognizing known terms and a rule engine for discovering new terms. The combination and dictionary look-up and rules result in good performance (87% precision and 94% recall on the GENIA 1.1 corpus for extracting general biological terms based on an approximate matching criterion). To demonstrate the subsequent mining and knowledge discovery activities that are made feasible by BioAnnotator, we also present a system called MedSummarizer that uses the extracted terms to identify the common concepts in a given group of genes.