OpenRaiser/Intern-Atlas huggingface dataset:
RStar: an RDF storage and query system for enterprise resource management
Authors: Li Ma, Zhong Su, Yue Pan, Li Zhang, Tao Liu
Venue: CIKM
Year: 2004

Modern corporations operate in an extremely complex environment and strongly depend on all kinds of information resources across the enterprise. Unfortunately, with the growth of an enterprise, its information resources are not only heterogeneous but also distributed in physically different systems and databases. How to effectively exploit information across the enterprise is becoming a critical but hard problem. In recent years, metadata which is the detailed description of the data is used to efficiently exploit information resources in the web. The World Wide Web Consortium (W3C) recommends the resource description framework (RDF) as a standard for the definition and use of metadata descriptions of resources in the web. In this paper, we present an RDF storage and query system called RStar for enterprise resource management. RStar uses a relational database as the persistent data store and defines RStar Query Language (RSQL) for resource retrieval. Currently, most of existing RDF storage and query systems are evaluated on small data sets and no detailed performance analysis is given for such systems. Therefore, we conduct extensive experiments on a large scale data set to investigate the performance problem in RDF storage. Such analysis will be helpful for designing RDF storage and query systems as well as for understanding not well-solved issues in RDF based enterprise resource management. In addition, experiences and lessons learned in our implementation are presented for further research and development.
A framework for refining similarity queries using learning techniques
Authors: Yiming Ma, Qi Zhong, Sharad Mehrotra, Dawit Yimam Seid
Venue: CIKM
Year: 2004

In numerous applications that deal with similarity search, a user may not have an exact idea of his information need and/or may not be able to construct a query that exactly captures his notion of similarity. A promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use the feedback on the relevance of the retrieved objects to refine the query. In this paper, we explore such a refinement strategy for a general class of SQL similarity queries. Our approach casts the refinement problem as that of learning concepts using examples. This is achieved by viewing the tuples on which a user provides feedback as a labeled training set for a learner. Under this setup, SQL query refinement consists of two learning tasks, namely learning the structure of the SQL query and learning the relative importance of the query components. The paper develops appropriate machine learning approaches suitable for these two learning tasks. The primary contribution of the paper is a general refinement framework that decides when each learner is invoked in order to quickly learn the user query. Experimental analyses over many real life datasets and queries show that our strategy outperforms the existing approaches significantly in terms of retrieval accuracy and query simplicity.
Dynamic extraction topic descriptors and discriminators: towards automatic context-based topic search
Authors: Ana Gabriela Maguitman, David B. Leake, Thomas Reichherzer, Filippo Menczer
Venue: CIKM
Year: 2004

Effective knowledge management may require going beyond initial knowledge capture, to support decisions about how to extend previously-captured knowledge. Electronic <i>concept maps,</i> interlinked with other concept maps and multimedia resources, can provide rich <i>knowledge models</i> for human knowledge capture and sharing. This paper presents research on methods for supporting experts as they extend these knowledge models, by searching the Web for new context-relevant topics as candidates for inclusion. This topic search problem presents two challenges: First, how to formulate queries to seek topics that reflect the context of the current knowledge model, and, second, how to identify candidate topics with the right balance of novelty and relevance. More generally, this problem raises the broad question of the interaction of topic information from the local analysis space (a collected set of documents) and the global search space (the Web). The paper develops a framework for understanding this interaction, and proposes and evaluates techniques for addressing the query formation and topic identification questions by dynamically extracting topic descriptors and discriminators from a knowledge model, to characterize information needs for retrieval and filtering of relevant material. Using these techniques, we have developed a support tool that starts from a knowledge model under construction and automatically produces a set of suggestions for topics to include, proactively supporting users as they extend knowledge models.
On structuring formal, semi-formal and informal data to support traceability in systems engineering environments
Authors: Paul Mason, Ken Cosh, Pulyamon Vihakapirom
Venue: CIKM
Year: 2004

The development of large, complex systems poses a number of challenges for systems engineers, not least of which is the ability to ensure user requirements have been satisfied. Effective requirements management - an amalgam of information capture, information <i>storage</i> and <i>management</i>, and information <i>dissemination</i> activities - is crucial in that respect. In this paper we concentrate on one of the core issues of <i>information management</i> in a requirements management context - namely <i>traceability</i>.
A dimensionality reduction technique for efficient similarity analysis of time series databases
Authors: Vasileios Megalooikonomou, Guo Li, Qiang Wang
Venue: CIKM
Year: 2004

Efficiently searching for similarities among time series and discovering interesting patterns is an important and non-trivial problem with applications in many domains. The high dimensionality of the data makes the analysis very challenging. To solve this problem, many dimensionality reduction methods have been proposed. PCA (Piecewise Constant Approximation) and its variant have been shown efficient in time series indexing and similarity retrieval. However, in certain applications, too many false alarms introduced by the approximation may reduce the overall performance dramatically. In this paper, we introduce a new piecewise dimensionality reduction technique that is based on Vector Quantization. The new technique, PVQA (Piecewise Vector Quantized Approximation), partitions each sequence into equi-length segments and uses vector quantization to represent each segment by the closest (based on a distance metric) codeword from a codebook of key-sequences. The efficiency of calculations is improved due to the significantly lower dimensionality of the new representation. We demonstrate the utility and efficiency of the proposed technique on real and simulated datasets. By exploiting prior knowledge about the data, the proposed technique generally outperforms PCA and its variants in similarity searches.
Using bi-modal alignment and clustering techniques for documents and speech thematic segmentations
Authors: Dalila Mekhaldi, Denis Lalanne, Rolf Ingold
Venue: CIKM
Year: 2004

In this paper, we describe a new method for a simultaneous thematic segmentation of the meeting dialogs and the documents discussed or visible throughout the meeting. This bi-modal method is suitable for multimodal applications that are centered on documents, such as meetings and lectures, where documents can be aligned with meeting dialogs. Bringing into play this alignment, our bi-modal segmentation method first transforms its results into a set of nodes in a 2D graph space, where the two axes represent respectively the document units and the meeting dialogs units. Secondly, via a clustering method, the most connected regions in the constituted bi-graph are detected. Finally, the denser clusters are projected on the two axes. The two sequences of segments, obtained on both axes, represent the thematic structure of the document and of the meeting dialogs respectively. We present in this article this bi-modal segmentation technique and its performance compared with two mono-modal segmentation methods. Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing] indexing methods;
Document clustering based on cluster validation
Authors: Zheng-Yu Niu, Dong-Hong Ji, Chew Lim Tan
Venue: CIKM
Year: 2004

International Conference on Information and Knowledge Management, Proceedings
Acquisition of categorized named entities for web search
Authors: Marius Pasca
Venue: CIKM
Year: 2004

The recognition of names and their associated categories within unstructured text traditionally relies on semantic lexicons and gazetteers. The amount of effort required to assemble large lexicons confines the recognition to either a limited domain (e.g., <i>medical imaging</i>), or a small set of pre-defined, broader categories of interest (e.g., <i>persons</i>, <i>countries</i>, <i>organizations</i>, <i>products</i>). This constitutes a serious limitation in an information seeking context. In this case, the categories of potential interest to users are more diverse (<i>universities</i>, <i>agencies</i>, <i>retailers</i>, <i>celebrities</i>), often refined (e.g., <i>SLR digital cameras</i>, <i>programming languages</i>, <i>multinational oil companies</i>), and usually overlapping (e.g., the same entity may be concurrently a <i>brand name</i>, a <i>technology company</i>, and an <i>industry leader</i>). We present a lightly supervised method for acquiring named entities in arbitrary categories. The method applies lightweight lexico-syntactic extraction patterns to the unstructured text of Web documents. The method is a departure from traditional approaches to named entity recognition in that: 1) it does not require any start-up seed names or training; 2) it does not encode any domain knowledge in its extraction patterns; 3) it is only lightly supervised, and data-driven; 4) it does not impose any a-priori restriction on the categories of extracted names. We illustrate applications of the method in Web search, and describe experiments on 500 million Web documents and news articles.
CiteSeer-API: towards seamless resource location and interlinking for digital libraries
Authors: Yves Petinot, C. Lee Giles, Vivek Bhatnagar, Pradeep B. Teregowda, Hui Han, Isaac G. Councill
Venue: CIKM
Year: 2004

We introduce CiteSeer-API, a public API to CiteSeer-like services. CiteSeer-API is SOAP/WSDL based and allows for easy programmatical access to all the specific functionalities offered by CiteSeer services, including full text search of documents and citations and citation-based document discovery. In order to enable operability and interlinking with arbitrary software agents and digital library systems, CiteSeer-API uses digital content signatures to create system-independent handles for the Document, Citation and Group resources of CiteSeer servers. We discuss specific functionalities of CiteSeer-API that take advantage of these handlers in order to enable seamless location of CiteSeer resources. Finally we argue that the digital signature scheme used by CiteSeer-API is well suited for the creation of machine-usable semantic descriptions of digital library services which is the key toward seamless discovery and integration of services such as CiteSeer-API. CiteSeer-API is currently showcased on CiteSeer.IST, the CiteSeer server of the School of Information Science and Technology at the Pennsylvania State University.
Providing consistent and exhaustive relevance assessments for XML retrieval evaluation
Authors: Benjamin Piwowarski, Mounia Lalmas
Venue: CIKM
Year: 2004

Comparing retrieval approaches requires test collections, which consist of documents, queries and relevance assessments. Obtaining consistent and exhaustive relevance assessments is crucial for the appropriate comparison of retrieval approaches. Whereas the evaluation methodology for flat text retrieval approaches is well established, the evaluation of XML retrieval approaches is a research issue. This is because XML documents are composed of nested components that cannot be considered independent in terms of relevance. This paper describes the methodology adopted in INEX (the INitiative for the Evaluation of XML Retrieval) to ensure consistent and exhaustive relevance assessments.
The EDAM project: mining mass spectra and more
Authors: Raghu Ramakrishnan
Venue: CIKM
Year: 2004

The EDAM project is a collaborative effort between computer scientists and environmental chemists at Carleton College and UW-Madison. The goal is to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. We are investigating techniques for automatically labeling mass spectra from different kinds of aerosol mass spectrometers, and then analyzing and exploring the rich spatiotemporal information collected from multiple geographically distributed instruments. In this talk, I will present an overview of some novel data mining problems, describe some of the techniques we are developing to address them, and discuss the broader applicability of these techniques to problems from other domains.
A vertical distance-based outlier detection method with local pruning
Authors: Dongmei Ren, Imad Rahal, William Perrizo, Kirk Scott
Venue: CIKM
Year: 2004

"One person's noise is another person's signal". Outlier detection is used to clean up datasets and also to discover useful anomalies, such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations, etc. Thus, outlier detection is critically important in the information-based society. This paper focuses on finding outliers in large datasets using distance-based methods. First, to speedup outlier detections, we revise Knorr and Ng's distance-based outlier definition; second, a vertical data structure, instead of traditional horizontal structures, is adopted to facilitate efficient outlier detection further. We tested our methods against national hockey league dataset and show an order of magnitude of speed improvement compared to the contemporary distance-based outlier detection approaches.
Of parts and relationships: an unending quest
Authors: Isidore Rigoutsos
Venue: CIKM
Year: 2004

Systems biology is a field which focuses on the interpretation of large, diverse sets of biological measurements in order to elucidate the complex mechanisms that underly important and (seemingly simple) macroscopic phenotypes. The problem at hand is hierarchical in nature, with the hierarchy spanning many levels. Each of these levels can be thought of as comprising multiple active agents that are diverse in their nature (e.g. genes, proteins, pathways, organelles, etc) and also in their behavior. It is within this setting that one seeks to build an integrated view of the system under study, as soon as the relevant units and the complex inter- and intra-level relationships in which these units participate have been characterized.
TEG: a hybrid approach to information extraction
Authors: Binyamin Rosenfeld, Ronen Feldman, Moshe Fresko, Jonathan Schler, Yonatan Aumann
Venue: CIKM
Year: 2004

This paper describes a hybrid statistical and knowledge-based information extraction model, able to extract entities and relations at the sentence level. The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labor by relying on statistics drawn from a training corpus. The implementation of the model, called TEG (Trainable Extraction Grammar), can be adapted to any IE domain by writing a suitable set of rules in a SCFG (Stochastic Context Free Grammar) based extraction language, and training them using an annotated corpus. The system does not contain any purely linguistic components, such as PoS tagger or parser. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems, while requiring orders of magnitude less manual rule writing and smaller amount of training data. The improvement in accuracy is slight for named entity extraction task and more pronounced for relation extraction.
Circumstance-based categorization analysis of knowledge management systems for the japanese market
Authors: Makoto Sano, David A. Evans
Venue: CIKM
Year: 2004

We conducted a survey of thirty of the approximately 1,700 customers of Justsystem Corporation's knowledge-management applications. Our goal was to discover the kinds of functions that customers hoped to address in their next-generation use of knowledge management technology and to assess the core processes that we will need to deploy in our products to address their desired solutions. In particular, we sought to analyze our customers' requirements along dimensions that take account of both the context of use of the application and its stage in the cycle of knowledge creation and use. As part of our analysis, we were able to classify all customer cases as focused by one or more of three Goals, supported by one or more of eleven technology Means. To establish appropriate categories of use, we exploited the stages of the <i>SECI Model</i>, several other transactional categories of knowledge use, and whether activities were targeted at internal or external users. Through the analysis, we found the typical technology components (Means) for each stage of knowledge creation and use associated with each set of goals. We consider such analysis essential to the task of designing next-generation knowledge-management applications and critical to overcoming the unfortunate tendency of developers to devise solutions that bear little relation to the true needs of users.
Integrating heterogeneous reatures for efficient content based music retrieval
Authors: Jialie Shen, John Shepherd, Anne H. H. Ngu
Venue: CIKM
Year: 2004

In this paper, we present a novel feature extraction method facilitating efficient content-based music retrieval and classification, called <i>InMAF</i>. The goal of our approach is to allow straightforward incorporation of multiple musical features, such as timbral texture, pitch and rhythm structure, into a single low dimensional vector that is effective for retrieval and classification. Unlike earlier approaches that used only acoustic properties as the basis for retrieval, our approach can easily incoporate human music perception to improve accuracy of retrieval and classification process. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification (using a variety of machine learning algorithms), query effectiveness and robustness against audio distortion.
Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model
Authors: Luo Si, Rong Jin
Venue: CIKM
Year: 2004

Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness. The main problem with previous research is that the content information and the rating information are not combined in an integrated way. This paper presents a unified probabilistic framework that allows the mutual interaction between these two types of information. Experiments have shown that the new unified filtering algorithm outperforms a pure collaborative filtering approach, a pure content-based filtering approach and another unified filtering algorithm.
Goal-oriented methods and meta methods for document classification and their parameter tuning
Authors: Stefan Siersdorfer, Sergej Sizov, Gerhard Weikum
Venue: CIKM
Year: 2004

Automatic text classification methods come with various calibration parameters such as thresholds for probabilities in Bayesian classifiers or for hyperplane distances in SVM classifiers. In a given application context these parameters should be set so as to meet the relative importance of various result quality metrics such as precision versus recall. In this paper we consider classifiers that can accept a document for a topic, reject it, or abstain. We aim to meet the application's goals in terms of accuracy (i.e., avoid false acceptances or rejections) and loss (i.e., limit the fraction of documents for which no decision is made). To this end we investigate restrictive forms of Support Vector Machine classifiers and we develop meta methods that split the training data into subsets for independently trained classifiers and then combine the results of these classifiers. These techniques tend to improve accuracy at the expense of document loss. We develop estimators that help to predict the accuracy and loss for a given setting of the methods' tuning parameters, and a methodology for efficiently deriving a setting that meets the application's goals. Our experiments confirm the practical viability of the approach.
Processing content-oriented XPath queries
Authors: Börkur Sigurbjörnsson, Jaap Kamps, Maarten de Rijke
Venue: CIKM
Year: 2004

Document-centric XML collections contain text-rich documents, marked up with XML tags that add lightweight semantics to the text. Querying such collections calls for a hybrid query language: the text-rich nature of the documents suggests a content-oriented (IR) approach, while the mark-up allows users to add structural constraints to their IR queries. Hybrid queries tend to be more expressive, which should lead---in principle---to better retrieval performance. In practice, the processing of these hybrid queries within an IR systems turns out to be far from trivial, because a delicate balance between structural and content information needs to be sought. We propose an approach to processing such hybrid content-and-structure queries that decomposes a query into multiple content-only queries whose results are then combined in ways determined by the structural constraints of the original query. We evaluate our methods using the INEX 2003 test-suite, and show (1) that effective ways of processing of content-oriented XPath queries are non-trivial, (2) that there are differences in the effectiveness for different topics types, but (3) that with appropriate processing methods retrieval effectiveness can improve.
Design of a data warehouse system for network/web services
Authors: Anoop Singhal
Venue: CIKM
Year: 2004

This paper describes the architecture and design of a data warehouse for AT&T Business Services. The main purpose of our system is to generate reports about the performance and reliability of the network. We describe the architecture of our system and discuss some open research problems in this area.