OpenRaiser/Intern-Atlas huggingface dataset:
Generating better concept hierarchies using automatic document classification
Authors: Razvan Stefan Bot, Yi-fang Brook Wu, Xin Chen, Quanzhi Li
Venue: CIKM
Year: 2005

This paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. The aim of the technique is to separate the initial retrieved documents into topical oriented categories, prior to the actual concept hierarchy generation. The topical categories correspond to different semantic aspects of the query. This is done using a 1-of-n automatic document classification, on the initial set of returned documents. Then, an individual topical concept hierarchy is automatically generated inside each of the resulted categories. Both steps are executed on the fly at retrieval time. Due to the efficiency constraints imposed by the web retrieval context, the algorithm only uses document snippets (rather than full web pages) for both document classification and concept hierarchy generation. Experimental results show that the algorithm is able to improve the quality of the concept hierarchy presented to the searcher; at the same time, the efficiency parameters are kept within reasonable intervals.
Static score bucketing in inverted indexes
Authors: Chavdar Botev, Nadav Eiron, Marcus Fontoura, Ning Li, Eugene J. Shekita
Venue: CIKM
Year: 2005

Maintaining strict static score order of inverted lists is a heuristic used by search engines to improve the quality of query results when the entire inverted lists cannot be processed. This heuristic, however, increases the cost of index generation and requires complex index build algorithms. In this paper, we study a new index organization based on static score bucketing. We show that this new technique significantly improves in index build performance while having minimal impact on the quality of search results.
Indexing time vs. query time: trade-offs in dynamic information retrieval systems
Authors: Stefan Büttcher, Charles L. A. Clarke
Venue: CIKM
Year: 2005

We examine issues in the design of fully dynamic information retrieval systems supporting both document insertions and deletions. The two main components of such a system, index maintenance and query processing, affect each other, as high query performance is usually paid for by additional work during update operations. Two aspects of the system -- incremental updates and garbage collection for delayed document deletions -- are discussed, with a focus on the respective indexing vs. query performance trade-offs. Depending on the relative number of queries and update operations, different strategies lead to optimal overall performance.
Mining officially unrecognized side effects of drugs by combining web search and machine learning
Authors: Carlo Curino, Yuanyuan Jia, Bruce Lambert, Patricia M. West, Clement T. Yu
Venue: CIKM
Year: 2005

We consider the problem of finding officially unrecognized side effects of drugs. By submitting queries to the Web involving a given drug name, it is possible to retrieve pages concerning the drug. However, many retrieved pages are irrelevant and some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time consuming and expensive, we provide an automatic method to generate the training data. The method is also shown to be very accurate. The side effects of three drugs which are not recognized by FDA are validated by an expert. We believe that the same approach can be applied to many real life problems and will yield high precision. Thus, this could lead a new way to perform retrieval with high accuracy.
Incremental test collections
Authors: Ben Carterette, James Allan
Venue: CIKM
Year: 2005

Corpora and topics are readily available for information retrieval research. Relevance judgments, which are necessary for system evaluation, are expensive; the cost of obtaining them prohibits in-house evaluation of retrieval systems on new corpora or new topics. We present an algorithm for cheaply constructing sets of relevance judgments. Our method intelligently selects documents to be judged and decides when to stop in such a way that with very little work there can be a high degree of confidence in the result of the evaluation. We demonstrate the algorithm's effectiveness by showing that it produces small sets of relevance judgments that reliably discriminate between two systems. The algorithm can be used to incrementally design retrieval systems by simultaneously comparing sets of systems. The number of additional judgments needed after each incremental design change decreases at a rate reciprocal to the number of systems being compared. To demonstrate the effectiveness of our method, we evaluate TREC ad hoc submissions, showing that with 95% fewer relevance judgments we can reach a Kendall's tau rank correlation of at least 0.9.
Knowledge-based assessment of behavior in dynamic environments
Authors: Michael Cebulla
Venue: CIKM
Year: 2005

In this paper we propose a knowledge-based approach for thesupport of adaptive and context-aware behavior in autonomic systems. In order to providemeaningful and adequate behavior in the presence of dynamic (or adverse) environmentssystems must possess the ability to assess their current state on the basis of knowledge about themselves andtheir environments. We propose to use ontologies for therepresentation of knowledge and its integration into a general systemsarchitecture supporting autonomic behavior. Especially we rely on fuzzy description logics in order to support robust automatic reasoning w.r.t. environmental conditions.We distinguish several aspects of knowledge and use multiset term rewritingto describe mechanisms of coordination between them.We discuss the advantages of our approach and give examples forparadigmatic applications.
Statistical relationship determination in automatic thesaurus construction
Authors: Libo Chen, Peter Fankhauser, Ulrich Thiel, Thomas Kamps
Venue: CIKM
Year: 2005

Statistical relationship determination among terms is one of the key issues in automatic thesaurus construction. We systematically analyze existing relevant approaches based on their underlying probabilistic assumptions, and propose a combined approach that overcomes their limitations.
A practical system of keyphrase extraction for web pages
Authors: Mo Chen, Jian-Tao Sun, Hua-Jun Zeng, Kwok-Yan Lam
Venue: CIKM
Year: 2005

Keyphrases can be used to facilitate Web users grasping the main topic(s) of a Web page. We present a practical system of automatic keyphrase extraction for Web pages. In this system, a regression model was first trained based on a set of human-labeled documents. Then it was used to extract keyphrases from new pages automatically. This paper makes three contributions. First, the structure information in a Web page was investigated for keyphrase extraction task. Second, the query log data associated with a Web page collected by a search engine server were used to help keyphrase extraction. Third, a method was put forward in this paper in order to evaluate the similarity of phrases.
MailRank: using ranking for spam detection
Authors: Paul-Alexandru Chirita, Jörg Diederich, Wolfgang Nejdl
Venue: CIKM
Year: 2005

Can we use social networks to combat spam? This paper investigates the feasibility of MailRank, a new email ranking and classification scheme exploiting the social communication network created via email interactions. The underlying email network data is collected from the email contacts of all MailRank users and updated automatically based on their email activities to achieve an easy maintenance. MailRank is used to rate the sender address of arriving emails such that emails from trustworthy senders can be ranked and classified as spam or non-spam. The paper presents two variants: Basic MailRank computes a global reputation score for each email address, whereas in Personalized MailRank the score of each email address is different for each MailRank user. The evaluation shows that MailRank is highly resistant against spammer attacks, which obviously have to be considered right from the beginning in such an application scenario. MailRank also performs well even for rather sparse networks, i.e., where only a small set of peers actually take part in the ranking of email addresses.
Frequent pattern discovery with memory constraint
Authors: Kun-Ta Chuang, Ming-Syan Chen
Venue: CIKM
Year: 2005

We explore in this paper a practicably interesting mining task to retrieve frequent itemsets with memory constraint. As opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to constrain the upper memory size that can be utilized by mining frequent itemsets in this paper.
Interconnection semantics for keyword search in XML
Authors: Sara Cohen, Yaron Kanza, Benny Kimelfeld, Yehoshua Sagiv
Venue: CIKM
Year: 2005

A framework for describing semantic relationships among nodes in XML documents is presented. In contrast to earlier work, the XML documents may have ID references (i.e., they correspond to graphs and not just trees). A specific interconnection semantics in this framework can be defined explicitly or derived automatically. The main advantage of interconnection semantics is the ability to pose queries on XML data in the style of keyword search. Several methods for automatically deriving interconnection semantics are presented. The complexity of the evaluation and the satisfiability problems under the derived semantics is analyzed. For many important cases, the complexity is tractable and hence, the proposed interconnection semantics can be efficiently applied to real-world XML documents.
Semantic similarity over the gene ontology: family correlation and selecting disjunctive ancestors
Authors: Francisco M. Couto, Mário J. Silva, Pedro Coutinho
Venue: CIKM
Year: 2005

Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. In most biological databases, proteins are already annotated with ontology terms. Previous studies identified a correlation between the sequence similarity and the semantic similarity of proteins. The semantic similarity of proteins was computed from their annotated GO terms. However, proteins sharing a biological role do not necessarily have a similar sequence.This paper introduces our study of the correlation between GO and family similarity. Family similarity overcomes some of the limitations of sequence similarity, thus we obtained a strong correlation between GO and family similarity. Additionally, this paper introduces GraSM, a novel method that uses all the information in the graph structure of the GO, instead of considering it as a hierarchical tree. When calculating the semantic similarity of two concepts, GraSM selects the disjunctive common ancestors rather than only using the most informative common ancestor. GraSM produced a higher family similarity correlation than the original semantic similarity measures.
An evaluation of evolved term-weighting schemes in information retrieval
Authors: Ronan Cummins, Colm O&apos, Riordan
Venue: CIKM
Year: 2005

This paper presents an evaluation of evolved term-weighting schemes on short, medium and long TREC queries. A previously evolved global (collection-wide) term-weighting scheme is evaluated on unseen TREC data and is shown to increase mean average precision over idf. A local (within-document) evolved term-weighting scheme is presented which is dependent on the best performing global scheme. The full evolved scheme (i.e. the combined local and global scheme) is compared to both the BM25 scheme and the Pivoted Normalisation scheme.Our results show that the local evolved solution does not perform well on some collections due to its document normalisation properties and we conclude that Okapi-tf can be tuned to interact effectively with the evolved global weighting scheme presented and increase mean average precision over the standard BM25 scheme.
Minimal document set retrieval
Authors: Wei Dai, Rohini K. Srihari
Venue: CIKM
Year: 2005

This paper presents a novel formulation and approach to the minimal document set retrieval problem. Minimal Document Set Retrieval (MDSR) is a promising information retrieval task in which each query topic is assumed to have different subtopics; the task is to retrieve and rank relevant document sets with maximum coverage but minimum redundancy of subtopics in each set. For this task, we propose three document set retrieval and ranking algorithms: Novelty Based method, Cluster Based method and Subtopic Extraction Based method. In order to evaluate the system performance, we design a new evaluation framework for document set ranking which evaluates both relevance between set and query topic, and redundancy within each set. Finally, we compare the performance of the three algorithms using the TREC interactive track dataset. Experimental results show the effectiveness of our algorithms.
Automatic construction of multifaceted browsing interfaces
Authors: Wisam Dakka, Panagiotis G. Ipeirotis, Kenneth R. Wood
Venue: CIKM
Year: 2005

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Interfaces that use multifaceted hierarchies represent a new powerful browsing paradigm which has been proven to be a successful complement to keyword searching. Thus far, multifaceted hierarchies have been created manually or semi-automatically, making it difficult to deploy multifaceted interfaces over a large number of databases. We present automatic and scalable methods for creation of multifaceted interfaces. Our methods are integrated with traditional relational databases and can scale well for large databases. Furthermore, we present methods for selecting the best portions of the generated hierarchies when the screen space is not sufficient for displaying all the hierarchy at once. We apply our technique to a range of large data sets, including annotated images, television programming schedules, and web pages. The results are promising and suggest directions for future research.
Connecting topics in document collections with stepping stones and pathways
Authors: Fernando Adrian Das Neves, Edward A. Fox, Xiaoyan Yu
Venue: CIKM
Year: 2005

In this paper, we present Stepping Stones and Pathways (SSP), an alternative model of building and presenting answers for the cases when queries on document collections cannot be answered just by a ranked list. Stepping Stones can handle questions like: What is the relation of topics X and Y? SSP addresses when the contents of a small set of related documents is needed as an answer rather than a single document, or when query splitting is required to satisfactorily explore a document space. Query results are networks of document groups representing topics, each group relating to and connecting (by documents) to other groups in the network. Thus, a network answers the user's information need. We devise new and more effective representations and techniques to visualize such answers, and to involve users as part of the answer-finding process. In order to verify the validity of our approach, and since the questions we aim to answer involve multiple topics, we performed a study involving a custom built broad collection of operating systems research papers, and evaluated the results with interested computer science students, using multiple measures.
Processing XPath queries with XML summaries
Authors: Takeharu Eda, Makoto Onizuka, Masashi Yamamuro
Venue: CIKM
Year: 2005

Range labeling and structural joins are well-studied techniques for efficiently processing XPath queries. However, when XPath queries become long, many times of structural joins are required. To solve this problem, we developed a method to reduce the number of joins and nodes read from the disk using strong DataGuides. Our method can process single paths without any joins and twig patterns with joins amongst branching nodes and leaves in queries. Experimental results verified that our approach outperforms the best optimization technique for structural joins by factors of up to several hundreds of times.
Scalable ranking for preference queries
Authors: Ying Feng, Divyakant Agrawal, Amr El Abbadi, Ambuj K. Singh
Venue: CIKM
Year: 2005

Top-k preference queries with multiple attributes are critical for decision-making applications. Previous research has concentrated on improving the computational efficiency mainly by using novel index structures and search strategies. Since current applications need to scale to terabytes of data and thousands of users, performance of such systems is strongly impacted by the amount of available memory. This paper proposes a scalable approach for memory-bounded top-k query processing.
Towards an approach for knowledge-based road detection
Authors: Mike Foedisch, Craig Schlenoff, Michael Shneier
Venue: CIKM
Year: 2005

Our previous work on road detection suggests the usage of prior knowledge in order to improve performance. In this paper we will explain our motivation for a novel approach, define requirements and point out issues, particularly concerning the representation of road depending on the use, which need to be addressed. The proposed system will provide symbolic data for high-level processes and guidance for low-level processes. Furthermore, we will outline the recognition approach based on previously discussed requirements and issues. This paper has a visionary character based on our experience with road detection for autonomous road vehicles.
A hybrid approach to NER by MEMM and manual rules
Authors: Moshe Fresko, Binyamin Rosenfeld, Ronen Feldman
Venue: CIKM
Year: 2005

This paper describes a framework for defining domain specific Feature Functions in a user friendly form to be used in a Maximum Entropy Markov Model (MEMM) for the Named Entity Recognition (NER) task. Our system called MERGE allows defining general Feature Function Templates, as well as Linguistic Rules incorporated into the classifier. The simple way of translating these rules into specific feature functions are shown. We show that MERGE can perform better from both purely machine learning based systems and purely-knowledge based approaches by some small expert interaction of rule-tuning.
Incremental stock time series data delivery and visualization
Authors: Tak-Chung Fu, Fu-Lai Chung, Pui-ying Tang, Robert Wing Pong Luk, Chak-man Ng
Venue: CIKM
Year: 2005

SB-Tree is a binary tree data structure proposed to represent time series according to the importance of data points. Its use in stock data management is distinguished by preserving the critical data points' attribute values, retrieving time series data according to the importance of data points and facilitating multi-resolution time series retrieval. As new stock data are available continuously, an effective updating mechanism for SB-Tree is needed. In this paper, a study of different updating approaches is reported. Three families of updating methods are proposed. They are periodic rebuild, batch update and point-by-point update. Their efficiency, effectiveness and characteristics are compared and reported.
Taxonomies by the numbers: building high-performance taxonomies
Authors: Stephen C. Gates, Wilfried Teiken, Keh-Shin F. Cheng
Venue: CIKM
Year: 2005

In this paper, we describe a system for the construction of taxonomies which yield high accuracies with automated categorization systems, even on Web and intranet documents. In particular, we describe the way in which measurement of five key features of the system can be used to predict when categories are sufficiently well defined to yield high accuracy categorization. We describe the use of this system to construct a large (8800-category) general-purpose taxonomy and categorization system.
Adaptive load shedding for windowed stream joins
Authors: Bugra Gedik, Kun-Lung Wu, Philip S. Yu, Ling Liu
Venue: CIKM
Year: 2005

We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventional approach of dropping tuples from the input streams, we explore the concept of selective processing for load shedding. We allow stream tuples to be stored in the windows and shed excessive CPU load by performing the join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are learned to be highly beneficial. We support such dynamic selective processing through three forms of runtime adaptations: adaptation to input stream rates, adaptation to time correlation between the streams and adaptation to join directions. Indexes are used to further speed up the execution of stream joins. Experiments are conducted to evaluate our adaptive load shedding in terms of output rate. The results show that our selective processing approach to load shedding is very effective and significantly outperforms the approach that drops tuples from the input streams.
On off-topic access detection in information systems
Authors: Nazli Goharian, Ling Ma
Venue: CIKM
Year: 2005

We focus on detecting insider access violations to off-topic documents. Previously, we utilized information retrieval techniques, e.g., clustering and relevance feedback, to warn of potential misuse. For the relevance feedback approach, we minimize the indicative features needed for detection using data mining techniques. We show that the derived reduced feature subset achieves equivalent performance to that of the previously derived full set of features.
Collision avoidance with bipartite arrangements
Authors: Björn Gottfried
Venue: CIKM
Year: 2005

We demonstrate that qualitative spatial reasoning techniquesprovide useful means for autonomous systems in the context of collision avoidance. A qualitative spatial representation is presented which relies on quite abstract distinctions which describe how two objects can be aligned relative to each other. Possible alignments involve left and right, front and back, during, contains, and overlaps. It is shown how such qualitative relations already allow to make important decisions,for instance, in order to predict collisions. While the proposed calculus guarantees that predictions about collision-free situations never lead to collisions, predictions about collisions do not necessarily lead to collisions (though they occur more likely when they are predicted). We shall learn that it is by no means necessary to utilise precise measurements in order to predict collisions.