Information Retrieval Community Research Articles

A ranking algorithm for query expansion based on the term's appearing probability in the single document

PurposeThe purpose of this paper is to establish a new approach for solving the expansion term problem.Design/methodology/approachThis study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.FindingsThe results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.Research limitations/implicationsThe paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.Originality/valueStable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.

Semantic Federation of Product Information from Structured and Unstructured Sources

Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.

PISA: A framework for integrating uncooperative peers into P2P-based federated search

In the past years, federated search over peer-to-peer (P2P) networks has attracted considerable attention from the information retrieval community. Most previous work assumes a so-called cooperative environment, where each peer can actively participate in information publishing and document indexing in a distributed manner. In contrast, little prior work has addressed the problem of incorporating uncooperative peers, which do not publish their own corpus statistics over a network. In this paper, we present a P2P-based federated search framework named PISA, which incorporates uncooperative peers when providing search service. Specifically, we (i) propose a heuristic query-based sampling approach named HQBS, which can obtain high-quality resource descriptions from uncooperative peers at a low communication cost; (ii) present two result merging methods, called RISE and RISE+, to merge the results returned by uncooperative peers; (iii) develop a method called Controlled and Selective Update (CSU) to efficiently maintain the index directory of PISA. Our extensive experiments on the TREC WT10g data set demonstrate that PISA can provide high-quality search results and integrate uncooperative peers at low cost.

Connecting the dots

Symbolic Computation is an area of computer science that after 20 years of initial research had its acme in the mid-1980s, when its many new algorithms were made available in "math systems for the masses" like Mathematica and Maple. Computational algebra and computational logic are the two main pillars on which this discipline is based. Currently, the field experiences a new blossom by the integration and combination of new numeric, algebraic, geometric and logic algorithms made available in new, interactive and easy-to-use versions of these systems combined with web services. Spectacular new applications are reported in areas so different as elementary particle physics, cryptography, and automated software generation. One of the most outstanding outcomes of Symbolic Computation is the recently born Wolfram Alpha system, fully programmed in Mathematical. The goal of this writing is to remind the Information Retrieval community of the chances and capabilities offered by Symbolic Computation.

Applying Biomedical Ontologies on Semantic Query Expansion

Abstract*1- Introduction*The interpretation of a question (or information need) depends, among other things, of a series of lexicalsemantic relations that complement and help the cognitive process of answering that information need. Despite this fact, currently used information retrieval mechanisms take few advantages of the semantic interpretation of users’ information needs (usually specified through keywords). In most of the cases, those mechanisms are based on keyword matching, and thus are excessively dependant on the query and document terms.There are several past results showing that, in general, information retrieval based on domain knowledge decreases the accuracy of keyword based search engines. We believe this approach deserves further discussion and experimentation, looking for more strong evidences that these negative results can really be generalized. Moreover, there are some questions left unanswered by previous work that our experiment is addressing:(i) Using a scientific ontology, with formal construction and maintenance processes, such as the OBO ontologies, would produce better results? (ii) Are there more efficient query expansion techniques using available domain knowledge?(iii) Is a scientific ontology complete enough to fulfill the information retrieval researchers’ needs, in general?*2- Semantic Query Expansion*To try to answer some of these questions, we run a query expansion experiment using the Gene Ontology (GO) as domain knowledge. As the document repository, we used an extraction of 10 years of PubMed publications (from 1994 to 2004), which contains approximately 4.6 Million documents. This dataset is a test collection used by the information retrieval community, called Genomic TREC.*3- Results*To evaluate our ontology-based semantic query expansion technique, we measured the effectiveness of the information retrieval mechanism with and without expansion. In a nutshell, the average result showed an increase of 28% on synonyms relations and a small decrease on other relations.Our results show a lot of consistence with past related work. In fact, if the expansion strategy does not selectively choose when and how to expand, only synonym relations are worth to be used. However, looking further, it is possible to find several opportunities to try other expansion strategies. For example, the problem with query expansion using generalization/specialization relationships is that, if it is always applied, the bad results are more frequent than the good ones. But, if the strategy is to be selective on when to use these relations for expansion, the increasing on accuracy can be outstanding. As shown by our experiment, there was a query with 98% increment on effectiveness. *4- Conclusion*We strongly believe that it is premature to assume that semantics-based query expansion is, in general, a recall-enhancing, precision-degrading technique. Our experiments suggest that by using scientific based ontologies (like OBO ontologies) with formal relations, it is possible to increase both recall and precision. Our group is currently revising this first experiment towards a better semantic query expansion strategy.*5- Acknowledgements*This work was partially funded by CAPES and CNPq research grants 311454/2006-2, 306889/2007-2 and 484713/2007-8.*References*_Fox E. Lexical relations enhancing effectiveness of information retrieval systems. SIGIR Forum, New York, v.15, n.3, p.5-3.__Voorhees E. Query expansion using lexicalsemantic relations. In: ACM SIGIR conference on research and development in information retrieval, Proceedings, Dublin:17, p.61–69, 1994_

An overview on XML similarity: Background, current trends and future directions

In recent years, XML has been established as a major means for information management, and has been broadly utilized for complex data representation (e.g. multimedia objects). Owing to an unparalleled increasing use of the XML standard, developing efficient techniques for comparing XML-based documents becomes essential in the database and information retrieval communities. In this paper, we provide an overview of XML similarity/comparison by presenting existing research related to XML similarity. We also detail the possible applications of XML comparison processes in various fields, ranging over data warehousing, data integration, classification/clustering and XML querying, and discuss some required and emergent future research directions.

A novel framework for efficient automated singer identification in large music databases

Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for efficient automated singer recognition. HSI uses multiple low-level features extracted from both vocal and nonvocal music segments to enhance the identification process; it achieves this via a hybrid architecture that builds profiles of individual singer characteristics based on statistical mixture models. An extensive experimental study on a large music database demonstrates the superiority of our method over state-of-the-art approaches in terms of effectiveness, efficiency, scalability, and robustness.

Spoken content retrieval

The second workshop on Searching Spontaneous Conversational Speech (SSCS 2008) was held in Singapore on July 24, 2008 in conjunction with the 31st Annual International ACM SIGIR Conference. The goal of the workshop was to bring the speech community and the information retrieval community together. The forum was designed to be conducive to the close interaction and the intense discussion necessary to promote fusion of these fields into a single discipline with a concerted vision of spoken content retrieval. At the workshop, talks and posters were presented covering a wide range of topics including vocabulary independent search, spoken term detection, combination of models/indexes, use of speech recognition lattices for search, segmentation, temporal analysis, benchmarking, exploitation of prosody, speech surrogates for user interfaces and multi-language collections. Demonstrations of speech-based retrieval systems from a variety of application domains introduced a strong practical emphasis into the workshop program. The workshop concluded with a panel discussion, whose goal it was to identify future research directions for speech retrieval. Among the important challenges identified during the panel discussions were: dealing with large scale multimedia collections, representing audio/video content effectively in the user interface, focusing on perfecting the component technologies on which speech retrieval systems are based, and developing systems and approaches that will enable users (both content seekers and content providers) to actively create their own speech search applications or contribute to the indexability of their content.

An empirical analysis of information retrieval based concept location techniques in software comprehension

Concept location, the problem of associating human oriented concepts with their counterpart solution domain concepts, is a fundamental problem that lies at the heart of software comprehension. Recent research has attempted to alleviate the impact of the concept location problem through the application of methods drawn from the information retrieval (IR) community. Here we present a new approach based on a complimentary IR method which also has a sound basis in cognitive theory. We compare our approach to related work through an experiment and present our conclusions. This research adapts and expands upon existing language modelling frameworks in IR for use in concept location, in software systems. In doing so it is novel in that it leverages implicit information available in system documentation. Surprisingly, empirical evaluation of this approach showed little performance benefit overall and several possible explanations are forwarded for this finding.

Conductors’ temporal gestures: Spatio temporal cues for visually mediated synchronization.

A growing body of research utilizies computational feature‐extraction methods to quantify both music and music‐related movement. The use of signal processing and visualization techniques, for example, is widespread in the music information retrieval community. At the same time, similar techniques are being applied to the analysis of movement data acquired using motion capture systems and are being increasingly used to quantify both temporal and expressive movements of musicians and others engaged in music‐related activity. One such activity is conducting, and this talk will focus on one specific aspect of this activity: how a conductor conveys the beat to musicians using gesture alone. A number of studies involving a variety of response paradigms, both movement and music feature extraction techniques, and both laboratory and live settings will be presented. Results of these studies suggest that perception of the beat is less related to spatial cues than it is to temporal cues, with periods of high acceleration along the trajectory of a gesture being the best predictor of perceived beat location. In other words, conductors communicate the beat primarily by varying the speed of movement along the trajectory, not by changing the direction of movement.

Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches.

Predicting Query Performance for Text Retrieval

PDF HTML阅读 XML下载 导出引用 引用提醒 文本检索的查询性能预测 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant No.60603094 (国家自然科学基金); the National Basic Research Program of China under Grant No.2004CB318109 (国家重点基础研究发展计划(973)); the Beijing Science and Technology Planning Program of China under Grant No.D0106008040291 (北京市科技计划) Predicting Query Performance for Text Retrieval Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:目前,查询性能预测(predicting query performance,简称PQP)已经被认为是检索系统最重要的功能之一.近几年的研究和实验表明,PQP技术在文本检索领域有着广阔的发展前景和拓展空间.对文本检索中的PQP进行综述,重点论述其主要方法和关键技术.首先介绍了常用的实验语料和评价体系;然后介绍了影响查询性能的各方面因素;之后,按照基于检索前和检索后的分类体系概述了目前主要的PQP方法;简介了PQP在几个方面的应用;最后讨论了PQP所面临的一些挑战. Abstract:Predicting query performance (PQP) has recently been recognized by the IR (information retrieval) community as an important capability for IR systems. In recent years, research work carried out by many groups has confirmed that predicting query performance is a good method to figure out the robustness problem of the IR system and useful to give feedback to users, search engines and database creators. In this paper, the basic predicting query performance approaches for text retrieval are surveyed. The data for experiments and the methods for evaluation are introduced, the contributions of different factors to overall retrieval variability across queries are presented, the main PQP approaches are described from Pre-Retrieval to Post-Retrieval aspects, and some applications of PQP are presented. Finally, several primary challenges and open issues in PQP are summarized. 参考文献 相似文献 引证文献

Cluster-based patent retrieval

Through the recent NTCIR workshops, patent retrieval casts many challenging issues to information retrieval community. Unlike newspaper articles, patent documents are very long and well structured. These characteristics raise the necessity to reassess existing retrieval techniques that have been mainly developed for structure-less and short documents such as newspapers. This study investigates cluster-based retrieval in the context of invalidity search task of patent retrieval. Cluster-based retrieval assumes that clusters would provide additional evidence to match user’s information need. Thus far, cluster-based retrieval approaches have relied on automatically-created clusters. Fortunately, all patents have manually-assigned cluster information, international patent classification codes. International patent classification is a standard taxonomy for classifying patents, and has currently about 69,000 nodes which are organized into a five-level hierarchical system. Thus, patent documents could provide the best test bed to develop and evaluate cluster-based retrieval techniques. Experiments using the NTCIR-4 patent collection showed that the cluster-based language model could be helpful to improving the cluster-less baseline language model.

Linear feature-based models for information retrieval

There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper, we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.

XML search

The development of approaches to access XML content has generated a wealth of issues in information retrieval (IR) and database (DB) (e.g., [2, 15, 17, 20, 19, 47, 26, 32, 24]). While the IR community has traditionally focused on searching unstructured content, and has developed various techniques for ranking query results and evaluating their effectiveness, the DB community has focused on developing query languages and efficient evaluation algorithms for highly structured content. Recent trends in DB and IR research demonstrate a growing interest in merging IR and DB techniques for accessing XML content. Support for a combination of "structured" and full-text search for effectively querying XML documents was unanimous in a recent panel at SIGMOD 2005 [3], and is being widely studied in the IR community [20].

Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion

AbstractThe aim of this research is twofold. On the one hand, high accuracy retrieval has been a concern of the information retrieval community for some time. We aim to investigate this issue via data fusion. On the other hand, the correlation among component results has been proven harmful to data fusion, but it has not been taken into account in data fusion algorithms. In the hope of achieving better performance, we propose a group of algorithms to eliminate the effect of uneven correlation among component results by assigning different weights to all component results or their combinations. Then the linear combination method or a variation is used for fusion. Extensive experimentation is carried out to evaluate the performances of these algorithms with six groups of component results, which are the top 10 systems submitted to Text REtrieval Conference (TREC) 6, 7, 8, 9, 2001, and 2002. The experimental results show that all eight data fusion methods involved outperform the best component system on average. Therefore, we demonstrate that the data fusion technique in general is effective with accurate retrieval results. The experimental results also demonstrate that all six methods presented in this article are effective for eliminating the effect of uneven correlation among component results. All of them outperform CombSum and five of them outperform CombMNZ on average.

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

Special issue on data management, analysis, and mining for the life sciences

During the last decade, biologists have experienced a fundamental revolution from traditional R&D involving the study of single genes and isolated cellular mechanisms to e-biology that addresses whole cell physiology and complex biological systems. This has been accompanied by an explosion in the number and size of public data resources, and a rapid growth in the variety and volume of laboratory data from microarrays measuring transcription levels simultaneously for hundreds of genes and to protein interaction screens measuring multiple components of protein complexes. Life science data is complex and data sets have complex inter-relationships. The data is often incomplete, uncertain, and can vary significantly across biological replicates. Data can evolve more quickly than the technologies developed to interpret the data. Thus, life sciences data is not well suited for current data models and query paradigms. The challenge for database researchers is developing appropriate technology to manage life sciences data, to make it accessible in an efficient way to scientists, and to provide the appropriate data models and data analysis tools. The last few years has seen emerging activity towards addressing this challenge in the database, data mining, and information retrieval communities. This special issue was organized to feature papers that reflect synergies between computational advances in data management and manipulation and the fields of molecular biology, cell physiology and systems biology. The call for Papers resulted in over 20 submissions in October 2004. Each paper was reviewed by two or three experts. As a result of the first round of review, we accepted

Music to knowledge: A visual programming environment for the development and evaluation of music information retrieval techniques

The objective of the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) project is the creation of a large, secure corpus of audio and symbolic music data accessible to the music information retrieval (MIR) community for the testing and evaluation of various MIR techniques. As part of the IMIRSEL project, a cross-platform JAVA based visual programming environment called Music to Knowledge (M2K) is being developed for a variety of music information retrieval related tasks. The primary objective of M2K is to supply the MIR community with a toolset that provides the ability to rapidly prototype algorithms, as well as foster the sharing of techniques within the MIR community through the use of a standardized set of tools. Due to the relatively large size of audio data and the computational costs associated with some digital signal processing and machine learning techniques, M2K is also designed to support distributed computing across computing clusters. In addition, facilities to allow the integration of non-JAVA based (e.g., C/C++, MATLAB, etc.) algorithms and programs are provided within M2K. [Work supported by the Andrew W. Mellon Foundation and NSF Grants No. IIS-0340597 and No. IIS-0327371.]

Integrated Semantic–Syntactic Video Modeling for Search and Browsing

Video processing and computer vision communities usually employ shot-based or object-based structural video models and associate low-level (color, texture, shape, and motion) and semantic descriptions (textual annotations) with these structural (syntactic) elements. Database and information retrieval communities, on the other hand, employ entity-relation or object-oriented models to model the semantics of multimedia documents. This paper proposes a new generic integrated semantic-syntactic video model to include all of these elements within a single framework to enable structured video search and browsing combining textual and low-level descriptors. The proposed model includes semantic entities (video objects and events) and the relations between them. We introduce a new "actor" entity to enable grouping of object roles in specific events. This context-dependent classification of attributes of an object allows for more efficient browsing and retrieval. The model also allows for decomposition of events into elementary motion units and elementary reaction/interaction units in order to access mid-level semantics and low-level video features. The instantiations of the model are expressed as graphs. Users can formulate flexible queries that can be translated into such graphs. Alternatively, users can input query graphs by editing an abstract model (model template). Search and retrieval is accomplished by matching the query graph with those instantiated models in the database. Examples and experimental results are provided to demonstrate the effectiveness of the proposed integrated modeling and querying framework.