TIJAH: Embracing IR Methods in XML Databases

This paper discusses our participation in INEX (the Initiative for the Evaluation of XML Retrieval) using the TIJAH XML-IR system. TIJAHs system design follows a standard layered database architecture, carefully separating the conceptual, logical and physical levels. At the conceptual level, we classify the INEX XPath-based query expressions into three different query patterns. For each pattern, we present its mapping into a query execution strategy. The logical layer exploits score region algebra (SRA) as the basis for query processing. We discuss the region operators used to select and manipulate XML document components. The logical algebra expressions are mapped into efficient relational algebra expressions over a physical representation of the XML document collection using the pre-post numbering scheme. The paper concludes with an analysis of experiments performed with the INEX test collection.     

Incremental Relevance Feedback in Japanese Text Retrieval

The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using number-to-view graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones.     

From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications

Information Retrieval systems typically sort the result with respect to document retrieval status values (RSV). According to the Probability Ranking Principle, this ranking ensures optimum retrieval quality if the RSVs are monotonously increasing with the probabilities of relevance (as e.g. for probabilistic IR models). However, advanced applications like filtering or distributed retrieval require estimates of the actual probability of relevance. The relationship between the RSV of a document and its probability of relevance can be described by a normalisation function which maps the retrieval status value onto the probability of relevance (mapping functions). In this paper, we explore the use of linear and logistic mapping functions for different retrieval methods. In a series of upper-bound experiments, we compare the approximation quality of the different mapping functions. We also investigate the effect on the resulting retrieval quality in distributed retrieval (only merging, without resource selection). These experiments show that good estimates of the actual probability of relevance can be achieved, and that the logistic model outperforms the linear one. Retrieval quality for distributed retrieval is only slightly improved by using the logistic function.     

Using Statistical Term Similarity for Sense Disambiguation in Cross-Language Information Retrieval

With the increasing availability of machine-readable bilingual dictionaries, dictionary-based automatic query translation has become a viable approach to Cross-Language Information Retrieval (CLIR). In this approach, resolving term ambiguity is a crucial step. We propose a sense disambiguation technique based on a term-similarity measure for selecting the right translation sense of a query term. In addition, we apply a query expansion technique which is also based on the term similarity measure to improve the effectiveness of the translation queries. The results of our Indonesian to English and English to Indonesian CLIR experiments demonstrate the effectiveness of the sense disambiguation technique. As for the query expansion technique, it is shown to be effective as long as the term ambiguity in the queries has been resolved. In the effort to solve the term ambiguity problem, we discovered that differences in the pattern of word-formation between the two languages render query translations from one language to the other difficult.     

Construction of query concepts based on feature clustering of documents

In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.     

An Ontology-Based Binary-Categorization Approach for Recognizing Multiple-Record Web Documents Using a Probabilistic Retrieval Model

The Web contains a tremendous amount of information. It is challenging to determine which Web documents are relevant to a user query, and even more challenging to rank them according to their degrees of relevance. In this paper, we propose a probabilistic retrieval model using logistic regression for recognizing multiple-record Web documents against an application ontology, a simple conceptual modeling approach. We notice that many Web documents contain a sequence of chunks of textual information, each of which constitutes a record. This type of documents is referred to as multiple-record documents. In our categorization approach, a document is represented by a set of term frequencies of index terms, a density heuristic value, and a grouping heuristic value. We first apply the logistic regression analysis on relevant probabilities using the (i) index terms, (ii) density value, and (iii) grouping value of each training document. Hereafter, the relevant probability of each test document is interpolated from the fitting curves. Contrary to other probabilistic retrieval models, our model makes only a weak independent assumption and is capable of handling any important dependent relationships among index terms. In addition, we use logistic regression, instead of linear regression analysis, because the relevance probabilities of training documents are discrete. Using a test set of car-ads and another one for obituary Web documents, our probabilistic model achieves the averaged recall ratio of 100%, precision ratio of 83.3%, and accuracy ratio of 92.5%.     

Query structuring and expansion with two-stage term dependence for Japanese web retrieval

In this paper, we propose a new term dependence model for information retrieval, which is based on a theoretical framework using Markov random fields. We assume two types of dependencies of terms given in a query: (i) long-range dependencies that may appear for instance within a passage or a sentence in a target document, and (ii) short-range dependencies that may appear for instance within a compound word in a target document. Based on this assumption, our two-stage term dependence model captures both long-range and short-range term dependencies differently, when more than one compound word appear in a query. We also investigate how query structuring with term dependence can improve the performance of query expansion using a relevance model. The relevance model is constructed using the retrieval results of the structured query with term dependence to expand the query. We show that our term dependence model works well, particularly when using query structuring with compound words, through experiments using a 100-gigabyte test collection of web documents mostly written in Japanese. We also show that the performance of the relevance model can be significantly improved by using the structured query with our term dependence model.

Towards Universal Text Retrieval: Tipster Text Retrieval Research at New Mexico State University

New Mexico State University's Computing Research Lab has participated in research in all three phases of the US Government's Tipster program. Our work on information retrieval has focused on research and development of multilingual and cross-language approaches to automatic retrieval. The work on automatic systems has been supplemented by additional research into the role of the IR system user in interactive retrieval scenarios: monolingual, multilingual and cross-language. The combined efforts suggest that universal text retrieval, in which a user can find, access and use documents in the face of language differences and information overload, may be possible.     

Query expansion methods for collaborative information retrieval

Information Retrieval Systeme haben in den letzten Jahren nur geringe Verbesserungen in der Retrieval Performance erzielt. Wir arbeiten an neuen Ans?tzen, dem sogenannten Collaborativen Information Retrieval (CIR), die das Potential haben, starke Verbesserungen zu erreichen. CIR ist die Methode, mit der durch Ausnutzen von Informationen aus früheren Anfragen die Retrieval Peformance für die aktuelle Anfrage verbessert wird. Wir haben ein eingeschr?nktes Szenario, in dem nur alte Anfragen und dazu relevante Antwortdokumente zur Verfügung stehen. Neue Ans?tze für Methoden der Query Expansion führen unter diesen Bedingungen zu Verbesserungen der Retrieval Performance . The accuracy of ad-hoc document retrieval systems has reached a stable plateau in the last few years. We are working on so-called collaborative information retrieval (CIR) systems which have the potential to overcome the current limits. We define CIR as a task, where an information retrieval (IR) system uses information gathered from previous search processes from one or several users to improve retrieval performance for the current user searching for information. We focus on a restricted setting in CIR in which only old queries and correct answer documents to these queries are available for improving a new query. For this restricted setting we propose new approaches for query expansion procedures. We show how CIR methods can improve overall IR performance.

CR Subject Classification H.3.3     

Exploring criteria for successful query expansion in the genomic domain

Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.

A Theoretical Study of a Generalized Version of Kleinberg’s HITS Algorithm

Kleinbergs HITS algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results.Supported in part by a grant from the Italian National Research Council (CNR) research project Technologies and Services for Enhanced Content Delivery.     

An effective and efficient results merging strategy for multilingual information retrieval in federated search environments

Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target languages in response to a user query in a single source language. In a multilingual federated search environment, different information sources contain documents in different languages. A general search strategy in multilingual federated search environments is to translate the user query to each language of the information sources and run a monolingual search in each information source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information sources that are in different languages. This is known as the results merging problem for multilingual information retrieval. Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the other side, a more effective merging method was proposed to download and translate all retrieved documents into the source language and generate the final ranked list by running a monolingual search in the search client. The latter method is more effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing both the query-based translation method and the document-based translation method. Then, query-specific and source-specific transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) () data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results merging algorithm with different transformation models. This paper also provides thorough experimental results as well as detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results of the cross-language evaluation forum-CLEF 2005, 2005).

A Comparison of Text-Based Methods for Detecting Duplication in Scanned Document Databases

This paper presents an experimental evaluation of several text-based methods for detecting duplication in scanned document databases using uncorrected OCR output. This task is made challenging both by the wide range of degradations printed documents can suffer, and by conflicting interpretations of what it means to be a duplicate. We report results for four sets of experiments exploring various aspects of the problem space. While the techniques studied are generally robust in the face of most types of OCR errors, there are nonetheless important differences which we identify and discuss in detail.     

Locating and Recognizing Text in WWW Images

The explosive growth of the World Wide Web has resulted in a distributed database consisting of hundreds of millions of documents. While existing search engines index a page based on the text that is readily extracted from its HTML encoding, an increasing amount of the information on the Web is embedded in images. This situation presents a new and exciting challenge for the fields of document analysis and information retrieval, as WWW image text is typically rendered in color and at very low spatial resolutions. In this paper, we survey the results of several years of our work in the area. For the problem of locating text in Web images, we describe a procedure based on clustering in color space followed by a connected-components analysis that seems promising. For character recognition, we discuss techniques using polynomial surface fitting and fuzzy n-tuple classifiers. Also presented are the results of several experiments that demonstrate where our methods perform well and where more work needs to be done. We conclude with a discussion of topics for further research.     

一种基于关键词的数据库检索机制的研究

认为利用关键词对结构化数据进行查询,实现信息检索和数据库查询的融合的技术已成为热点研究问题。基于模式图的检索算法是目前数据库关键词检索研究的技术之一。现有的模式图算法仍然存在着检索效率低下、查询准确率不高等问题。在对现有算法进行改进的基础上,设计并实现一个基于改进算法的系统,实验表明,使用改进算法的系统具有更高的检索性能和检索效率。     

Generalized Hamming Distance

Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a Generalized Hamming distance that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently. We envision many uses for such a measure. In this paper we define and prove some basic properties of the Generalized Hamming distance, and illustrate its use in the area of object recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.     

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.

An empirical study of gene synonym query expansion in biomedical information retrieval

Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly. So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical for improving performance.

A probability ranking principle for interactive information retrieval

The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR). In this article, a new theoretical framework for interactive retrieval is proposed: The basic idea is that during IIR, a user moves between situations. In each situation, the system presents to the user a list of choices, about which s/he has to decide, and the first positive decision moves the user to a new situation. Each choice is associated with a number of cost and probability parameters. Based on these parameters, an optimum ordering of the choices can the derived—the PRP for IIR. The relationship of this rule to the classical PRP is described, and issues of further research are pointed out.

《网络教育信息资源检索与利用》的教学设计与实践

结合深圳市基础教育和中学教师继续教育的实际情况，设计了《网络教育信息资源检索与利用》的教学内容．并进行了教学实践。指出了对师范院校文献检索课程教学的启示。