Standard Information Retrieval Research Articles

Patent retrieval – A question of access

The development of models and systems in Information Retrieval (IR) has been driven by the empirical measurement of effectiveness. However, in recall-oriented domains such as patent search where there is a significant cost of missing a relevant document, standard IR effectiveness measurement only reveals part of the truth. Since credible estimates of recall are not available, it is difficult to evaluate or design systems for this domain. Here, we propose a measure of corpus access, retrievability, and show using four large patent corpora that it can be used both to evaluate models for patent retrieval and also the corpora themselves for the ease with which a document can be retrieved.

Bipolar queries in textual information retrieval: A new perspective

A new concept of a bipolar query against collections of textual documents, i.e. in the context of information retrieval (IR), is introduced using recent developments in bipolar information modeling and bipolar database queries. Specifically, a particular approach to bipolar queries with an explicit “and possibly” type of an aggregation operator is used. An effective and efficient processing of such bipolar queries using standard IR data structures is briefly discussed. The bipolar queries proposed combine a flexibility provided by fuzzy logic with a more sophisticated representation of user preferences and intentions. This combination can make the search of vast resources of textual document, notably those available via the Internet, more intelligent.

Knowledge representation and inference techniques to improve the management of gas and oil facilities

This paper describes an experimental work carried out in the framework of an important European project to create and make use of a wide-ranging knowledge base in the gas/oil domain. In the context of this work, “knowledge base” means a collection of formal statement relating, with a negligible loss of information, the inner content (the ‘meaning’) of “complex events” included in two different “storyboards”. These events – originally presented under the form of unstructured natural language information – concern some general activities proper to the management of gas/oil facilities, like recognizing and monitoring gas leakage alarms in a gas processing plant or triggering the different steps needed to activate a gas turbine. To express this sort of information and to set up the knowledge base, the NKRL (Narrative Knowledge Representation Language) formalism has been used. NKRL is a conceptual meta-model and Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich ‘narrative’ data sources. The final knowledge base has been firstly tested in depth using the standard NKRL querying and information retrieval tools. High-level inference procedures have then been used, both “transformation rules” – unsuccessful queries are ‘transformed’ to produce results that are ‘semantically similar’ to those searched for initially – and “hypothesis rules” – information in the knowledge base is automatically aggregated to supply a sort of ‘causal’ explanation of some retrieved events.

Database Access Pattern Protection Without Full-Shuffles

Privacy protection is one of the fundamental security requirements for database outsourcing. A major threat is information leakage from database access patterns generated by query executions. The standard private information retrieval (PIR) schemes, which are widely regarded as theoretical solutions, entail <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) computational overhead per query for a database with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> items. Recent works propose to protect access patterns by introducing a trusted component with constant storage size. The resulting privacy assurance is as strong as PIR, though with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1) online computation cost, they still have <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) amortized cost per query due to periodically full database shuffles. In this paper, we design a novel scheme in the same model with provable security, which only shuffles a portion of the database. The amortized server computational complexity is reduced to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (√{ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> log <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> / <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> }). With a secure storage storing thousands of items, our scheme can protect the access pattern privacy of databases of billions of entries, at a lower cost than those using ORAM-based poly-logarithm algorithms.

Incorporating rich features to boost information retrieval performance: A SVM-regression based re-ranking approach

Document ranking is an essential problem in the field of information retrieval (IR). Traditional weighting models such as BM25 and Language model can only take advantage of query terms. IR is a complex process that may be affected by a series of heterogeneous features. It is necessary to refine first-pass retrieval results by taking rich features into account. Traditional heuristic re-ranking approaches can only take advantage of a small number of homogeneous features that may affect information retrieval performance. In this paper, we propose and evaluate a regression-based document re-ranking approach for IR, in which we use SVM regression model to learn a re-ranking function automatically. Under this regression-based framework, we can take advantage of rich features to re-rank the firs-pass retrieved documents by traditional weighting models. We conduct a series of experiments on four standard IR collections in two different languages. The experimental results show that our proposed approach can significantly improve the retrieval performance over the first-pass retrieval. Moreover, by refining the first-pass retrieved document set, the traditional pseudo relevant feedback approaches can also be enhanced.

A unified representation of web logs for mining applications

The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information that, while hard to extract using standard IR techniques, can be useful i...

Document Clustering based on Topic Maps

Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1) to represent the document in such a form that inherently captures semantics of the text. This may also help to reduce dimensionality of the document, and (2) to define a similarity measure based on the semantic representation such that it assigns higher numerical values to document pairs which have higher semantic relationship. Feature space of the documents can be very challenging for document clustering. A document may contain multiple topics, it may contain a large set of class-independent general-words, and a handful class-specific core-words. With these features in mind, traditional agglomerative clustering algorithms, which are based on either Document Vector model (DVM) or Suffix Tree model (STC), are less efficient in producing results with high cluster quality. This paper introduces a new approach for document clustering based on the Topic Map representation of the documents. The document is being transformed into a compact form. A similarity measure is proposed based upon the inferred information through topic maps data and structures. The suggested method is implemented using agglomerative hierarchal clustering and tested on standard Information retrieval (IR) datasets. The comparative experiment reveals that the proposed approach is effective in improving the cluster quality.

Evaluating an integrated system supporting multiple information‐seeking strategies

AbstractMany studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information‐seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information‐seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.

Focused web crawling in the acquisition of comparable corpora

Cross-Language Information Retrieval (CLIR) resources, such as dictionaries and parallel corpora, are scarce for special domains. Obtaining comparable corpora automatically for such domains could be an answer to this problem. The Web, with its vast volumes of data, offers a natural source for this. We experimented with focused crawling as a means to acquire comparable corpora in the genomics domain. The acquired corpora were used to statistically translate domain-specific words. The same words were also translated using a high-quality, but non-genomics-related parallel corpus, which fared considerably worse. We also evaluated our system with standard information retrieval (IR) experiments, combining statistical translation using the Web corpora with dictionary-based translation. The results showed improvement over pure dictionary-based translation. Therefore, mining the Web for comparable corpora seems promising.

A comparison of geometric approaches to assessing spatial similarity for GIR

This research compares the geographic information retrieval (GIR) performance of a set of logistic regression models with those of five non‐probabilistic methods that compute a spatial similarity score for a query–document pair. All methods are applied to a test collection of queries and documents indexed spatially by two convex conservative geometric approximations: the minimum bounding box (MBB) and the convex hull. In the comparison, the tested logistic regression models outperform, in terms of standard information retrieval recall and precision measures, all of the non‐probabilistic methods. The retrieval performance achieved by the logistic regression models on MBB approximations is similar to that achieved by the use of the non‐probabilistic methods on convex hulls. Although these results are valid only for the test collection used in this study, they suggest that a logistic regression approach to GIR provides an alternative to the use of higher‐quality geometric representations that are more difficult to obtain, implement, and process. Additionally, this research demonstrates the ability of a probabilistic approach to effectively incorporate information about geographic context in the spatial ranking process.

Developing Engineering Ontology for Information Retrieval

When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Since search remains word based, engineers do not have the effective means to harness and reuse past designs and experiences. Current information retrieval approaches based on statistical methods and keyword matching do not satisfy users’ needs in the engineering domain. Therefore, we propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

Web retrieval systems and the Greek language: do they have an understanding?

Searching the web is a common activity of web users. English and non-English speakers utilize international or local search engines so as to satisfy their information needs. Most of the attempts at evaluation of search engines focus on English queries and on English document collections. In this paper an evaluation methodology is presented and the capabilities of international and local web retrieval systems using Greek queries are evaluated based on this method. We aim at identifying difficulties and knowledge requirements when using a Greek supporting search engine. The importance of interface localization and the effects of standard information retrieval techniques such as case insensitivity, stopword removal and simple stemming are studied in international and local search engines. The evaluation methodology is applicable to other non-English natural languages as well.

Interactive question answering: Is Cross-Language harder than monolingual searching?

Is Cross-Language answer finding harder than Monolingual answer finding for users? In this paper we provide initial quantitative and qualitative evidence to answer this question. In our study, which involves 16 users searching questions under four different system conditions, we find that interactive cross-language answer finding is not substantially harder (in terms of accuracy) than its monolingual counterpart, using general purpose Machine Translation systems and standard Information Retrieval machinery, although it takes more time. We have also seen that users need more context to provide accurate answers (full documents) than what is usually considered by systems (paragraphs or passages). Finally, we also discuss the limitations of standard evaluation methodologies for interactive Information Retrieval experiments in the case of cross-language question answering.

Precision recall with user modeling (PRUM)

Standard Information Retrieval (IR) metrics are not well suited for new paradigms like XML or Web IR in which retrievable information units are document elements and/or sets of related documents. Part of the problem stems from the classical hypotheses on the user models: They do not take into account the structural or logical context of document elements or the possibility of navigation between units. This article proposes an explicit and formal user model that encompasses a large variety of user behaviors. Based on this model, we extend the probabilistic precision-recall metric to deal with the new IR paradigms.

Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony

The segmentation of music into intro-chorus-verse-outro, and similar segments, is a difficult topic. A method for performing automatic segmentation based on features related to rhythm, timbre, and harmony is presented, and compared, between the features and between the features andmanual segmentation of a database of 48 songs. Standard information retrieval performance measures are used in the comparison, and it is shown that the timbre-related feature performs best.

Evolving local and global weighting schemes in information retrieval

This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector space model. Based on a set of queries and their human determined relevant documents, weighting schemes are evolved which achieve a high average precision. In Information Retrieval (IR) systems, useful information for term weighting schemes is available from the query, individual documents and the collection as a whole. We evolve term weighting schemes in both local (within-document) and global (collection-wide) domains which interact with each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test collections and are compared to the traditional tf-idf weighting scheme and to the BM25 weighting scheme using standard IR performance metrics. Furthermore, we show that the global weighting schemes evolved on small collections also increase average precision on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as both high and low frequency terms are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections.

On the development of name search techniques for Arabic

AbstractThe need for effective identity matching systems has led to extensive research in the area of name search. For the most part, such work has been limited to English and other Latin‐based languages. Consequently, algorithms such as Soundex and n‐gram matching are of limited utility for languages such as Arabic, which has vastly different morphologic features that rely heavily on phonetic information. The dearth of work in this field is partly caused by the lack of standardized test data. Consequently, we have built a collection of 7,939 Arabic names, along with 50 training queries and 111 test queries. We use this collection to evaluate a variety of algorithms, including a derivative of Soundex tailored to Arabic (ASOUNDEX), measuring effectiveness by using standard information retrieval measures. Our results show an improvement of 70% over existing approaches.

Eigenvalue‐based model selection during latent semantic indexing

AbstractIn this study amended parallel analysis (APA), a novel method for model selection in unsupervised learning problems such as information retrieval (IR), is described. At issue is the selection of k, the number of dimensions retained under latent semantic indexing (LSI). Amended parallel analysis is an elaboration of Horn's parallel analysis, which advocates retaining eigenvalues larger than those that we would expect under term independence. Amended parallel analysis operates by deriving confidence intervals on these “null” eigenvalues. The technique amounts to a series of nonparametric hypothesis tests on the correlation matrix eigenvalues. In the study, APA is tested along with four established dimensionality estimators on six standard IR test collections. These estimates are evaluated with regard to two IR performance metrics. Additionally, results from simulated data are reported. In both rounds of experimentation APA performs well, predicting the best values of k on 3 of 12 observations, with good predictions on several others, and never offering the worst estimate of optimal dimensionality.

UCLIR: a Multilingual Information Retrieval Tool

In this paper the Unicode Cross-Language Information Retrieval system (UCLIR) is described. UCLIR accepts a query in one language and then retrieves relevant documents in any of several languages. The core process involves a suite of technologies including machine translation and standard monolingual information retrieval.

Open Archives Data Service Prototype and Automated Subject Indexing Using D-Lib® Archive Content As a Testbed

The Indiana University School of Library and Information Science opened a new research laboratory in January 2003; The Indiana University School of Library and Information Science Information Processing Laboratory [IU IP Lab]. The purpose of the new laboratory is to facilitate collaboration between scientists in the department in the areas of information retrieval (IR) and information visualization (IV) research. The lab has several areas of focus. These include grid and cluster computing, and a standard Java-based software platform to support plug and play research datasets, a selection of standard IR modules and standard IV algorithms. Future development includes software to enable researchers to contribute datasets, IR algorithms, and visualization algorithms into the standard environment. We decided early on to use OAI-PMH as a resource discovery tool because it is consistent with our mission.