Evaluation of effective XML information retrieval

XML is being adopted as a common storage format in scientific data repositories, digital libraries, and on the World Wide Web. Accordingly, there is a need for content-oriented XML retrieval systems that can efficiently and effectively store, search and retrieve information from XML document collections. Unlike traditional information retrieval systems where whole documents are usually indexed and retrieved as information units, XML retrieval systems typically index and retrieve document components of varying granularity. To evaluate the effectiveness of such systems, test collections where relevance assessments are provided according to an XML-specific definition of relevance are necessary. Such test collections have been built during four rounds of the INitiative for the Evaluation of XML Retrieval (INEX). There are many different approaches to XML retrieval; most approaches either extend full-text information retrieval systems to handle XML retrieval, or use database technologies that incorporate existing XML standards to handle both XML presentation and retrieval. We present a hybrid approach to XML retrieval that combines text information retrieval features with XML-specific features found in a native XML database. Results from our experiments on the INEX 2003 and 2004 test collections demonstrate the usefulness of applying our hybrid approach to different XML retrieval tasks. A realistic definition of relevance is necessary for meaningful comparison of alternative XML retrieval approaches. The three relevance definitions used by INEX since 2002 comprise two relevance dimensions, each based on topical relevance. We perform an extensive analysis of the two INEX 2004 and 2005 relevance definitions, and show that assessors and users find them difficult to understand. We propose a new definition of relevance for XML retrieval, and demonstrate that a relevance scale based on this definition is useful for XML retrieval experiments. Finding the appropriate approach to evaluate XML retrieval effectiveness is the subject of ongoing debate within the XML information retrieval research community. We present an overview of the evaluation methodologies implemented in the current INEX metrics, which reveals that the metrics follow different assumptions and measure different XML retrieval behaviours. We propose a new evaluation metric for XML retrieval and conduct an extensive analysis of the retrieval performance of simulated runs to show what is measured. We compare the evaluation behaviour obtained with the new metric to the behaviours obtained with two of the official INEX 2005 metrics, and demonstrate that the new metric can be used to reliably evaluate XML retrieval effectiveness. To analyse the effectiveness of XML retrieval in different application scenarios, we use evaluation measures in our new metric to investigate the behaviour of XML retrieval approaches under the following two scenarios: the ad-hoc retrieval scenario, exploring the activities carried out as part of the INEX 2005 Ad-hoc track; and the multimedia retrieval scenario, exploring the activities carried out as part of the INEX 2005 Multimedia track. For both application scenarios we show that, although different values for retrieval parameters are needed to achieve the optimal performance, the desired textual or multimedia information can be effectively located using a combination of XML retrieval approaches

Using Proximity and Tag Weights for Focused Retrieval in Structured Documents

International audienceFocused information retrieval is concerned with the retrieval of small units of information. In this context, the structure of the documents as well as the proximity among query terms have been found useful for improving retrieval effectiveness. In this article, we propose an approach combining the proximity of the terms and the tags which mark these terms. Our approach is based on a Fetch and Browse method where the fetch step is performed with BM25 and the browse step with a structure enhanced proximity model. In this way, the ranking of a document depends not only upon the existence of the query terms within the document but also upon the tags which mark these terms. Thus, the document tends to be highly relevant when query terms are close together and are emphasized by tags. The evaluation of this model on a large XML structured collection provided by the INEX 2010 XML IR evaluation campaign shows that the use of term proximity and structure improves the retrieval effectiveness of BM25 in the context of focused information retrieval

Evaluation Metrics

An evaluation metric is used to evaluate the effectiveness of information retrieval systems and to justify theoretical and/or pragmatical developments of these systems. It consists of a set of measures that follow a common underlying evaluation methodology. There are many metrics that can be used to evaluate the effectiveness of semi-structured text (XML) retrieval systems. These metrics are based on different evaluation assumptions, incorporate different hypotheses of the expected user behaviour, and implement their own evaluation methodologies to handle the level of overlap among the XML information units

Configurable indexing and ranking for XML information retrieval

Indexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in false negatives and false positives, and improper ranking may lead to low precisions. In this paper, we propose a configurable XML information retrieval system, in which users can configure appropriate index types for XML tags and text contents. Based on users ’ index configurations, the system transforms XML structures into a compact tree representation, Ctree, and indexes XML text contents. To support XML ranking, we propose the concepts of “weighted term frequency ” and “inverted element frequency, ” where the weight of a term depends on its frequency and location within an XML element as well as its popularity among similar elements in an XML dataset. We evaluate the effectiveness of our system through extensive experiments on the INEX 03 dataset and 30 content and structure (CAS) topics. The experimental results reveal that our system has significantly high precision at low recall regions and achieves the highest average precision (0.3309) as compared with 38 official INEX 03 submissions using the strict evaluation metric

The Role of Context in Matching and Evaluation of XML Information Retrieval

Sähköisten kokoelmien kasvun, hakujen arkipäiväistymisen ja mobiililaitteiden yleistymisen myötä yksi tiedonhaun menetelmien kehittämisen tavoitteista on saavuttaa alati tarkempia hakutuloksia; pitkistäkin dokumenteista oleellinen sisältö pyritään osoittamaan hakijalle tarkasti. Tiedonhakija pyritään siis vapauttamaan turhasta dokumenttien selaamisesta. Internetissä ja muussa sähköisessä julkaisemisessa dokumenttien osat merkitään usein XML-kielen avulla dokumenttien automaattista käsittelyä varten. XML-merkkaus mahdollistaa dokumenttien sisäisen rakenteen hyödyntämisen. Toisin sanoen tätä merkkausta voidaan hyödyntää kehitettäessä tarkkuusorientoituneita (kohdennettuja) tiedonhakujärjestelmiä ja menetelmiä. Väitöskirja käsittelee tarkkuusorientoitunutta tiedonhakua, jossa eksplisiittistä XML merkkausta voidaan hyödyntää. Väitöskirjassa on kaksi pääteemaa, joista ensimmäisen käsittelee XML -tiedonhakujärjestelmä TRIX:in (Tampere Retrieval and Indexing for XML) kehittämistä, toteuttamista ja arviointia. Toinen teema käsittelee kohdennettujen tiedonhakujärjestelmien empiirisiä arviointimenetelmiä. Ensimmäisen teeman merkittävin kontribuutio on kontekstualisointi, jolloin täsmäytyksessä XML-tiedonhaulle tyypillistä tekstievidenssin vähäisyyttä kompensoidaan hyödyntämällä XML-hierarkian ylempien tai rinnakkaisten osien sisältöä (so. kontekstia). Menetelmän toimivuus osoitetaan empiirisin menetelmin. Tutkimuksen seurauksena kontekstualisointi (contextualization) on vakiintunut alan yleiseen, kansainväliseen sanastoon. Toisessa teemassa todetaan kohdennetun tiedonhaun vaikuttavuuden mittaamiseen käytettävien menetelmien olevan monin tavoin puutteellisia. Puutteiden korjaamiseksi väitöskirjassa kehitetään realistisempia arviointimenetelmiä, jotka ottavat huomioon palautettavien hakuyksiköiden kontekstin, lukemisjärjestyksen ja käyttäjälle selailusta koituvan vaivan. Tutkimuksessa kehitetty mittari (T2I(300)) on valittu varsinaiseksi mittariksi kansainvälisessä INEX (Initiative for the Evaluation of XML Retrieval) hankkeessa, joka on vuonna 2002 perustettu XML tiedonhaun tutkimusfoorumi.This dissertation addresses focused retrieval, especially its sub-concept XML (eXtensible Mark-up Language) information retrieval (XML IR). In XML IR, the retrievable units are either individual elements, or sets of elements grouped together typically by a document. These units are ranked according to their estimated relevance by an XML IR system. In traditional information retrieval, the retrievable unit is an atomic document. Due to this atomicity, many core characteristics of such document retrieval paradigm are not appropriate for XML IR. Of these characteristics, this dissertation explores element indexing, scoring and evaluation methods which form two main themes: 1. Element indexing, scoring, and contextualization 2. Focused retrieval evaluation To investigate the first theme, an XML IR system based on structural indices is constructed. The structural indices offer analyzing power for studying element hierarchies. The main finding in the system development is the utilization of surrounding elements as supplementary evidence in element scoring. This method is called contextualization, for which we distinguish three models: vertical, horizontal and ad hoc contextualizations. The models are tested with the tools provided by (or derived from) the Initiative for the Evaluation of XML retrieval (INEX). The results indicate that the evidence from element surroundings improves the scoring effectiveness of XML retrieval. The second theme entails a task where the retrievable elements are grouped by a document. The aim of this theme is to create methods measuring XML IR effectiveness in a credible fashion in a laboratory environment. The credibility is pursued by assuming the chronological reading order of a user together with a point where the user becomes frustrated after reading a certain amount of non-relevant material. Novel metrics are created based on these assumptions. The relative rankings of systems measured with the metrics differ from those delivered by contemporary metrics. In addition, the focused retrieval strategies benefit from the novel metrics over traditional full document retrieval

Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments ("General" and "Specific") and two categories of topics ("Broad" and "Narrow"). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call "Coherent Retrieval Elements". The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.Comment: Postprint version. The editor version can be accessed through the DO

Enhancing Content-And-Structure Information Retrieval using a Native XML Database

Three approaches to content-and-structure XML retrieval are analysed in this paper: first by using Zettair, a full-text information retrieval system; second by using eXist, a native XML database, and third by using a hybrid XML retrieval system that uses eXist to produce the final answers from likely relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics can be classified in two categories: the first retrieving full articles as final answers, and the second retrieving more specific elements within articles as final answers. We show that for both topic categories our initial hybrid system improves the retrieval effectiveness of a native XML database. For ranking the final answer elements, we propose and evaluate a novel retrieval model that utilises the structural relationships between the answer elements of a native XML database and retrieves Coherent Retrieval Elements. The final results of our experiments show that when the XML retrieval task focusses on highly relevant elements our hybrid XML retrieval system with the Coherent Retrieval Elements module is 1.8 times more effective than Zettair and 3 times more effective than eXist, and yields an effective content-and-structure XML retrieval

DCU and ISI@INEX 2010: Ad-hoc and data-centric tracks

We describe the participation of Dublin City University (DCU)and the Indian Statistical Institute (ISI) in INEX 2010. The main contributions of this paper are: i) a simplified version of Hierarchical Language Model (HLM) which involves scoring XML elements with a combined probability of generating the given query from itself and the top level article node, is shown to outperform the baselines of Language Model (LM) and Vector Space Model (VSM) scoring of XML elements; ii) the Expectation Maximization (EM) feedback in LM is shown to be the most effective on the domain specic collection of IMDB; iii) automated removal of sentences indicating aspects of irrelevance from the narratives of INEX ad-hoc topics is shown to improve retrieval eectiveness

Exploiting Query Structure and Document Structure to Improve Document Retrieval Effectiveness

In this paper we present a systematic analysis of document retrieval using unstructured and structured queries within the score region algebra (SRA) structured retrieval framework. The behavior of di®erent retrieval models, namely Boolean, tf.idf, GPX, language models, and Okapi, is tested using the transparent SRA framework in our three-level structured retrieval system called TIJAH. The retrieval models are implemented along four elementary retrieval aspects: element and term selection, element score computation, score combination, and score propagation. The analysis is performed on a numerous experiments evaluated on TREC and CLEF collections, using manually generated unstructured and structured queries. Unstructured queries range from the short title queries to long title + description + narrative queries. For generating structured queries we exploit the knowledge of the document structure and the content used to semantically describe or classify documents. We show that such structured information can be utilized in retrieval engines to give more precise answers to user queries then when using unstructured queries