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

iAggregator: Multidimensional Relevance Aggregation Based on a Fuzzy Operator

International audienceRecently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog,1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms

Experiences in evaluating multilingual and text-image information retrieval

23 pages, 8 figures.One important step during the development of information retrieval (IR) processes is the evaluation of the output regarding the information needs of the user. The "high quality" of the output is related to the integration of different methods to be applied in the IR process and the information included in the retrieved documents, but how can "quality" be measured? Although some of these methods can be tested in a stand-alone way, it is not always clear what will happen when several methods are integrated. For this reason, much effort has been put into establishing a good combination of several methods or to correctly tuning some of the algorithms involved. The current approach is to measure the precision and recall figures yielded when different combinations of methods are included in an IR process. In this article, a short description of the current techniques and methods included in an IR system is given, paying special attention to the multilingual aspect of the problem. Also a discussion of their influence on the final performance of the IR process is presented by explaining previous experiences in the evaluation process followed in two projects (MIRACLE and OmniPaper) related to multilingual information retrieval.This work has been partially supported by the projects OmniPaper (European Union, 5th Framework Programme for Research and Technological Development, IST-2001-32174), NEDINE (E-Content project Ref.: 22225), and GPS Project—Software Process Management Platform: modeling, reuse, and measurement (National Research Plan, TIN2004-07083).Publicad

Mixed-Language Arabic- English Information Retrieval

Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries

Techniques for improving efficiency and scalability for the integration of information retrieval and databases

PhDThis thesis is on the topic of integration of Information Retrieval (IR) and Databases (DB), with particular focuses on improving efficiency and scalability of integrated IR and DB technology (IR+DB). The main purpose of this study is to develop efficient and scalable techniques for supporting integrated IR and DB technology, which is a popular approach today for handling complex queries over text and structured data. Our specific interest in this thesis is how to efficiently handle queries over large-scale text and structured data. The work is based on a technology that integrates probability theory and relational algebra, where retrievals for text and data are to be expressed in probabilistic logical programs such as probabilistic relational algebra or probabilistic Datalog. To support efficient processing of probabilistic logical programs, we proposed three optimization techniques that focus on aspects covered logical and physical layers, which include: scoring-driven query optimization using scoring expression, query processing with top-k incorporated pipeline, and indexing with relational inverted index. Specifically, scoring expressions are proposed for expressing the scoring or probabilistic semantics of implied scoring functions of PRA expressions, so that efficient query execution plan can be generated by rule-based scoring-driven optimizer. Secondly, to balance efficiency and effectiveness so that to improve query response time, we studied methods for incorporating topk algorithms into pipelined query execution engine for IR+DB systems. Thirdly, the proposed relational inverted index integrates IR-style inverted index and DB-style tuple-based index, which can be used to support efficient probability estimation and aggregation as well as conventional relational operations. Experiments were carried out to investigate the performances of proposed techniques. Experimental results showed that the efficiency and scalability of an IR+DB prototype have been improved, while the system can handle queries efficiently on considerable large data sets for a number of IR tasks

Probabilistic retrieval models - relationships, context-specific application, selection and implementation

PhDRetrieval models are the core components of information retrieval systems, which guide the document and query representations, as well as the document ranking schemes. TF-IDF, binary independence retrieval (BIR) model and language modelling (LM) are three of the most influential contemporary models due to their stability and performance. The BIR model and LM have probabilistic theory as their basis, whereas TF-IDF is viewed as a heuristic model, whose theoretical justification always fascinates researchers. This thesis firstly investigates the parallel derivation of BIR model, LM and Poisson model, wrt event spaces, relevance assumptions and ranking rationales. It establishes a bridge between the BIR model and LM, and derives TF-IDF from the probabilistic framework. Then, the thesis presents the probabilistic logical modelling of the retrieval models. Various ways of how to estimate and aggregate probability, and alternative implementation to nonprobabilistic operator are demonstrated. Typical models have been implemented. The next contribution concerns the usage of of context-specific frequencies, i.e., the frequencies counted based on assorted element types or within different text scopes. The hypothesis is that they can help to rank the elements in structured document retrieval. The thesis applies context-specific frequencies on term weighting schemes in these models, and the outcome is a generalised retrieval model with regard to both element and document ranking. The retrieval models behave differently on the same query set: for some queries, one model performs better, for other queries, another model is superior. Therefore, one idea to improve the overall performance of a retrieval system is to choose for each query the model that is likely to perform the best. This thesis proposes and empirically explores the model selection method according to the correlation of query feature and query performance, which contributes to the methodology of dynamically choosing a model. In summary, this thesis contributes a study of probabilistic models and their relationships, the probabilistic logical modelling of retrieval models, the usage and effect of context-specific frequencies in models, and the selection of retrieval models

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Merging Multiple Search Results Approach for Meta-Search Engines

Meta Search Engines are finding tools developed for enhancing the search performance by submitting user queries to multiple searchengines and combining the search results in a unified ranked list. They utilized data fusion technique, which requires three major steps: databases selection, the results combination, and the results merging. This study tries to build a framework that can be used for merging the search results retrieved from any set of search engines. This framework based on answering three major questions:1.How meta-search developers could define the optimal rank order for the selected engines.2. How meta-search developers could choose the best search engines combination.3.What is the optimal heuristic merging function that could be used for aggregating the rank order of the retrieved documents form incomparable search engines.The main data collection process depends onrunning 40 general queries on three major search engines (Google, AltaVista, and Alltheweb). Real users have involved in the relevance judgment process for a five point relevancy scale. Theperformance of the three search engines, their different combinations and different merging algorithm have been compared to rank the database, choose the best combination and define the optimal merging function.The major findings of this study are (1) Ranking the databases in merging process should depends on their overall performance not their popularity or size; (2)Larger databases tend to perform better than smaller databases; (3)The combination of the search engines should depend on ranking the database and choosing theappropriate combination function; (4)Search Engines tend to retrieve more overlap relevant document than overlap irrelevant documents; and (5) The merging function which take theoverlapped documents into accounts tend to perform better than the interleave and the rank similarity function.In addition to these findings the study has developed a set of requirements for the merging process to be successful. This procedure include the databases selection, the combination, and merging upon heuristic solutions