Georgina Ramirez

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. TIJAH’s 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.

TIJAH at INEX 2004 modeling phrases and relevance feedback

This paper discusses our participation in INEX using the TIJAH XML-IR system. We have enriched the TIJAH system, which follows a standard layered database architecture, with several new features. An extensible conceptual level processing unit has been added to the system. The algebra on the logical level and the implementation on the physical level have been extended to support phrase search and structural relevance feedback. The conceptual processing unit is capable of rewriting NEXI content-only and content-and-structure queries into the internal form, based on the retrieval model parameter specification, that is either predefined or based on relevance feedback. Relevance feedback parameters are produced based on the data fusion of result element score values and sizes, and relevance assessments. The introduction of new operators supporting phrase search in score region algebra on the logical level is discussed in the paper, as well as their implementation on the physical level using the pre-post numbering scheme. The framework for structural relevance feedback is also explained in the paper. We conclude with a preliminary analysis of the system performance based on INEX 2004 runs.

TIJAH scratches INEX 2005: vague element selection, image search, overlap, and relevance feedback

Retrieving information from heterogeneous data sources in a flexible manner and within a single (database) framework is still a challenge. In this paper we present several extensions of our prototype database system TIJAH developed for structured retrieval. The extensions are aimed at modeling vague selection of XML elements and image retrieval. All three levels (conceptual, logical, and physical) of the TIJAH system are enhanced to support the extensions. Additionally, we analyze different ways of removing overlap and explain how structural information can be used for relevance feedback.

Relevant contextual features in XML retrieval

We present and analyze results of a collaborative user study carried out by the Interactive Track at INEX 2005 [4]. The overall goal of the Interactive Track is to investigate the behavior of users when interacting with components of XML documents. In this paper we use the data collected from this user study to investigate dependencies between several contextual features and the structural characteristics of the relevant components (e.g. type and number of relevant elements). The three contextual features analyzed are the users familiarity with the topic, the requests type, and the users motivation to perform the search. The dependencies found indicate that XML retrieval systems should also benefit from the use of contextual information.

Structural features in content oriented XML retrieval

The structural features of XML components are an extra source of information that should be used in a content-oriented retrieval task on this type of documents. In this paper we explore one of the structural features from the INEX collection [1] that could be used in content-oriented search. We analyse the gain this knowledge could add to the performance of an information retrieval system and present a first approach on how this structural information could be extracted from a relevance feedback process to be used as priors in a language modelling framework.